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Asymmetry activation and its relation to coherence under permutation operation

Masahito Hayashi School of Data Science, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, 518172, China International Quantum Academy, Futian District, Shenzhen 518048, China Graduate School of Mathematics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8602, Japan [email protected], [email protected]
Abstract

A Dicke state and its decohered state are invariant for permutation. However, when another qubits state to each of them is attached, the whole state is not invariant for permutation, and has a certain asymmetry for permutation. The amount of asymmetry can be measured by the number of distinguishable states under the group action or the mutual information. Generally, the amount of asymmetry of the whole state is larger than the amount of asymmetry of the added state. That is, the asymmetry activation happens in this case. This paper investigates the amount of the asymmetry activation under Dicke states. To address the asymmetry activation asymptotically, we introduce a new type of central limit theorem by using several formulas on hypergeometric functions. We reveal that the amounts of the asymmetry and the asymmetry activation with a Dicke state have a strictly larger order than those with the decohered state in a specific type of the limit.

1 Introduction

Asymmetry is a root of the variety of various physical phenomena. Consider a perfectly symmetric state. Such a state does not reflect any changes so that it can realize no variety. To realize rich variety, the state needs to have sufficient asymmetry. In physics, people consider that symmetry breaking is needed for our world. Therefore, to explain various phenomena, many researchers introduced spontaneous symmetry breaking, which plays an important role in various areas in physics, nuclear star, superfluidity, superconductivity, cold atoms, Higgs boson, Nambu-goldstone boson. When the degree of symmetry breaking is large, it realizes rich phenomena. Since the amount of symmetry breaking can be restated as the amount of asymmetry, it is a central topic to study the amount of asymmetry.

Fortunately, quantum information theory has strong tools to evaluate the amount of asymmetry [1, 2]. The variety generated by group symmetry under a given state can be measured by the number of distinguishable states generated by group action to describe the symmetry. This number is evaluated by the difference between the von Neumann entropy of the averaged state with respect to the group action and the von Neumann entropy of the original state. This fact can be shown by a simple application of quantum channel coding theorem [3, 4] to the family of the states translated by the group action although this idea can be backed to various studies [5, 6, 7]. Therefore, the difference between the von Neumann entropies of these two states can be considered as the amount of the asymmetry of the given state. Now, we focus on the permutation symmetry on qubits similar to the recent paper [8]. When we apply the permutation of the state |1M 0N\ket{1^{M}\,0^{N}}, the averaged state ρmix,N+M,M\rho_{mix,N+M,M} is permutation-invariant, and has no asymmetry. However, it still works as a resource for asymmetry when it is attached to another qubits state |1l 0kl\ket{1^{l}\,0^{k-l}}. When we have only the state |1l 0kl\ket{1^{l}\,0^{k-l}}, the amount of asymmetry is measured by the von Neumann entropy S(ρmix,k,l)S(\rho_{mix,k,l}) of ρmix,k,l\rho_{mix,k,l}. When the state ρmix,N+M,M\rho_{mix,N+M,M} is attached, the amount of asymmetry is S(ρmix,k+N+M,l+M)S(ρmix,N+M,M)S(\rho_{mix,k+N+M,l+M})-S(\rho_{mix,N+M,M}), which is larger than the asymmetry S(ρmix,k,l)S(\rho_{mix,k,l}) of the added state. In this paper, we focus on the increase of the amount of asymmetry by adding an invariant state, and call this phenomena an asymmetry activation. In fact, an asymmetry activation with various groups often happens behind of symmetry breaking. In this paper, we study the asymmetry activation under quantum information-theoretical objects because no systematic research studied the asymmetry activation in the area of quantum information.

Now, we discuss how quantum theory can improve the asymmetry activation because the above state is classical. To address this problem, we focus on the coherence, which is a key concept in quantum theory [9]. That is, we consider a permutation-invariant state with great coherence. We focus on the Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} with the same weight as the classical state ρmix,N+M,M\rho_{mix,N+M,M}. This state has the largest coherence among invariant states with the same weight as the state ρmix,N+M,M\rho_{mix,N+M,M} because any invariant state with the same weight has the same diagonal elements as the state ρmix,N+M,M\rho_{mix,N+M,M}. A Dicke state has been playing an important role in the calculation of entanglement measures [10, 11, 12, 13, 14], quantum communication, and quantum networking [15, 16]. The experiments [15, 16] are motivated by the fact the overlap of symmetric Dicke states with biseparable states is close to 1/2 for large NN [17]. Multipartite entanglement has been detected in an ensemble of thousand of atoms realizing a Dicke state [18]. Some typical Dicke states have been realized in trapped atomic ions [19]. Recently, the multi-qubit Dicke state with half-excitations has been employed to implement a scalable quantum search based on Grover’s algorithm by using adiabatic techniques [20]. These studies show the importance of Dicke states

In particular, the Dicke state can be used for quantum metrology, which has been tested experimentally in cold gases of thousands of atoms [21, 22]. The Dicke states are optimal in linear interferometers, in the sense, that they saturate the inequality FQ[Jx,ρ]+FQ[Jy,ρ]+FQ[Jz,ρ]N(N+2)/4F_{Q}[J_{x},\rho]+F_{Q}[J_{y},\rho]+F_{Q}[J_{z},\rho]\leq N(N+2)/4, which are saturated also by GHZ states [23, 24]. Here, FQF_{Q} is the quantum Fisher information corresponding to a unitary dynamics. Also the reference [25] studied the estimation of the same direction with Dicke states. These studies consider the information only for the direction of the rotation by the special unitary group. They do not focus on the direction of permutation because the Dicke state is invariant for permutation. It is a completely new idea to use the information of the direction of permutation by modifying the Dicke state. To execute this new idea, using non-negative integers n,m,k,ln,m,k,l as M:=mlM:=m-l, N:=nmk+lN:=n-m-k+l, we consider the asymmetry of the following state.

|Ξn,m|k,l:=|1l 0kl|ΞN+M,M(2)n.\displaystyle\ket{\Xi_{n,m|k,l}}:=\ket{1^{l}\,0^{k-l}}\otimes\ket{\Xi_{N+M,M}}\in(\mathbb{C}^{2})^{\otimes n}. (1)

The amount of asymmetry is the von Neumann entropy S(avπ(|Ξn,m|k,l))S(\operatorname{av}_{\pi}(\ket{\Xi_{n,m|k,l}})), where avπ\operatorname{av}_{\pi} expresses the averaged state with respect to the permutation.

However, the calculation of this von Neumann entropy is not simple while S(ρmix,k,l)S(\rho_{mix,k,l}) is calculated to log(kl)\log\binom{k}{l}, which is approximated to lh(k/l)lh(k/l), where h(x):=xlogx(1x)log(1x)h(x):=-x\log x-(1-x)\log(1-x) is the binary entropy. This calculation is closely related to Schur-Weyl duality, one of the key structure in the representation theory. When we make a measurement given by the decomposition defined by Schur-Weyl duality for the system whose state is |Ξn,m|k,l\ket{\Xi_{n,m|k,l}}, we obtain the distribution of this measurement outcome. This distribution takes a key role in this calculation, The paper [26] discovered notable formulas for this distribution by using Hahn and Racah polynomials, which are F23{}_{3}F_{2}- and F34{}_{4}F_{3}-hypergeometric orthogonal polynomials, respectively. It is possible to numerically calculate this von Neumann entropy by using these tools.

Even though its numerical calculation is possible, it is quite difficult to understand its behavior. To grasp its trend, we consider two kinds of asymptotics, i.e., Type I and Type II limits. Type I limit assumes that kk and ll are fixed and mm is linear for nn, i.e., m=ξnm=\xi n with fixed ratio ξ\xi, under nn\to\infty. Type II limit does that k,lk,l and mm are linear for nn under nn\to\infty. Type I limit corresponds to the situation of the law of small numbers, and Type II limit corresponds to the situation of the central limit theorem. For the relation of Type I and Type II limits with the law of small numbers and the central limit theorem, see Appendix A. Based on these formulas, the paper [27] derived the asymptotic approximation of the distribution in the limit nn\to\infty.

In Type I limit, we show that the von Neumann entropy S(avπ[Ξn,m|k,l])S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)} behaves as

S(avπ[Ξn,m|k,l])=((kl)ξ+l(1ξ))logn+O(1).\displaystyle\begin{split}&S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}\\ =&((k-l)\xi+l(1-\xi))\log n+O(1).\end{split} (2)

The same scaling logn\log n appears in the bosonic coherent channel when the total power is fixed and the number nn of modes increases [28]. Figure 1 shows an example of this approximation. When we use the decohered state ρmix,N+M,M\rho_{mix,N+M,M}, the amount of asymmetry is calculated as

S(ρmix,k+N+M,l+M)S(ρmix,N+M,M)=kh(ξ)h(ξ)(ξkl)+O(1).\displaystyle\begin{split}&S(\rho_{mix,k+N+M,l+M})-S(\rho_{mix,N+M,M})\\ =&kh(\xi)-h^{\prime}(\xi)(\xi k-l)+O(1).\end{split} (3)

The amount of asymmetry in (3) is a constant for nn while the case with Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} has the order logn\log n as (2). In particular, in this scenario, while the size of the added system is finite, the amount of activated asymmetry goes to infinity. Therefore, the Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} significantly improves the amount of asymmetry over the decohered state ρmix,N+M,M\rho_{mix,N+M,M} under Type I limit.

In Type II limit, i.e., the limit nn\to\infty with k,lk,l and mm linear for nn, we need to treat many ratios and parameters, which are summarized in Table 1. While the fixed ratios 1 and 2 are natural, the fixed ratio 3 is useful for our purpose.

fixed ratio α=ln\alpha=\frac{l}{n}β=mln\beta=\frac{m-l}{n}
set 1 γ=kln\gamma=\frac{k-l}{n}δ=nmk+ln\delta=\frac{n-m-k+l}{n}
fixed ratio α=ln\alpha=\frac{l}{n}ξ=mn\xi=\frac{m}{n}
set 2 κ=kn\kappa=\frac{k}{n}
fixed ratio β=mln\beta=\frac{m-l}{n}δ=nmk+ln\delta=\frac{n-m-k+l}{n}
set 3 ξ=mn\xi=\frac{m}{n}
limit pdf μ:=1D2\mu:=\frac{1-\sqrt{D}}{2}σ2:=(1βδ)βδD\sigma^{2}:=\frac{(1-\beta-\delta)\beta\delta}{D}
parameters D:=4βδ+(2ξ1)2D:=4\beta\delta+(2\xi-1)^{2}
Table 1: Ratios and parameters for Type II limit

Then, we show that

S(avπ[Ξn,m|k,l])=nh(μ)+o(n).\displaystyle S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=nh(\mu)+o(n). (4)

Since the amount of asymmetry of the added system is log(kl)\log\binom{k}{l} and is approximated to n(α+γ)h(αα+γ)+o(n)n(\alpha+\gamma)h(\frac{\alpha}{\alpha+\gamma})+o(n), the amount of the asymmetry activation is approximated to n(h(μ)(α+γ)h(αα+γ))+o(n)n(h(\mu)-(\alpha+\gamma)h(\frac{\alpha}{\alpha+\gamma}))+o(n).

When we use the decohered state ρmix,N+M,M\rho_{mix,N+M,M}, the amount of asymmetry is calculated as

S(ρmix,k+N+M,l+M)S(ρmix,N+M,M)=n(h(ξ)(β+δ)h(ββ+δ))+o(n).\displaystyle\begin{split}&S(\rho_{mix,k+N+M,l+M})-S(\rho_{mix,N+M,M})\\ =&n(h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}))+o(n).\end{split} (5)

Hence, the amount of the asymmetry activation is approximated to n(h(ξ)(β+δ)h(ββ+δ))(α+γ)h(αα+γ))+o(n)n(h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}))-(\alpha+\gamma)h(\frac{\alpha}{\alpha+\gamma}))+o(n). For this derivation, we employ a kind of central limit theorem obtained in [27]. Since the inequality h(μ)h(ξ)(β+δ)h(ββ+δ)h(\mu)\geq h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}) holds as shown later, the Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} significantly improves the amount of asymmetry over the decohered state ρmix,N+M,M\rho_{mix,N+M,M} even under Type II limit. In addition, similar characterization is also possible from the viewpoint of the number of distinguished states.

When the Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} is decohered, the state changes to the classical state ρmix,N+M,M\rho_{mix,N+M,M}. Since both states are invariant for permutation, their difference is the existence/non-existence of the coherence. Therefore, the difference between (2) and (3) and the difference between (4) and (5) can be considered as the improvement of asymmetry by using the coherence. In addition, the formula (4) has a very simple form and is characterized by the binary entropy of the quantity μ\mu. Since the quantity μ\mu has not appeared in physics, the formula (4) means that the quantity μ\mu is a newly discovered quantity that has a physical role related to asymmetry. Therefore, we can expect that the new quantity μ\mu takes certain roles in other topics.

The remaining part of this paper is organized as follows. As a preparation, Section 2.1 discusses the general theory for the amount of asymmetry, and prepares several formulas for the number of distinguished states. Also, it explains the general theory for asymmetry activation. Section 3 gives our problem setting, and introduces a key distribution to determine the amount of asymmetry. Section 4 discusses the amount of asymmetry for the decohered state. Section 5 reviews the asymptotic behavior of the distribution related to Schur-Weyl duality, which was obtained in the paper [27]. Section 6 derives the asymptotic behavior of the von Neumann entropy of the average state, which expresses the asymptotic behavior. Section 7 considers the asymptotic behavior of the number of distinguishable states. Section 8 gives another example for the asymmetry activation. Section 9 gives the conclusion and discussion.

Appendix B gives the proofs of the statements given in Section 3. Appendix C gives the derivation of the statement given in Section 4. Appendix D gives the derivation of the statements given in Sections 6 and 7. Appendix E shows an asymptotic formula for the von Neumann entropy presented in Section 6.

2 General theory

2.1 General theory for asymmetry

First, we discuss a general theory for asymmetry when a unitary representation ff of a finite group GG is given on \mathcal{H}. Since the invariance of a state ρ\rho under the GG-action ff is expressed as f(g)ρf(g)=|ψψ|f(g)\rho f(g)^{\dagger}=\left|\psi\right>\!\left<\psi\right| for any gGg\in G, the asymmetry of ρ\rho is considered as the degree of change caused by the GG-action. When a state f(g)ρf(g)f(g)\rho f(g)^{\dagger} is generated with equal probability 1|G|\frac{1}{\left|G\right|}, the averaged state with respect to the representation ff is given as

avf[ρ]:=gG1|G|f(g)ρf(g).\displaystyle\operatorname{av}_{f}[\rho]:=\sum_{g\in G}\frac{1}{\left|G\right|}f(g)\rho f(g)^{\dagger}. (6)

In this case, the map gf(g)ρf(g)g\mapsto f(g)\rho f(g)^{\dagger} is considered as a classical-quantum channel, and the mutual information is given as

S(avf[ρ])gG1|G|S(f(g)ρf(g))=S(avf[ρ])S(ρ),\displaystyle\begin{split}&S(\operatorname{av}_{f}[\rho])-\sum_{g\in G}\frac{1}{\left|G\right|}S(f(g)\rho f(g)^{\dagger})\\ =&S(\operatorname{av}_{f}[\rho])-S(\rho),\end{split} (7)

where the von Neumann entropy S(ρ)S(\rho) is defined as Trρlogρ-\operatorname{\mathrm{Tr}}\rho\log\rho. If we use the relative entropy D(ρσ)=Trρ(logρσ)D(\rho\|\sigma)=\operatorname{\mathrm{Tr}}\rho(\log\rho-\sigma), the mutual information is rewritten as

gG1|G|D(f(g)ρf(g)avf[ρ])=D(ρavf[ρ])\displaystyle\sum_{g\in G}\frac{1}{\left|G\right|}D(f(g)\rho f(g)^{\dagger}\|\operatorname{av}_{f}[\rho])=D(\rho\|\operatorname{av}_{f}[\rho]) (8)

because D(f(g)ρf(g)avf[ρ])=D(ρavf[ρ])D(f(g)\rho f(g)^{\dagger}\|\operatorname{av}_{f}[\rho])=D(\rho\|\operatorname{av}_{f}[\rho]). That is, the asymmetry is measured by S(avf[ρ])S(ρ)=D(ρavf[ρ])S(\operatorname{av}_{f}[\rho])-S(\rho)=D(\rho\|\operatorname{av}_{f}[\rho]). Therefore, the mutual information (7) can be considered as the degree of asymmetry of ρ\rho under the representation ff. This quantity was also introduced in the context of the resource theory of asymmetry [2] and other context [1]. In particular, when ρ\rho is a pure state |ψψ|\left|\psi\right>\!\left<\psi\right|, it is simplified to

S(avf[ψ]):=Tr(avf[ψ]logavf[ψ]).\displaystyle S(\operatorname{av}_{f}[\psi]):=-\operatorname{\mathrm{Tr}}_{\mathcal{H}}\bigl{(}\operatorname{av}_{f}[\psi]\log\operatorname{av}_{f}[\psi]\bigr{)}. (9)

Further, when a channel Γ\Gamma satisfies the covariance condition:

Γ(f(g)ρf(g))=f(g)Γ(ρ)f(g),\displaystyle\Gamma(f(g)\rho f(g)^{\dagger})=f(g)\Gamma(\rho)f(g)^{\dagger}, (10)

the information processing inequality for Γ\Gamma implies

S(avf[ρ])S(ρ)=D(ρavf[ρ])\displaystyle S(\operatorname{av}_{f}[\rho])-S(\rho)=D(\rho\|\operatorname{av}_{f}[\rho])
\displaystyle\geq D(Γ(ρ)Γ(avf[ρ]))=D(Γ(ρ)avf[Γ(ρ)])\displaystyle D(\Gamma(\rho)\|\Gamma(\operatorname{av}_{f}[\rho]))=D(\Gamma(\rho)\|\operatorname{av}_{f}[\Gamma(\rho)])
=\displaystyle= S(avf[Γ(ρ)])S(Γ(ρ)).\displaystyle S(\operatorname{av}_{f}[\Gamma(\rho)])-S(\Gamma(\rho)). (11)

This fact shows that the application of a covariant channel decreases the amount of asymmetry.

In fact, to connect the mutual information (7) and the distinguishability, we need to consider the repetitive use of the classical-quantum channel defined by {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G}. When our interest is the case when the above classical-quantum channel is used only once, we need to prepare another measure. In this case, we evaluate the number M(ρ,ϵ)M(\rho,\epsilon) of distinguished states among {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G} with error probability ϵ\epsilon.

Assume that the state ρ\rho is a constant times of a projection to a vv-dimensional subspace and the set {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G} is composed of orthogonal states. In this case avf[ρ]\operatorname{av}_{f}[\rho] is a constant times of a projection to a ww-dimensional subspace. In this case, the number M(ρ,0)M(\rho,0) of distinguishable state is w/vw/v. Also, S(avf[ρ])S(ρ)S(\operatorname{av}_{f}[\rho])-S(\rho) equals logw/v\log w/v. When we allow error probability 11j1-\frac{1}{j} with a positive integer jj, the number of distinguishable inputs is jw/vjw/v. This is because it is allowed that jj informations are represented to the same state. Hence, we have the formula

logM(ρ,11j)=logj+logwv.\displaystyle\log M(\rho,1-\frac{1}{j})=\log j+\log\frac{w}{v}. (12)

Next, we prepare a general formula for M(ρ,ϵ)M(\rho,\epsilon) for a general state ρ\rho when the set of states {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G} is given. We prepare the following notation. Given a Hermitian matrix AA, the symbols {A0}\{A\geq 0\} and {A>0}\{A>0\} express the projections to the eigenspace of AA corresponding to non-negative and strictly positive eigenvalues, respectively. For two Hermitian matrices AA and BB, the symbol {AB}\{A\geq B\} denotes the projection {AB0}\{A-B\geq 0\}. Other projections such as {A>B}\{A>B\} and {AB}\{A\leq B\} are similarly defined. Then, we consider the information spectrum relative entropy DsδD_{s}^{\delta}, which is given by [29]:

Dsδ(ρρ):=max{λTrρ{ρeλρ}δ}.\displaystyle D_{s}^{\delta}(\rho\|\rho^{\prime}):=\max\bigl{\{}\lambda\ \mid\operatorname{\mathrm{Tr}}\rho\{\rho\leq e^{\lambda}\rho^{\prime}\}\leq\delta\bigr{\}}. (13)

Since avf[ρ]\operatorname{av}_{f}[\rho] is invariant for ff, applying [29, Lemma 4] to the case σ=avf[ρ]\sigma=\operatorname{av}_{f}[\rho], we have

logM(ρ,ϵ)Dsϵ+δ(ρavf[ρ])+log1δ.\displaystyle\log M(\rho,\epsilon)\leq D_{s}^{\epsilon+\delta}(\rho\|\operatorname{av}_{f}[\rho])+\log\frac{1}{\delta}. (14)

The paper [7, Theorem 2] derived the following evaluation:

logM(ρ,ϵ)Dsϵδ(ρavf[ρ]))log1δ,\displaystyle\log M(\rho,\epsilon)\geq D_{s}^{\epsilon-\delta}(\rho\|\operatorname{av}_{f}[\rho]))-\log\frac{1}{\delta}, (15)

which is slightly better than [29, Lemma 3] in this special case. We define

Hsϵ(ρ):=max{λTrρ{logρλ}ϵ}\displaystyle H_{s}^{\epsilon}(\rho):=\max\bigl{\{}\lambda\mid\operatorname{\mathrm{Tr}}\rho\{-\log\rho\leq\lambda\}\leq\epsilon\bigr{\}}
=\displaystyle= max{λTrρ{ρeλ}ϵ}.\displaystyle\max\bigl{\{}\lambda\mid\operatorname{\mathrm{Tr}}\rho\{\rho\geq e^{-\lambda}\}\leq\epsilon\bigr{\}}. (16)

When ρ\rho is a pure state, as shown Theorem 12 in B, we have

M(ρ,ϵ)\displaystyle M(\rho,\epsilon) Hsϵδ1δ2(avf[ρ])log1δ1δ2,\displaystyle\geq H_{s}^{\epsilon-\delta_{1}-\delta_{2}}(\operatorname{av}_{f}[\rho])-\log\frac{1}{\delta_{1}\delta_{2}}, (17)
M(ρ,ϵ)\displaystyle M(\rho,\epsilon) Hsϵ+δ1+2δ2(avf[ρ])+log1δ1δ22.\displaystyle\leq H_{s}^{\epsilon+\delta_{1}+2\delta_{2}}(\operatorname{av}_{f}[\rho])+\log\frac{1}{\delta_{1}\delta_{2}^{2}}. (18)

2.2 General theory for asymmetry activation

The asymmetry activation is generally formulated as follows. We consider two quantum systems 1{\cal H}_{1} and 2{\cal H}_{2}, and two unitary representation f1f_{1} and f2f_{2} of compact groups G1G_{1} and G2G_{2} on 1{\cal H}_{1} and 2{\cal H}_{2}, respectively. Then, we consider a larger compact group G3G_{3} that contains G1×G2G_{1}\times G_{2} and its unitary representation f3f_{3} on 12{\cal H}_{1}\otimes{\cal H}_{2}, which satisfies f3|G1×G2=f1f2f_{3}|_{G_{1}\times G_{2}}=f_{1}\otimes f_{2}. We choose an invariant state ρ1\rho_{1} on 1{\cal H}_{1} and a state ρ2\rho_{2} on 2{\cal H}_{2}. Then, the amount of asymmetry of the original state is D(ρ1avf1[ρ1])+D(ρ2avf2[ρ2])=D(ρ2avf2[ρ2])D(\rho_{1}\|\operatorname{av}_{f_{1}}[\rho_{1}])+D(\rho_{2}\|\operatorname{av}_{f_{2}}[\rho_{2}])=D(\rho_{2}\|\operatorname{av}_{f_{2}}[\rho_{2}]). In contrast, the amount of asymmetry of the whole state is D(ρ1ρ2avf3[ρ1ρ2])D(\rho_{1}\otimes\rho_{2}\|\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}]). Hence, the amount of the asymmetry activation is calculated as

D(ρ1ρ2avf3[ρ1ρ2])D(ρ2avf2[ρ2])\displaystyle D(\rho_{1}\otimes\rho_{2}\|\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}])-D(\rho_{2}\|\operatorname{av}_{f_{2}}[\rho_{2}])
=\displaystyle= S(ρ1)S(ρ2)+S(avf3[ρ1ρ2])\displaystyle-S(\rho_{1})-S(\rho_{2})+S(av_{f_{3}}[\rho_{1}\otimes\rho_{2}])
+S(ρ2)S(avf2[ρ2])\displaystyle+S(\rho_{2})-S(\operatorname{av}_{f_{2}}[\rho_{2}])
=\displaystyle= S(ρ1)S(avf2[ρ2])+S(avf3[ρ1ρ2])\displaystyle-S(\rho_{1})-S(\operatorname{av}_{f_{2}}[\rho_{2}])+S(av_{f_{3}}[\rho_{1}\otimes\rho_{2}])
=\displaystyle= D(ρ1avf2[ρ2]avf3[ρ1ρ2]).\displaystyle D(\rho_{1}\otimes\operatorname{av}_{f_{2}}[\rho_{2}]\|\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}]). (19)

Now, we choose a TP-CP map Γ1\Gamma_{1} on 1{\cal H}_{1} and a TP-CP map Γ2\Gamma_{2} on 2{\cal H}_{2}. When the covariance condition

Γ1Γ2(f3(g)ρf3(g))=f3(g)Γ1Γ2(ρ)f3(g)\displaystyle\Gamma_{1}\otimes\Gamma_{2}(f_{3}(g)\rho f_{3}(g)^{\dagger})=f_{3}(g)\Gamma_{1}\otimes\Gamma_{2}(\rho)f_{3}(g)^{\dagger} (20)

holds for gG3g\in G_{3}, the information processing inequality for Γ1Γ2\Gamma_{1}\otimes\Gamma_{2} implies

D(ρ1avf2[ρ2]avf3[ρ1ρ2])\displaystyle D(\rho_{1}\otimes\operatorname{av}_{f_{2}}[\rho_{2}]\|\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}])
\displaystyle\geq D(Γ1[ρ1]Γ2[avf2[ρ2]](Γ1Γ2)[avf3[ρ1ρ2]])\displaystyle D(\Gamma_{1}[\rho_{1}]\otimes\Gamma_{2}[\operatorname{av}_{f_{2}}[\rho_{2}]]\|(\Gamma_{1}\otimes\Gamma_{2})[\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}]])
=\displaystyle= D(Γ[ρ1]avf2[Γ2[ρ2]]avf3[Γ1[ρ1]Γ2[ρ2]]).\displaystyle D(\Gamma[\rho_{1}]\otimes\operatorname{av}_{f_{2}}[\Gamma_{2}[\rho_{2}]]\|\operatorname{av}_{f_{3}}[\Gamma_{1}[\rho_{1}]\otimes\Gamma_{2}[\rho_{2}]]). (21)

When the decoherence operator satisfies the above condition (20), the decoherence reduces the amount of the asymmetry activation.

3 Formulation

This paper investigates how an invariant state activates the amount of asymmetry. To address this question, we focus on the nn-fold tensor product =(2)n\mathcal{H}=(\mathbb{C}^{2})^{\otimes n} of the qubit system 2=|0|1\mathbb{C}^{2}=\mathbb{C}\ket{0}\oplus\mathbb{C}\ket{1}, and consider it as the representation space of the permutation group G=𝔖nG=\mathfrak{S}_{n} acting by permutation of tensor factors as

π(g)(|i1in):=|ig1(1)ig1(n),g𝔖n.\displaystyle\pi(g)(\ket{i_{1}\dotsm i_{n}}):=\ket{i_{g^{-1}(1)}\dotsm i_{g^{-1}(n)}},\quad g\in\mathfrak{S}_{n}. (22)

Then, we apply the general theory given in Subsection 2.2 when 1=(2)N+M{\cal H}_{1}=(\mathbb{C}^{2})^{\otimes N+M}, 2=(2)k{\cal H}_{2}=(\mathbb{C}^{2})^{\otimes k}, G1=𝔖N+MG_{1}=\mathfrak{S}_{N+M}, G2=𝔖kG_{2}=\mathfrak{S}_{k}, G3=𝔖N+M+kG_{3}=\mathfrak{S}_{N+M+k} and ρ2=|1l 0kl(2)k\rho_{2}=\ket{1^{l}\,0^{k-l}}\in(\mathbb{C}^{2})^{\otimes k} is defined as

|1l 0kl:=|11l00kl=|1l|0(kl)\displaystyle\ket{1^{l}\,0^{k-l}}:=|\overbrace{1\dotsm 1}^{l}\,\overbrace{0\dotsm 0}^{k-l}\rangle=\ket{1}^{\otimes l}\otimes\ket{0}^{\otimes(k-l)} (23)

while the state |1l 0kl\ket{1^{l}\,0^{k-l}} is asymmetric with respect to the permutation.

As one possible choice of ρ1\rho_{1}, we choose a Dicke state on (2)(N+M)(\mathbb{C}^{2})^{\otimes(N+M)}, which is a typical invariant state because it is given as the permutation invariant state with fixed weights NN and MM as

|ΞN+M,M\displaystyle\ket{\Xi_{N+M,M}}
:=\displaystyle:= (N+MM)1/2(|1M 0N+permuted terms).\displaystyle\tbinom{N+M}{M}^{-1/2}\bigl{(}\ket{1^{M}\,0^{N}}+\text{permuted terms}\bigr{)}. (24)

Remember that the state |Ξn,m|k,l\ket{\Xi_{n,m|k,l}} is defined as

|Ξn,m|k,l=|1l 0kl|ΞN+M,M(2)n.\displaystyle\ket{\Xi_{n,m|k,l}}=\ket{1^{l}\,0^{k-l}}\otimes\ket{\Xi_{N+M,M}}\in(\mathbb{C}^{2})^{\otimes n}. (25)

In contrast, the decohered state of the Dicke state is given as

ρmix,N+M,M:=(N+MM)1(|1M 0N1M 0N|+permuted terms).\displaystyle\begin{split}&\rho_{mix,N+M,M}\\ :=&\tbinom{N+M}{M}^{-1}\bigl{(}\left|1^{M}\,0^{N}\right>\!\left<1^{M}\,0^{N}\right|+\text{permuted terms}\bigr{)}.\end{split} (26)

To study the asymmetry, we focus on the SU(2)\operatorname{SU}(2)-𝔖n\mathfrak{S}_{n} Schur-Weyl duality on the tensor product system =(2)n\mathcal{H}=(\mathbb{C}^{2})^{\otimes n} depicted as

SU(2)(2)n𝔖n.\displaystyle\operatorname{SU}(2)\curvearrowright(\mathbb{C}^{2})^{\otimes n}\curvearrowleft\mathfrak{S}_{n}. (27)

The Schur-Weyl duality claims that we have the following decomposition of (2)n(\mathbb{C}^{2})^{\otimes n} into irreducible representations (irreps for short):

(2)n=x=0n/2𝒰(nx,x)𝒱(nx,x).\displaystyle(\mathbb{C}^{2})^{\otimes n}=\bigoplus_{x=0}^{{n/2}}\mathcal{U}_{(n-x,x)}\boxtimes\mathcal{V}_{(n-x,x)}. (28)

Here \boxtimes denotes the tensor product of linear spaces, equipped with SU(2)\operatorname{SU}(2)-action on the left and 𝔖n\mathfrak{S}_{n}-action on the right factor, 𝒱(nx,x)\mathcal{V}_{(n-x,x)} denotes the 𝔖n\mathfrak{S}_{n}-irrep corresponding to the partition (nx,x)(n-x,x), and 𝒰(nx,x)\mathcal{U}_{(n-x,x)} denotes the highest weight SU(2)\operatorname{SU}(2)-irrep of dimension n2x+1n-2x+1. Using the hook formula (see [30, I.5, Example 2; I.7, (7.6)] for example), we can compute the dimension of 𝒱(nx,x)\mathcal{V}_{(n-x,x)} as

dim𝒱(nx,x)=k!(nx,x)h()=n2x+1nx+1(nx)=(nx)(nx1),\displaystyle\begin{split}&\dim\mathcal{V}_{(n-x,x)}=\frac{k!}{\prod_{\square\in(n-x,x)}h(\square)}\\ =&\frac{n-2x+1}{n-x+1}\binom{n}{x}=\binom{n}{x}-\binom{n}{x-1},\end{split} (29)

where (nx,x)\square\in(n-x,x) denotes a box in the Young diagram corresponding to the partition (nx,x)(n-x,x). Using this formula, we have the completely mixed state ρmix,𝒱(nx,x)=1dim𝒱(nx,x)𝗂𝖽𝒱(nx,x)\rho_{\text{mix},\mathcal{V}_{(n-x,x)}}=\frac{1}{\dim\mathcal{V}_{(n-x,x)}}\operatorname{\mathsf{id}}_{\mathcal{V}_{(n-x,x)}}. The highest weight SU(2)\operatorname{SU}(2)-irrep of dimension 2j+12j+1 has the standard basis {|j,mm=j,j+1,,j}\{\ket{j,m}\mid m=-j,-j+1,\dotsc,j\}, where jj and mm express the total and z-direction angular momenta, respectively.

In our analysis, the projector 𝖯(nx,x)\mathsf{P}_{(n-x,x)} onto the isotypical component in the decomposition (28) plays a key role, and is defined as

𝖯(nx,x):(2)n-↠𝒰(nx,x)𝒱(nx,x).\displaystyle\mathsf{P}_{(n-x,x)}\colon(\mathbb{C}^{2})^{\otimes n}\relbar\joinrel\twoheadrightarrow\mathcal{U}_{(n-x,x)}\boxtimes\mathcal{V}_{(n-x,x)}. (30)

As the set of the projectors {𝖯(nx,x)}x\{\mathsf{P}_{(n-x,x)}\}_{x} forms a projective measurement, we define the probability p(x|n,m,k,l)p(x\,|\,n,m,k,l) as

p(x|n,m,k,l):=Ξn,m|k,l|𝖯(nx,x)|Ξn,m|k,l,\displaystyle p(x\,|\,n,m,k,l):=\bra{\Xi_{n,m|k,l}}\mathsf{P}_{(n-x,x)}\ket{\Xi_{n,m|k,l}}, (31)

and denote the corresponding probability distribution by Pn,m,k,lP_{n,m,k,l}. Since we can switch |0|1\ket{0}\leftrightarrow\ket{1} in the system, the probability mass function (pmf) enjoys the system symmetry

p(x|n,m,k,l)=p(x|n,nm,k,kl),\displaystyle p(x\,|\,n,m,k,l)=p(x\,|\,n,n-m,k,k-l), (32)

Since the distribution Pn,m,k,lP_{n,m,k,l} describes the averaged state avπ[Ξn,m|k,l]\operatorname{av}_{\pi}[\Xi_{n,m|k,l}] as

avπ[Ξn,m|k,l]=x=0n/2p(x|n,m,k,l)|n2x,n2m2n2x,n2m2|ρmix,𝒱(nx,x),\displaystyle\begin{split}&\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\\ =&\sum_{x=0}^{{n/2}}p(x\,|\,n,m,k,l)\left|\tfrac{n}{2}-x,\tfrac{n-2m}{2}\right>\!\left<\tfrac{n}{2}-x,\tfrac{n-2m}{2}\right|\\ &\boxtimes\rho_{\text{mix},\mathcal{V}_{(n-x,x)}},\end{split} (33)

our analysis is concentrated in the distribution Pn,m,k,lP_{n,m,k,l}. We also denote by XX a discrete random variable distributed by the probability mass function (pmf for short) p(x|n,m,k,l)p(x\,|\,n,m,k,l). We choose integers n,m,kn,m,k and ll from the following set;

𝔑:={(n,m,k,l)04|m,kn,m+knlmk}.\displaystyle\begin{split}&\mathfrak{N}\\ :=&\Big{\{}(n,m,k,l)\in\mathbb{Z}_{\geq 0}^{4}\Big{|}\begin{array}[]{l}m,k\leq n,\\ \ m+k-n\leq l\leq m\wedge k\end{array}\Big{\}}.\end{split} (34)

Then, the von Neumann entropy is given by

S(avπ[Ξn,m|k,l])=xp(x|n,m,k,l)(logdim𝒱(nx,x)logp(x|n,m,k,l)),\displaystyle\begin{split}&S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}\\ =&\sum_{x}p(x\,|\,n,m,k,l)\\ &\cdot\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}-\log p(x\,|\,n,m,k,l)\bigr{)},\end{split} (35)

which expresses the amount of asymmetry of |Ξn,m|k,l\ket{\Xi_{n,m|k,l}}. Furthermore, we can also consider the number of approximately orthogonal elements among the states {π(g)|Ξn,m|k,l}g𝔖n\{\pi(g)\ket{\Xi_{n,m|k,l}}\}_{g\in\mathfrak{S}_{n}}, which is characterized by a slightly different quantity. As explained in Section 7, the number of distinguishable elements with error probability ε>0\varepsilon>0 is approximated by the following value:

H¯sε(avπ[Ξn,m|k,l]):=max{λ|Pn,m,k,l[{xlogdim𝒱(nx,x)logp(x|n,m,k,l)λ}]ϵ}.\displaystyle\begin{split}&\overline{H}_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ &:=\max\biggl{\{}\lambda\bigg{|}P_{n,m,k,l}[\{x\mid\log\dim\mathcal{V}_{(n-x,x)}\\ &\hskip 43.05542pt-\log p(x\,|\,n,m,k,l)\leq\lambda\}]\leq\epsilon\biggr{\}}.\end{split} (36)

4 Asymmetry with decohered symmetric state

First, we discuss the case with the decohered state ρmix,N+M,M\rho_{mix,N+M,M}. Then, we have

S(avπ[|1l 0kl1l 0kl|ρmix,N+M,M])\displaystyle S(\operatorname{av}_{\pi}[\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M}])
S(|1l 0kl1l 0kl|ρmix,N+M,M)\displaystyle-S(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M})
=\displaystyle= S(ρmix,k+N+M,l+M)S(ρmix,N+M,M)\displaystyle S(\rho_{mix,k+N+M,l+M})-S(\rho_{mix,N+M,M})
=\displaystyle= log(l+Mk+N+M)log(MN+M)\displaystyle\log\binom{l+M}{k+N+M}-\log\binom{M}{N+M}
=\displaystyle= log(mn)log(mlnk).\displaystyle\log\binom{m}{n}-\log\binom{m-l}{n-k}. (37)

To consider the relation with S(avπ[Ξn,m|k,l])S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}, we consider the pinching map {\cal E} defined as

(ρ):=x{0,1}n|xx|ρ|xx|,\displaystyle{\cal E}(\rho):=\sum_{\vec{x}\in\{0,1\}^{n}}\left|\vec{x}\right>\!\left<\vec{x}\right|\rho\left|\vec{x}\right>\!\left<\vec{x}\right|, (38)

which expresses the decoherence process. Since the pinching map {\cal E} satisfies the covariance condition (10) with respect to permutation, the relation (11) yields the inequality

S(avπ[Ξn,m|k,l])\displaystyle S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}
\displaystyle\geq S(ρmix,k+N+M,l+M)S(ρmix,N+M,M).\displaystyle S(\rho_{mix,k+N+M,l+M})-S(\rho_{mix,N+M,M}). (39)

This inequality also follows from the inequality (21) for the amount of asymmetry activation. Therefore, the Dicke state realizes a larger amount of asymmetry than the decohered state. However, this inequality does not explain how much the Dicke state improves it over the decohered state. To answer this question, we need to consider the asymptotic behavior.

Here, we consider the number M(|1l 0kl1l 0kl|ρmix,N+M,M,ϵ)M(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M},\epsilon) as well. Counting the possible combinatorics, we find that the number of discriminated states is calculated as M(|1l 0kl1l 0kl|ρmix,N+M,M,0)=(l+Mk+N+M)/(MN+M)M(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M},0)=\binom{l+M}{k+N+M}/\binom{M}{N+M}. Since the set {π(g)|1l 0kl1l 0kl|ρmix,N+M,Mπ(g)}g𝔖n\{\pi(g)\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M}\pi(g)^{\dagger}\}_{g\in\mathfrak{S}_{n}} is composed of orthogonal states, due to (12), the difference between M(|1l 0kl1l 0kl|ρmix,N+M,M,0)M(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M},0) and M(|1l 0kl1l 0kl|ρmix,N+M,M,ϵ)M(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M},\epsilon) with finite error probability ϵ\epsilon is a constant. Hence, we discuss the asymptotics of the value given in (37).

In Type I limit, we have

log(mn)log(mlnk)\displaystyle\log\binom{m}{n}-\log\binom{m-l}{n-k}
=\displaystyle= kh(ξ)h(ξ)(ξkl)+o(1).\displaystyle kh(\xi)-h^{\prime}(\xi)(\xi k-l)+o(1). (40)

That is, the amount of asymmetry is a constant for nn in this case. In Type II limit, we have

log(mn)log(mlnk)\displaystyle\log\binom{m}{n}-\log\binom{m-l}{n-k}
=\displaystyle= n(h(ξ)(β+δ)h(ββ+δ))+12log(β+δ)\displaystyle n(h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}))+\frac{1}{2}\log(\beta+\delta)
12log(2πξ(1ξ))\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}
+12log(2πββ+δ(1ββ+δ))+o(1).\displaystyle+\tfrac{1}{2}\log\bigl{(}2\pi\frac{\beta}{\beta+\delta}(1-\frac{\beta}{\beta+\delta})\bigr{)}+o(1). (41)

The relations (40) and (41) are shown in Appendix C

5 Asymptotics analysis on distribution Pn,m,k,lP_{n,m,k,l}

To derive the asymptotic behaviors of S(avπ[Ξn,m|k,l])S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)} and logM(Ξn,m|k,l,ϵ)\log M({\Xi_{n,m|k,l}},\epsilon), we need the asymptotic analysis of our distribution Pn,m,k,lP_{n,m,k,l} under the limit nn\to\infty. The paper [27] studied the following two cases.

  1. 1.

    The limit nn\to\infty with k,lk,l and m/nm/n fixed.

  2. 2.

    The limit nn\to\infty with m/nm/n, k/nk/n and l/nl/n fixed.

We call them Type I and II limits, respectively. The details are given in Subsection 5.1 and Subsections 5.2. Hereafter we denote by Prn\operatorname{Pr}_{n} the probability for the distribution Pn,nξ,nκ,nαP_{n,n\xi,n\kappa,n\alpha}, and by XX a random variable distributed subject to the distribution. The contents of this section were already shown in the paper [27].

5.1 Asymptotic analysis of Type I limit

To address Type I limit, we employ the binomial distribution Bξ,jB_{\xi,j} with jj trials with successful probability 0ξ10\leq\xi\leq 1 whose pmf is given by x(jx)ξx(1ξ)jxx\mapsto\binom{j}{x}\xi^{x}(1-\xi)^{j-x}. Then, we introduce the distribution PR|ξ;k,l:=Bξ,klB1ξ,lP_{R|\xi;k,l}:=B_{\xi,k-l}*B_{1-\xi,l} where * denotes the convolution of probability distributions. Its pmf is denoted by q(x|ξ;k,l)q(x\,|\,\xi;k,l) and written as

q(x|ξ;k,l)=ξlx(1ξ)klxu=uu(klxu)(lu)ξ2(xu)(1ξ)2u,\displaystyle\begin{split}&q(x\,|\,\xi;k,l)\\ =&\xi^{l-x}(1-\xi)^{k-l-x}\sum_{u=u_{*}}^{u^{*}}\\ &\cdot\binom{k-l}{x-u}\binom{l}{u}\xi^{2(x-u)}(1-\xi)^{2u},\end{split} (42)

where u:=max(0,xk+l)u_{*}:=\max(0,x-k+l) and u:=min(x,l)u^{*}:=\min(x,l).

Theorem 1.

In the limit nn\to\infty with k,lk,l and ξ=m/n\xi=m/n fixed, the distribution Pn,m,k,lP_{n,m,k,l} is approximated to the above-defined distribution PR|ξ;k,lP_{R|\xi;k,l} up to O(1/n)O(1/n). In particular, the asymptotic expectation is given by

limn𝔼[X]=(kl)ξ+l(1ξ).\displaystyle\lim_{n\to\infty}\mathbb{E}[X]=(k-l)\xi+l(1-\xi). (43)

In fact, it is known as the law of small numbers that the distribution Bc/n,nB_{c/n,n} converges to a Poisson distribution as nn goes to infinity with a fixed number cc. In Type I limit, kk and ll are fixed, and correspond to the fixed number cc of the law of small numbers. That is, we can consider Type I limit as the law of small numbers in our setting.

Remark 2.

Since the limit pmf q(x|ξ,k,l)q(x\,|\,\xi,k,l) is a convolution of two binomial distributions. it is a polynomial of ξ\xi, and this property was used in the paper [31].

5.2 Asymptotic analysis of type II limit

We discuss Type II limit. For this aim, we employ three kinds of parametrizations of the fixed ratios as Table 1. For example, the condition (n,m,k,l)𝔑(n,m,k,l)\in\mathfrak{N} in (34) is equivalent to

α,β,γ,δ0.\displaystyle\alpha,\beta,\gamma,\delta\geq 0. (44)

However, in the following, we employ the set of three parameters β,δ,ξ0\beta,\delta,\xi\geq 0, which are also free parameters under the conditions ξβ\xi\geq\beta and 1ξδ1-\xi\geq\delta. Also, the following quantities are fundamental for the discussion:

μ:=1D2,σ:=(1βδ)βδD,D:=4βδ+(2ξ1)2.\displaystyle\begin{split}\mu&:=\frac{1-\sqrt{D}}{2},\leavevmode\nobreak\ \sigma:=\sqrt{\frac{(1-\beta-\delta)\beta\delta}{D}},\\ D&:=4\beta\delta+(2\xi-1)^{2}.\end{split} (45)

These definitions of these parameters are summarized in Table 1.

Theorem 3.

Consider the limit nn\to\infty with fixed ratios α=ln\alpha=\frac{l}{n}, β=mln\beta=\frac{m-l}{n}, γ=kln\gamma=\frac{k-l}{n} and δ=nmk+ln\delta=\frac{n-m-k+l}{n}. Then, for any ε>0\varepsilon\in\mathbb{R}_{>0}, we have

limnPrn[|Xnμ|>ε]=0.\displaystyle\lim_{n\to\infty}\operatorname{Pr}_{n}\Bigl{[}\left|\tfrac{X}{n}-\mu\right|>\varepsilon\Bigr{]}=0. (46)

In particular, the expectation 𝔼[X]\mathbb{E}[X] behaves as

𝔼[X]=nμ+o(n).\displaystyle\mathbb{E}[X]=n\mu+o(n). (47)

Considering the definition of the variance σ2\sigma^{2} by using β,δ,ξ0\beta,\delta,\xi\geq 0, we find the following fact. When the relation α=γ=0\alpha=\gamma=0 nor βδ=0\beta\delta=0 does not hold, i.e.,

α+γ=1βδ,β,δ>0,\displaystyle\alpha+\gamma=1-\beta-\delta,\beta,\delta>0, (48)

the variance σ2\sigma^{2} is well defined by (45) and is strictly positive. Then, we have the following theorem.

Theorem 4.

Assume the condition (48). Then, for any real t<ut<u, the relation

limnPrn[tXnμnσu]=12πtues2/2𝑑s\displaystyle\lim_{n\to\infty}\operatorname{Pr}_{n}\Bigl{[}t\leq\frac{X-n\mu}{\sqrt{n}\sigma}\leq u\Bigr{]}=\frac{1}{\sqrt{2\pi}}\int_{t}^{u}e^{-s^{2}/2}\,ds (49)

holds. In particular, the pmf p(x):=p(x|n,m,k,l)p(x):=p(x\,|\,n,m,k,l) behaves as

p(x)=Ψ(x)(1+o(1))(n),Ψ(x):=12πnσexp(12(xnμnσ)2).\displaystyle\begin{split}p(x)&=\Psi(x)\cdot\bigl{(}1+o(1)\bigr{)}\quad(n\to\infty),\\ \Psi(x)&:=\frac{1}{\sqrt{2\pi n}\sigma}\exp\biggl{(}-\frac{1}{2}\Bigl{(}\frac{x-n\mu}{\sqrt{n}\sigma}\Bigr{)}^{2}\biggr{)}.\end{split} (50)

In addition, we have

𝔼[X]=nμ+ϕ+o(1),ϕ:=σ2μ12μ,\displaystyle\mathbb{E}[X]=n\mu+\phi+o(1),\quad\phi:=\frac{\sigma^{2}-\mu}{1-2\mu}, (51)
limn𝕍[Xn]=limn𝔼[(Xnμn)2]=σ2\displaystyle\lim_{n\to\infty}\mathbb{V}\Bigl{[}\frac{X}{\sqrt{n}}\Bigr{]}=\lim_{n\to\infty}\mathbb{E}\Bigl{[}\Big{(}\frac{X-n\mu}{\sqrt{n}}\Big{)}^{2}\Bigr{]}=\sigma^{2} (52)
limn𝔼[(Xnμn)j]<\displaystyle\lim_{n\to\infty}\mathbb{E}\Bigl{[}\Big{(}\frac{X-n\mu}{\sqrt{n}}\Big{)}^{j}\Bigr{]}<\infty (53)

for j=3,4,.j=3,4,\ldots.

In the special case αγ=0\alpha\gamma=0, we can further discuss the tight exponential evaluation of the tail probability as follows.

Proposition 5.

In Type II limit with αβδ>0\alpha\beta\delta>0, γ=0\gamma=0 and 0<ξ=α+β1/20<\xi=\alpha+\beta\leq 1/2, the probability Prn[|Xnμ|ϵ]\operatorname{Pr}_{n}\bigl{[}|\tfrac{X}{n}-\mu|\geq\epsilon\bigr{]} goes to zero exponentially for any ϵ>0\epsilon>0.

6 Asymptotics analysis on S(avπ[Ξn,m|k,l])S\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}

As an interlude, let us derive the asymptotic formula (2) of S(avπ[Ξn,m|k,l])S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]) for the state Ξn,m|k,l\Xi_{n,m|k,l} with respect to the distribution Pn,m,k,lP_{n,m,k,l} in Type I limit nn\to\infty with fixed ξ=m/n\xi=m/n, kk and ll. We denote the pmf by p(x):=p(x|n,m,k,l)p(x):=p(x\,|\,n,m,k,l), and the limit pmf by q(x):=q(x|ξ;k,l)q(x):=q(x\,|\,\xi;k,l) for simplicity. Thus, we have p(x)=q(x)+O(1/n)p(x)=q(x)+O(1/n) by Theorem 1. Now, recall the expression (35) of S(avπ[Ξn,m|k,l])S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]), which includes the dimension formula dim𝒱(nx,x)=(nx)n2x+1nx+1\dim\mathcal{V}_{(n-x,x)}=\binom{n}{x}\frac{n-2x+1}{n-x+1} in (29). Let us cite from [32, Chap. 5, (5.12)] the asymptotic formula of binomial coefficient (nx)\binom{n}{x} for fixed xx:

log(nx)=xlogn+x(x+12)logxlog2π+o(1).\displaystyle\begin{split}&\log\tbinom{n}{x}\\ =&x\log n+x-(x+\tfrac{1}{2})\log x-\log\sqrt{2\pi}+o(1).\end{split} (54)

Applying it to the dimension formula, we show (2) as

S(avπ[Ξn,m|k,l])=x=0kp(x)(logdim𝒱(nx,x)logp(x))=x=0kq(x)(xlogn+x(x+12)logxlog2πlogq(x))+o(1)=ulogn+ulog2π+x=0kq(x)((x+12)logxlogq(x))+o(1),\displaystyle\begin{split}&S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ =&\sum_{x=0}^{k}p(x)\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)\bigr{)}\\ =&\sum_{x=0}^{k}q(x)\bigl{(}x\log n+x-(x+\tfrac{1}{2})\log x\\ &-\log\sqrt{2\pi}-\log q(x)\bigr{)}+o(1)\\ =&u\log n+u-\log\sqrt{2\pi}\\ &+\sum_{x=0}^{k}q(x)\bigl{(}(x+\tfrac{1}{2})\log x-\log q(x)\bigr{)}+o(1),\end{split} (55)

where we used the asymptotic expectation u:=(kl)ξ+l(1ξ)u:=(k-l)\xi+l(1-\xi) in (43). The last line in (55) shows the conclusion (2). Therefore, with the comparison with (37) and (40), Dicke state |ΞN+M,M\ket{\Xi_{N+M,M}} significantly improves the amount of asymmetry over the classical symmetric state ρmix,N+M,M\rho_{mix,N+M,M}.

Refer to caption
Figure 1: Plots of S(avπ[Ξn,m|k,l])/lognS\bigl{(}\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]\bigr{)}/\log n (blue dots) and the approximation a(n,ξ,k,l)/logna(n,\xi,k,l)/\log n (red dots) for (ξ=m/n,k,l)=(0.5,2,1)(\xi=m/n,k,l)=(0.5,2,1). This case implies u:=(kl)ξ+l(1ξ)=1u:=(k-l)\xi+l(1-\xi)=1. Here, a(n,ξ,k,l)a(n,\xi,k,l) is defined as ulogn+ulog2π+x=0kq(x)((x+12)logxlogq(x))u\log n+u-\log\sqrt{2\pi}+\sum_{x=0}^{k}q(x)\bigl{(}(x+\tfrac{1}{2})\log x-\log q(x)\bigr{)} by using (55). The horizontal axis is log10n\log_{10}n, and the black line shows the limit value u=1u=1.

Continuing the interlude, we consider Type II limit of S(avπ[Ξn,m|k,l])S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]). When β=0\beta=0, i.e., ml=0m-l=0, we have avπ[Ξn,m|k,l]=ρmix,n,m\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]=\rho_{mix,n,m}. Hence, we have

S(avπ[Ξn,m|k,l])=S(ρmix,n,l)=log(mn),\displaystyle S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=S(\rho_{mix,n,l})=\log\binom{m}{n}, (56)

which was already discussed in Section 4. The same discussion can be applied to the case with δ=0\delta=0. When α+γ=0\alpha+\gamma=0, we have |Ξn,m|k,l=|Ξn,m\ket{\Xi_{n,m|k,l}}=\ket{\Xi_{n,m}} so that S(avπ[Ξn,m|k,l])=S(|Ξn,m)=0S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=S(\ket{\Xi_{n,m}})=0. In this case, S(avπ[|1l 0kl1l 0kl|ρmix,N+M,M])S(|1l 0kl1l 0kl|ρmix,N+M,M)S(\operatorname{av}_{\pi}[\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M}])-S(\left|1^{l}\,0^{k-l}\right>\!\left<1^{l}\,0^{k-l}\right|\otimes\rho_{mix,N+M,M}) is also zero. These cases have no difference from the case in Section 4. Therefore, we assume the assumption (48) in the following.

We have

S(avπ[Ξn,m|k,l])=x=0kp(x)(nh(μ)+h(μ)(xnμ)+O((xnμn)2))+O(logn),\displaystyle\begin{split}&S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ =&\sum_{x=0}^{k}p(x)\Big{(}nh(\mu)+h^{\prime}(\mu)(x-n\mu)\\ &+O((\frac{x-n\mu}{\sqrt{n}})^{2})\Big{)}+O(\log n),\end{split} (57)

which is shown in Appendix D. Theorem 4 implies that

S(avπ[Ξn,m|k,l])=nh(μ)+O(logn).\displaystyle S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=nh(\mu)+O(\log n). (58)

When the relation αγ=0\alpha\gamma=0 holds additionally, the relation (58) is replaced by a more precise form as follows

S(avπ[Ξn,m|k,l])=nh(μ)+C0+o(1).\displaystyle S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=nh(\mu)+C_{0}+o(1). (59)

This relation will be shown in Proposition 14 in E, and the the precise form of the constant C0C_{0} is given in Proposition 14.

Due to (37) and (41), the comparison between the Dicke state and the decohered state is summarized to the comparison between their first-order terms h(μ)h(\mu) and h(ξ)(β+δ)h(ββ+δ)h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}). In fact, the limit nn\to\infty in (39) with the division by nn implies

h(μ)h(ξ)(β+δ)h(ββ+δ).\displaystyle h(\mu)\geq h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}). (60)

Here, we employ the relations (41) and (58). The difference between LHS and RHS shows the asymptotic difference of the amounts of the asymmetry activation caused by the decoherence. We consider the case when τ=0\tau=0, in which two free parameters describe other parameters as

α=ξκ,β=ξ(1κ),γ=(1ξ)κ,δ=(1ξ)(1κ).\displaystyle\begin{split}\alpha&=\xi\kappa,\leavevmode\nobreak\ \beta=\xi(1-\kappa),\\ \gamma&=(1-\xi)\kappa,\leavevmode\nobreak\ \delta=(1-\xi)(1-\kappa).\end{split} (61)

Then, since ββ+δ=ξ\frac{\beta}{\beta+\delta}=\xi and D=4(1ξ)ξ(1κ)2+(2ξ1)2D=4(1-\xi)\xi(1-\kappa)^{2}+(2\xi-1)^{2}, we have

h(μ)=h(1(1κ)(1ξ)ξ(1κ)24(1ξ)ξ(1κ)2+(2ξ1)22),h(ξ)(β+δ)h(ββ+δ)=κh(ξ).\displaystyle\begin{split}&h(\mu)=h(\frac{1-\sqrt{\frac{(1-\kappa)(1-\xi)\xi(1-\kappa)^{2}}{4(1-\xi)\xi(1-\kappa)^{2}+(2\xi-1)^{2}}}}{2}),\\ &h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta})=\kappa h(\xi).\end{split} (62)

In this case, h(μ)h(\mu) and h(ξ)(β+δ)h(ββ+δ)h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}) are plotted as Fig. 2.

Refer to caption
Figure 2: Comparison between h(μ)h(\mu) and h(ξ)(β+δ)h(ββ+δ)h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}). We consider the case with (61). The base of the logarithm is 22. We set ξ\xi to be 0.3. The horizontal line shows κ\kappa, which runs from 0 to 11. The green line shows h(μ)h(\mu), and the blue line shows h(ξ)(β+δ)h(ββ+δ)h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}).

7 Asymptotics analysis on logM(Ξn,m|k,l,ϵ)\log M({\Xi_{n,m|k,l}},\epsilon)

Next, to address the asymptotic behavior of logM(Ξn,m|k,l,ϵ)\log M({\Xi_{n,m|k,l}},\epsilon), we apply the discussion in Section 2.1 to our setting. Since the state avπ[Ξn,m|k,l]\operatorname{av}_{\pi}[\Xi_{n,m|k,l}] is characterized as (33), Hsϵ(avπ[Ξn,m|k,l])H_{s}^{\epsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]) equals H¯sε(avπ[Ξn,m|k,l])\overline{H}_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]) which is defined in (36). Hence,

Hsε(avπ[Ξn,m|k,l])=max{λPn,m,k,l[{xlogdim𝒱(nx,x)logp(x|n,m,k,l)λ}]ϵ}.\displaystyle\begin{split}&H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ =&\max\bigl{\{}\lambda\mid P_{n,m,k,l}[\{x\mid\log\dim\mathcal{V}_{(n-x,x)}\\ &\hskip 43.05542pt-\log p(x\,|\,n,m,k,l)\leq\lambda\}]\leq\epsilon\bigr{\}}.\end{split} (63)

To discuss Type I limit, we denote the cdf of Bξ,klB1ξ,lB_{\xi,k-l}*B_{1-\xi,l} by F[Bξ,klB1ξ,l]F[B_{\xi,k-l}*B_{1-\xi,l}]. By recalling the dimension formula (29) of dim𝒱(nx,x)\dim\mathcal{V}_{(n-x,x)}, logdim𝒱(nx,x)\log\dim\mathcal{V}_{(n-x,x)} is approximated to xlogn+o(logn)x\log n+o(\log n) with a fixed value xx by (54). When nn is sufficiently large, logdim𝒱(nx,x)logp(x|n,m,k,l)\log\dim\mathcal{V}_{(n-x,x)}-\log p(x\,|\,n,m,k,l) is monotonically increasing for xx. We choose xn(ϵ)x_{n}(\epsilon) as F[Pn,m,k,l](xn(ϵ))=ϵF[P_{n,m,k,l}](x_{n}(\epsilon))=\epsilon. Hence, the combination of (63) and Theorem 1 implies

Hsε(avπ[Ξn,m|k,l])=logdim𝒱(nxn(ϵ),xn(ϵ))logp(xn(ϵ)|n,m,k,l)=xn(ϵ)logn+o(logn)log(q(xn(ϵ)|ξ;k,l)+O(1/n))=F[Bξ,klB1ξ,l]1(ϵ)logn+o(logn).\displaystyle\begin{split}&H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ =&\log\dim\mathcal{V}_{(n-x_{n}(\epsilon),x_{n}(\epsilon))}-\log p(x_{n}(\epsilon)\,|\,n,m,k,l)\\ =&x_{n}(\epsilon)\log n+o(\log n)-\log(q(x_{n}(\epsilon)\,|\,\xi;k,l)\\ &+O(1/n))\\ =&F[B_{\xi,k-l}*B_{1-\xi,l}]^{-1}(\epsilon)\log n+o(\log n).\end{split} (64)

The combination of (17), (18), and (64) yields

logM(Ξn,m|k,l,ϵ)=F[Bξ,klB1ξ,l]1(ϵ)logn+o(logn).\displaystyle\begin{split}&\log M(\Xi_{n,m|k,l},\epsilon)\\ =&F[B_{\xi,k-l}*B_{1-\xi,l}]^{-1}(\epsilon)\log n+o(\log n).\end{split} (65)

In Type II limit, when the assumption (48) does not hold, the analysis on logM(Ξn,m|k,l,ϵ)\log M(\Xi_{n,m|k,l},\epsilon) is the same as the case in Section 4. Therefore, we assume the assumption (48) in the following. Then, as shown in Appendix D, we have

Hsε(avπ[Ξn,m|k,l])=nh(μ)+h(μ)(xn(ϵ)nμ)+O((xn(ϵ)nμn)2)+O(logn).\displaystyle\begin{split}&H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\\ =&nh(\mu)+h^{\prime}(\mu)(x_{n}(\epsilon)-n\mu)\\ &+O((\frac{x_{n}(\epsilon)-n\mu}{\sqrt{n}})^{2})+O(\log n).\end{split} (66)

Theorem 4 implies that

Hsε(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
=\displaystyle= h(μ+Φ1(ϵ)σn+o(1n))n+O(logn)\displaystyle h(\mu+\frac{\Phi^{-1}(\epsilon)}{\sigma\sqrt{n}}+o(\frac{1}{\sqrt{n}}))n+O(\log n)
=\displaystyle= nh(μ)+nh(μ)Φ1(ϵ)σ+o(n).\displaystyle nh(\mu)+\sqrt{n}h^{\prime}(\mu)\frac{\Phi^{-1}(\epsilon)}{\sigma}+o(\sqrt{n}). (67)

The combination of (17), (18), (67) yields

logM(Ξn,m|k,l,ϵ)=nh(μ)+nh(μ)Φ1(ϵ)σ+o(n).\displaystyle\begin{split}&\log M(\Xi_{n,m|k,l},\epsilon)\\ =&nh(\mu)+\sqrt{n}h^{\prime}(\mu)\frac{\Phi^{-1}(\epsilon)}{\sigma}+o(\sqrt{n}).\end{split} (68)

These relations (65) and (68) show the asymptotic behavior of the degree of the asymmetry. The asymptotic expansion (67) has Gaussian distribution in the second order term, which is similar to the asymptotic distillation of the coherence [33].

8 Another example of asymmetry activation

To consider another simple example of asymmetry activation, we choose (n)n(\mathbb{C}^{n})^{\otimes n}, SU(n)\operatorname{SU}(n), and the nn-tensor product representation of SU(n)\operatorname{SU}(n) as 1{\cal H}_{1}, G1G_{1}, and f1f_{1}, respectively. Then, the nn-tensor anti-symmetric subspace 𝒜n,n{\cal A}_{n,n} of SU(n)\operatorname{SU}(n) is a one-dimensional space. We choose the unique state on the nn-tensor anti-symmetric subspace of SU(n)\operatorname{SU}(n) as the invariant state ρ1\rho_{1}. Given dnd\geq n, we choose (d)n(\mathbb{C}^{d})^{\otimes n}, SU(d)\operatorname{SU}(d), and the 22-tensor product representation of SU(d)\operatorname{SU}(d) as 2{\cal H}_{2}, G2G_{2}, and f2f_{2}, respectively.

We choose SU(nd)\operatorname{SU}(nd) and the nn-tensor product representation of SU(nd)\operatorname{SU}(nd) as G3G_{3} and f3f_{3}, respectively. We choose the state ρ2\rho_{2} as a state on the anti-symmetric subspace 𝒜d,n{\cal A}_{d,n} of (d)n(\mathbb{C}^{d})^{\otimes n}. Since the dimension of 𝒜d,n{\cal A}_{d,n} is (dn){d\choose n}, D(ρ2avf2[ρ2])=log(dn)S(ρ2)D(\rho_{2}\|\operatorname{av}_{f_{2}}[\rho_{2}])=\log{d\choose n}-S(\rho_{2}). In this case, the vector of the support of ρ1ρ2\rho_{1}\otimes\rho_{2} is invariant for the exchange of the first and second system. Hence, the vector of the support belongs to the symmetric subspace 𝒮nd,n{\cal S}_{nd,n} of (nd)n(\mathbb{C}^{nd})^{\otimes n}, whose dimension is (nd+n1n){nd+n-1\choose n}. Thus, we have D(ρ1ρ2avf3[ρ1ρ2])=log(nd+n1n)S(ρ2)D(\rho_{1}\otimes\rho_{2}\|\operatorname{av}_{f_{3}}[\rho_{1}\otimes\rho_{2}])=\log{nd+n-1\choose n}-S(\rho_{2}). Therefore, the amount of the asymmetry activation is

log(nd+n1n)log(dn)\displaystyle\log{nd+n-1\choose n}-\log{d\choose n}
=\displaystyle= j=0n1lognd+n1jdj\displaystyle\sum_{j=0}^{n-1}\log\frac{nd+n-1-j}{d-j}
=\displaystyle= j=0n1log(n+(n1)(j+1)dj).\displaystyle\sum_{j=0}^{n-1}\log(n+\frac{(n-1)(j+1)}{d-j}). (69)

Given a fixed integer nn, the amount of the asymmetry activation takes the maximum log(n2+n1n)\log{n^{2}+n-1\choose n} when d=nd=n. In this case, this value is approximated to be nlogn+O(n)n\log n+O(n).

9 Conclusion and discussion

We have shown that the coherence in the Dicke state enhances the asymmetry for the permutation when a qubits state |1l 0kl\ket{1^{l}\,0^{k-l}} is attached. To clarify the merit of the coherence, we have discussed two types of limits, Type I and II limits. In these limits, the case with the Dicke state has a larger amount of degree than the case with the decohered state. In particular, under Type I limit, the amount of the degree in the former has a strictly larger order than that of the latter. Under Type II limit, both cases have different leading terms. Their difference characterizes the effect of the existence of the coherence. This fact shows the importance of the coherence even in the asymmetry and the asymmetry activation for permutation.

Here, we emphasize the generality of the concept of asymmetry activation. Asymmetry activation may happen in various situations. To explain this aspect, we have shown another example in Section 8. It is another interesting future study to discuss various examples of asymmetry activation.

Using this discussion, we have derived the inequality (60). Since the inequality (60) can be considered without this type of limit, it is possible to directly derive this inequality without considering this type of limit. This kind of derivation is an interesting future study. Further, our analysis has revealed the physical importance of the value μ\mu, whose importance has not been recognized until this study.

For our asymptotic derivations, we have employed various asymptotic formulas for the distribution Pn,m,k,lP_{n,m,k,l}. Since the distribution Pn,m,k,lP_{n,m,k,l} has a highly complicated form, this fact shows the usefulness of these asymptotic formulas. Since the distribution Pn,m,k,lP_{n,m,k,l} is based on the the Schur-Weyl duality, which is a key structure in quantum information, we can expect that these formulas can be used in other topics in quantum information.

Appendix A Type I and Type II limits and Limit theorems for binomial distributions

First, we summarize existing results for binomial distributions. Given a real number p(0,1)p\in(0,1), we define the binomial distribution as

Pn,p(k):=(nk)pk(1p)nk.\displaystyle P_{n,p}(k):={n\choose k}p^{k}(1-p)^{n-k}. (70)

The central limit theorem addresses the case when pp is fixed and nn increases. It states

limnk=np+x0nnp+x1nPn,p(k)\displaystyle\lim_{n\to\infty}\sum_{k=np+x_{0}\sqrt{n}}^{np+x_{1}\sqrt{n}}P_{n,p}(k)
=\displaystyle= x0x112πp(1p)ex22p(1p).\displaystyle\int_{x_{0}}^{x_{1}}\frac{1}{\sqrt{2\pi p(1-p)}}e^{-\frac{x^{2}}{2p(1-p)}}. (71)

However, the above convergence is not uniform when pp is close to 0 or 11. To cover the asymptotic behavior in this case, we consider the case when pp is chosen to λ/n\lambda/n and nn increases. Then, we have the following relation

limnPn,λ/n(k)=eλλkk!,\displaystyle\lim_{n\to\infty}P_{n,\lambda/n}(k)=e^{-\lambda}\frac{\lambda^{k}}{k!}, (72)

which is called the law of small numbers.

In our model, m=M+lm=M+l expresses the total number of 11 and n=N+m+kln=N+m+k-l expresses the total number of 0. Hence, the ratio mn+m\frac{m}{n+m} corresponds to the parameter pp in the binomial distribution. Hence, Type I and Type II limits corresponds to the law of small number and the central limit theorem, respectively. Therefore, although Type II limit works with ξ=mn>0\xi=\frac{m}{n}>0, when ξ=mn\xi=\frac{m}{n} is close to zero, Type II limit does not work so that Type I is needed for this case.

Appendix B Single-shot bounds for cq-channels

The aim of this section is to derive the inequalities (17) and (18), which will be shown in Theorem 9. We begin with a general setting of classical-quantum channel explained in Subsection 7.

B.1 Single-shot bound for general cq-channel

We consider a pure state channel 𝒳xWx\mathcal{X}\ni x\mapsto W_{x} where WxW_{x} is a state on a Hilbert space \mathcal{H}. Let M(ρ,ϵ)M(\rho,\epsilon) be the number of distinguished states among {Wx}x𝒳\{W_{x}\}_{x\in\mathcal{X}}. We consider a joint system 𝒳\mathcal{H}_{\mathcal{X}}\otimes\mathcal{H}, where 𝒳\mathcal{H}_{\mathcal{X}} is a Hilbert space spanned by {|x}x𝒳\{\ket{x}\}_{x\in\mathcal{X}}. Dependently of a distribution PP on 𝒳\mathcal{X} and a state ρ\rho on \mathcal{H}, we define two states R[P]R[P] and S[P,ρ]S[P,\rho] on the joint system 𝒳\mathcal{H}_{\mathcal{X}}\otimes\mathcal{H} by

R[P]:=xP(x)|xx|Wx,S[P,ρ]:=(xP(x)|xx|)ρ,\displaystyle\begin{split}R[P]&:=\sum_{x}P(x)\left|x\right>\!\left<x\right|\otimes W_{x},\\ S[P,\rho]&:=\Bigl{(}\sum_{x}P(x)\left|x\right>\!\left<x\right|\Bigr{)}\otimes\rho,\end{split} (73)

and a state WPW_{P} on \mathcal{H} by

WP:=xP(x)Wx.\displaystyle W_{P}:=\sum_{x}P(x)W_{x}. (74)

Recall the information spectrum relative entropy DsδD_{s}^{\delta} in (13):

Dsδ(ρρ):=max{λTrρ{ρeλρ}δ},D_{s}^{\delta}(\rho\|\rho^{\prime}):=\max\bigl{\{}\lambda\mid\operatorname{\mathrm{Tr}}\rho\{\rho\leq e^{\lambda}\rho^{\prime}\}\leq\delta\bigr{\}}, (75)

The hypothesis testing relative entropy DHϵD_{H}^{\epsilon} is defined by [34]

DHϵ(ρρ):=logmin{TrQρ0QI,TrQρ1ϵ}.\displaystyle\begin{split}&D_{H}^{\epsilon}(\rho\|\rho^{\prime})\\ :=&-\log\min\bigl{\{}\operatorname{\mathrm{Tr}}{Q\rho^{\prime}}\mid 0\leq Q\leq I,\operatorname{\mathrm{Tr}}{Q\rho}\geq 1-\epsilon\bigr{\}}.\end{split} (76)

Nagaoka [35] essentially showed the following inequality for any channel.

Lemma 6.

For any state ρ\rho^{\prime}, we have

M(ρ,ϵ)maxPDHϵ(R[P]S[P,ρ]).\displaystyle M(\rho,\epsilon)\leq\max_{P}D_{H}^{\epsilon}(R[P]\|S[P,\rho^{\prime}]). (77)

By [36, Corollary 1], we have the following inequality for any mixed state ρ¯\bar{\rho}:

Lemma 7.

The inequality

M(ρ,ϵ)maxPDsδ(R[P]S[P,WP])log1ϵϵδ\displaystyle M(\rho,\epsilon)\geq\max_{P}D_{s}^{\delta}(R[P]\|S[P,W_{P}])-\log\frac{1-\epsilon}{\epsilon-\delta} (78)

holds.

By [37, (22)] and [38, Lemma 12], we also have:

Lemma 8.

The inequality

Dsϵ(ρρ)DHϵ(ρρ)Dsϵ+δ(ρρ)logδ\displaystyle D_{s}^{\epsilon}(\rho\|\rho^{\prime})\leq D_{H}^{\epsilon}(\rho\|\rho^{\prime})\leq D_{s}^{\epsilon+\delta}(\rho\|\rho^{\prime})-\log\delta (79)

holds.

B.2 Single-shot bound for pure state channel

In the case when the states WxW_{x} are pure states, we have the following theorem.

Theorem 9.

When all states WxW_{x} are pure states, we have the following relations.

M(ρ,ϵ)\displaystyle M(\rho,\epsilon) maxPHsϵδ1δ2(WP)log1ϵδ1δ2,\displaystyle\geq\max_{P}H_{s}^{\epsilon-\delta_{1}-\delta_{2}}(W_{P})-\log\frac{1-\epsilon}{\delta_{1}\delta_{2}}, (80)
M(ρ,ϵ)\displaystyle M(\rho,\epsilon) maxPHsϵ+δ1+2δ2(WP)+log1δ1δ22.\displaystyle\leq\max_{P}H_{s}^{\epsilon+\delta_{1}+2\delta_{2}}(W_{P})+\log\frac{1}{\delta_{1}\delta_{2}^{2}}. (81)

To show Theorem 9, we prepare the following Lemmas 10 and 11, using the fact that WxW_{x} is a pure state. Although these two lemmas were essentially proved in [28, Appendix II], it is difficult to extract them from the reference, and we present them here with proofs.

Lemma 10.

The relation

DHϵ(R[P]S[P,WP])Hsϵ(WP)\displaystyle D_{H}^{\epsilon}(R[P]\|S[P,W_{P}])\geq H_{s}^{\epsilon}(W_{P}) (82)

holds.

Proof.

We define the projection Wx′′W_{x}^{\prime\prime} as

Wx′′\displaystyle W_{x}^{\prime\prime}
:=\displaystyle:= {1TrWx{I>eλWP}{I>eλWP}Wx{I>eλWP} when TrWx{IeλWP}00 when TrWx{IeλWP}=0.\displaystyle\begin{cases}\frac{1}{\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}}\{I>e^{\lambda}W_{P}\}W_{x}\{I>e^{\lambda}W_{P}\}\\ \hskip 43.05542pt\text{ when }\operatorname{\mathrm{Tr}}W_{x}\{I\geq e^{\lambda}W_{P}\}\neq 0\\ 0\\ \hskip 43.05542pt\text{ when }\operatorname{\mathrm{Tr}}W_{x}\{I\geq e^{\lambda}W_{P}\}=0.\end{cases} (83)

Hence, we have

TrWx{I>eλWP}=TrWxWx′′,\displaystyle\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}=\operatorname{\mathrm{Tr}}W_{x}W_{x}^{\prime\prime}, (84)

and thus

TrWP{IeλWP}\displaystyle\operatorname{\mathrm{Tr}}W_{P}\{I\geq e^{\lambda}W_{P}\}
=\displaystyle= xP(x)TrWx{IeλWP}\displaystyle\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}\{I\geq e^{\lambda}W_{P}\}
=\displaystyle= xP(x)TrWx(IWx′′).\displaystyle\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}(I-W_{x}^{\prime\prime}). (85)

Since the support of Wx′′W_{x}^{\prime\prime} is included in the support of {I>eλWP}\{I>e^{\lambda}W_{P}\} and Wx′′W_{x}^{\prime\prime} is the zero matrix or a rank-one projection, Wx′′WPWx′′W_{x}^{\prime\prime}W_{P}W_{x}^{\prime\prime} is the zero matrix or a rank-one matrix whose eigenvalue is not greater than eλe^{-\lambda}. Hence,

TrWPWx′′eλ.\displaystyle\operatorname{\mathrm{Tr}}W_{P}W_{x}^{\prime\prime}\leq e^{-\lambda}. (86)

We define the projection TT as

T:=x|xx|Wx′′.\displaystyle T:=\sum_{x}\left|x\right>\!\left<x\right|\otimes W_{x}^{\prime\prime}. (87)

Then, using (85) and (86), we have

TrS[P,WP]T\displaystyle\operatorname{\mathrm{Tr}}S[P,W_{P}]T eλ,\displaystyle\leq e^{-\lambda},
TrR[P](IT)\displaystyle\operatorname{\mathrm{Tr}}R[P](I-T) =TrWP{IeλWP}.\displaystyle=\operatorname{\mathrm{Tr}}W_{P}\{I\geq e^{\lambda}W_{P}\}.

Lemma 11.

We have

Dsϵ(R[P]S[P,xP(x)Wx])\displaystyle D_{s}^{\epsilon}\Big{(}R[P]\Big{\|}S\Big{[}P,\sum_{x}P(x)W_{x}\Big{]}\Big{)}
\displaystyle\leq Hsϵ+2δ(xP(x)Wx)+2log1δ.\displaystyle H_{s}^{\epsilon+2\delta}\Bigl{(}\sum_{x}P(x)W_{x}\Bigr{)}+2\log\frac{1}{\delta}. (88)
Proof.

In this proof, we set WP:=xP(x)WxW_{P}:=\sum_{x}P(x)W_{x}. We choose λ:=2log1δ\lambda^{\prime}:=2\log\frac{1}{\delta} and assume that

λ=Hsϵδ(xP(x)Wx).\displaystyle\lambda=H_{s}^{\epsilon-\delta}\Big{(}\sum_{x}P(x)W_{x}\Big{)}. (89)

We define the non-negative matrix WxW_{x}^{\prime} as

Wx:={Wx>eλ+λWP}\displaystyle W_{x}^{\prime}:=\{W_{x}>e^{\lambda+\lambda^{\prime}}W_{P}\} (90)

Since WxW_{x} is a rank-one projection, {Wx>eλ+λWP}\{W_{x}>e^{\lambda+\lambda^{\prime}}W_{P}\} is a rank-one projection or 0, which implies TrWxWx1\operatorname{\mathrm{Tr}}W_{x}^{\prime}W_{x}\leq 1. Since TrWx(Wxeλ+λWP)0\operatorname{\mathrm{Tr}}W_{x}^{\prime}(W_{x}-e^{\lambda+\lambda^{\prime}}W_{P})\geq 0, we have

TrWx(eλ+λWP)TrWxWx1.\displaystyle\operatorname{\mathrm{Tr}}W_{x}^{\prime}(e^{\lambda+\lambda^{\prime}}W_{P})\leq\operatorname{\mathrm{Tr}}W_{x}^{\prime}W_{x}\leq 1. (91)

Then, we have

TrWx(eλWP)eλ.\displaystyle\operatorname{\mathrm{Tr}}W_{x}^{\prime}(e^{\lambda}W_{P})\leq e^{-\lambda^{\prime}}. (92)

Also we have

{IeλWP}eλWP.\displaystyle\{I\leq e^{\lambda}W_{P}\}\leq e^{\lambda}W_{P}. (93)

The combination of (93) and (91) implies

TrWx{IeλWP}TrWx(eλWP)eλ.\displaystyle\operatorname{\mathrm{Tr}}W_{x}^{\prime}\{I\leq e^{\lambda}W_{P}\}\leq\operatorname{\mathrm{Tr}}W_{x}^{\prime}(e^{\lambda}W_{P})\leq e^{-\lambda^{\prime}}. (94)

Now, we have

Wx{I>eλWP}Wx{I>eλWP}1={I>eλWP}Wx{IeλWP}+{IeλWP}Wx1{I>eλWP}Wx{IeλWP}1+{IeλWP}Wx1(a)2Tr{IeλWP}Wx(b)2eλ/2,\displaystyle\begin{split}&\bigl{\|}W_{x}^{\prime}-\{I>e^{\lambda}W_{P}\}W_{x}^{\prime}\{I>e^{\lambda}W_{P}\}\bigr{\|}_{1}\\ =&\Big{\|}\{I>e^{\lambda}W_{P}\}W_{x}^{\prime}\{I\leq e^{\lambda}W_{P}\}\\ &\hskip 43.05542pt+\{I\leq e^{\lambda}W_{P}\}W_{x}^{\prime}\Big{\|}_{1}\\ \leq&\left\|\{I>e^{\lambda}W_{P}\}W_{x}^{\prime}\{I\leq e^{\lambda}W_{P}\}\right\|_{1}\\ &+\left\|\{I\leq e^{\lambda}W_{P}\}W_{x}^{\prime}\right\|_{1}\\ \stackrel{{\scriptstyle(a)}}{{\leq}}&2\sqrt{\operatorname{\mathrm{Tr}}\{I\leq e^{\lambda}W_{P}\}W_{x}^{\prime}}\stackrel{{\scriptstyle(b)}}{{\leq}}2e^{-\lambda^{\prime}/2},\end{split} (95)

where (a)(a) follows from Winter’s gentle measurement lemma [39]111Its detail is also available from [40, Exercise 6.8 and Solution to Exercise 6.8]., and (b)(b) follows from (94). Thus,

TrWx{Wx>eλ+λWP}=TrWxWx\displaystyle\operatorname{\mathrm{Tr}}W_{x}\{W_{x}>e^{\lambda+\lambda^{\prime}}W_{P}\}=\operatorname{\mathrm{Tr}}W_{x}W_{x}^{\prime}
\displaystyle\leq TrWx{I>eλWP}Wx{IeλWP}+2eλ/2\displaystyle\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}W_{x}^{\prime}\{I\geq e^{\lambda}W_{P}\}+2e^{-\lambda^{\prime}/2}
\displaystyle\leq TrWx{I>eλWP}+2eλ/2\displaystyle\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}+2e^{-\lambda^{\prime}/2}
=\displaystyle= TrWx{I>eλWP}+2δ.\displaystyle\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}+2\delta. (96)

Then, we have

TrR[P]{R[P]eλ+λS[P,xP(x)Wx]}=xP(x)TrWx{Wxeλ+λWP}=1xP(x)TrWx{Wx>eλ+λWP}1(xP(x)TrWx{I>eλWP})2δ=(xP(x)TrWx{IeλWP})2δ=Tr(W¯{IeλWP})2δ,\displaystyle\begin{split}&\operatorname{\mathrm{Tr}}R[P]\Big{\{}R[P]\leq e^{\lambda+\lambda^{\prime}}S\Big{[}P,\sum_{x}P(x)W_{x}\Big{]}\Big{\}}\\ &=\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}\{W_{x}\leq e^{\lambda+\lambda^{\prime}}W_{P}\}\\ &=1-\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}\{W_{x}>e^{\lambda+\lambda^{\prime}}W_{P}\}\\ &\geq 1-\Big{(}\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}\{I>e^{\lambda}W_{P}\}\Big{)}-2\delta\\ &=\Big{(}\sum_{x}P(x)\operatorname{\mathrm{Tr}}W_{x}\{I\leq e^{\lambda}W_{P}\}\Big{)}-2\delta\\ &=\operatorname{\mathrm{Tr}}\Big{(}\bar{W}\{I\leq e^{\lambda}W_{P}\}\Big{)}-2\delta,\end{split} (97)

which implies (88). ∎

Proof of Theorem 9.

We will show Theorem 9 by combining Lemmas 10 and 11 to Lemmas in Appendix B.1. The inequality (80) is shown as follows:

M(ρ,ϵ)(a)maxPDsϵδ1(R[P]S[P,WP])log1ϵδ1(b)maxPDHϵδ1δ2(R[P]S[P,WP])log1ϵδ1δ2(c)maxPHsϵδ1δ2(WP)log1ϵδ1δ2,\displaystyle\begin{split}&M(\rho,\epsilon)\\ &\stackrel{{\scriptstyle(a)}}{{\geq}}\max_{P}D_{s}^{\epsilon-\delta_{1}}(R[P]\|S[P,W_{P}])-\log\frac{1-\epsilon}{\delta_{1}}\\ &\stackrel{{\scriptstyle(b)}}{{\geq}}\max_{P}D_{H}^{\epsilon-\delta_{1}-\delta_{2}}(R[P]\|S[P,W_{P}])-\log\frac{1-\epsilon}{\delta_{1}\delta_{2}}\\ &\stackrel{{\scriptstyle(c)}}{{\geq}}\max_{P}H_{s}^{\epsilon-\delta_{1}-\delta_{2}}(W_{P})-\log\frac{1-\epsilon}{\delta_{1}\delta_{2}},\end{split} (98)

where steps (a)(a), (b)(b) and (c)(c) follow from (78) of Lemma 7, from the second equation of (79) of Lemma 8, and from (88) of Lemma 11, respectively. The inequality (81) is shown as follows:

M(ρ,ϵ)(a)maxPDHϵ(R[P]S[P,WP])(b)maxPDsϵ+δ1(R[P]S[P,WP])logδ1(c)maxPHsϵ+δ1+2δ2(WP)+log1δ1δ22.\displaystyle\begin{split}&M(\rho,\epsilon)\\ &\stackrel{{\scriptstyle(a)}}{{\leq}}\max_{P}D_{H}^{\epsilon}(R[P]\|S[P,W_{P}])\\ &\stackrel{{\scriptstyle(b)}}{{\leq}}\max_{P}D_{s}^{\epsilon+\delta_{1}}(R[P]\|S[P,W_{P}])-\log\delta_{1}\\ &\stackrel{{\scriptstyle(c)}}{{\leq}}\max_{P}H_{s}^{\epsilon+\delta_{1}+2\delta_{2}}(W_{P})+\log\frac{1}{\delta_{1}\delta_{2}^{2}}.\end{split} (99)

In the step (a)(a), we applied (77) of Lemma 6 to the case with ρ=WP\rho^{\prime}=W_{P}. The steps (b)(b) and (c)(c) follow from the second inequality in (79) of Lemma 8, and from (82) of Lemma 10, respectively. ∎

B.3 Single-shot bound for pure state covariant channel

Now, we consider the group covariant case, i.e., the case when the set of states {Wx}x𝒳\{W_{x}\}_{x\in\mathcal{X}} is given as {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G}, where GG is a finite group, ff is its unitary representation, and ρ\rho is a pure state. In this case, Theorem 9 is simplified as follows. The following theorem shows (17) and (18).

Theorem 12.

When {Wx}x𝒳\{W_{x}\}_{x\in\mathcal{X}} is given as the above defined set {f(g)ρf(g)}gG\{f(g)\rho f(g)^{\dagger}\}_{g\in G}, we have the following relations.

M(ρ,ϵ)\displaystyle M(\rho,\epsilon) Hsϵδ1δ2(avf[ρ])log1δ1δ2,\displaystyle\geq H_{s}^{\epsilon-\delta_{1}-\delta_{2}}(\operatorname{av}_{f}[\rho])-\log\frac{1}{\delta_{1}\delta_{2}}, (100)
M(ρ,ϵ)\displaystyle M(\rho,\epsilon) Hsϵ+δ1+2δ2(avf[ρ])+log1δ1δ22.\displaystyle\leq H_{s}^{\epsilon+\delta_{1}+2\delta_{2}}(\operatorname{av}_{f}[\rho])+\log\frac{1}{\delta_{1}\delta_{2}^{2}}. (101)
Proof.

Since Trρ{ρeλavf[ρ]}=Trf(g)ρf(g){f(g)ρf(g)eλavf[ρ]}\operatorname{\mathrm{Tr}}\rho\{\rho\leq e^{\lambda}\operatorname{av}_{f}[\rho]\}=\operatorname{\mathrm{Tr}}f(g)\rho f(g)^{\dagger}\{f(g)\rho f(g)^{\dagger}\leq e^{\lambda}\operatorname{av}_{f}[\rho]\}, we have

Dsϵ+δ1(R[P]S[P,avf[ρ]])=Dsϵ+δ1(R[PU]S[PU,avf[ρ]]),\displaystyle\begin{split}&D_{s}^{\epsilon+\delta_{1}}(R[P]\|S[P,\operatorname{av}_{f}[\rho]])\\ =&D_{s}^{\epsilon+\delta_{1}}(R[P_{U}]\|S[P_{U},\operatorname{av}_{f}[\rho]]),\end{split} (102)

where PUP_{U} is the uniform distribution on GG. In the following step (a)(a), we apply (77) of Lemma 6 to the case with ρ=avf[ρ]\rho^{\prime}=\operatorname{av}_{f}[\rho]:

M(ρ,ϵ)(a)maxPDHϵ(R[P]S[P,avf[ρ]])(b)maxPDsϵ+δ1(R[P]S[P,avf[ρ]])logδ1=(c)Dsϵ+δ1(R[PU]S[PU,avf[ρ]])logδ1(d)maxPHsϵ+δ1+2δ2(avf[ρ])+log1δ1δ22,\displaystyle\begin{split}&M(\rho,\epsilon)\\ &\stackrel{{\scriptstyle(a)}}{{\leq}}\max_{P}D_{H}^{\epsilon}(R[P]\|S[P,\operatorname{av}_{f}[\rho]])\\ &\stackrel{{\scriptstyle(b)}}{{\leq}}\max_{P}D_{s}^{\epsilon+\delta_{1}}(R[P]\|S[P,\operatorname{av}_{f}[\rho]])-\log\delta_{1}\\ &\stackrel{{\scriptstyle(c)}}{{=}}D_{s}^{\epsilon+\delta_{1}}(R[P_{U}]\|S[P_{U},\operatorname{av}_{f}[\rho]])-\log\delta_{1}\\ &\stackrel{{\scriptstyle(d)}}{{\leq}}\max_{P}H_{s}^{\epsilon+\delta_{1}+2\delta_{2}}(\operatorname{av}_{f}[\rho])+\log\frac{1}{\delta_{1}\delta_{2}^{2}},\end{split} (103)

where steps (b)(b), (c)(c) and (d)(d) follow from the second inequality of (79) of Lemma 8, from (102), and from (82) of Lemma 10, respectively.

By [7, Theorem 2], we have the following evaluation:

logM(ρ,ϵ)Dsϵδ(ρavf[ρ]))log1δ.\log M(\rho,\epsilon)\geq D_{s}^{\epsilon-\delta}(\rho\|\operatorname{av}_{f}[\rho]))-\log\frac{1}{\delta}. (104)

Applying to (104) the same discussion as the proof of (80), we obtain (101). ∎

Appendix C Derivations of (40) and (41)

We derive the formulas (40) and (41). For this aim, we recall the following formula when x/nx/n is fixed and nn\to\infty;

log(nx)=nh(xn)+12logn2πx(nx)+o(1)\displaystyle\log\tbinom{n}{x}=nh(\tfrac{x}{n})+\tfrac{1}{2}\log\tfrac{n}{2\pi x(n-x)}+o(1)
=\displaystyle= nh(xn)12logn12log(2πxn(1xn))+o(1),\displaystyle nh(\tfrac{x}{n})-\tfrac{1}{2}\log n-\tfrac{1}{2}\log\bigl{(}\tfrac{2\pi x}{n}(1-\tfrac{x}{n})\bigr{)}+o(1), (105)

Under the Type I limit, using (105), we have

log(mlnk)\displaystyle\log\binom{m-l}{n-k}
=\displaystyle= (nk)h(nξlnk)12log(nk)\displaystyle(n-k)h(\frac{n\xi-l}{n-k})-\frac{1}{2}\log(n-k)
12log(2πnξlnk(1nξlnk))+o(1)\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\frac{n\xi-l}{n-k}(1-\frac{n\xi-l}{n-k})\bigr{)}+o(1)
=\displaystyle= (nk)h(ξl/n1k/n)12log(nk)\displaystyle(n-k)h(\frac{\xi-l/n}{1-k/n})-\frac{1}{2}\log(n-k)
12log(2πξl/n1k/n(1ξl/n1k/n))+o(1)\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\frac{\xi-l/n}{1-k/n}(1-\frac{\xi-l/n}{1-k/n})\bigr{)}+o(1)
=\displaystyle= (nk)h((ξl/n)(1+k/n)+o(1/n))\displaystyle(n-k)h((\xi-l/n)(1+k/n)+o(1/n))
12log(nk)12log(2πξ(1ξ))+o(1)\displaystyle-\frac{1}{2}\log(n-k)-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}+o(1)
=\displaystyle= (nk)h(ξ+ξkln+o(1/n))12log(nk)\displaystyle(n-k)h(\xi+\frac{\xi k-l}{n}+o(1/n))-\frac{1}{2}\log(n-k)
12log(2πξ(1ξ))+o(1)\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}+o(1)
=\displaystyle= (nk)h(ξ)+(1kn)h(ξ)(ξkl)\displaystyle(n-k)h(\xi)+(1-\frac{k}{n})h^{\prime}(\xi)(\xi k-l)
12logn(1kn)12log(2π(ξ(1ξ))+o(1)\displaystyle-\frac{1}{2}\log n(1-\frac{k}{n})-\tfrac{1}{2}\log\bigl{(}2\pi(\xi(1-\xi)\bigr{)}+o(1)
=\displaystyle= (nk)h(ξ)12logn+12kn\displaystyle(n-k)h(\xi)-\frac{1}{2}\log n+\frac{1}{2}\cdot\frac{k}{n}
12log(2πξ(1ξ))+h(ξ)(ξkl)+o(1).\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}+h^{\prime}(\xi)(\xi k-l)+o(1). (106)

Therefore, we obtain (40) as follows.

log(mn)log(mlnk)\displaystyle\log\binom{m}{n}-\log\binom{m-l}{n-k}
=\displaystyle= nh(ξ)12logn12log(2πξ(1ξ))+o(1)\displaystyle nh(\xi)-\frac{1}{2}\log n-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}+o(1)
((nk)h(ξ)12logn12log(2πξ(1ξ))\displaystyle-\Big{(}(n-k)h(\xi)-\frac{1}{2}\log n-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}
+h(ξ)(ξkl)+o(1))\displaystyle+h^{\prime}(\xi)(\xi k-l)+o(1)\Big{)}
=\displaystyle= kh(ξ)h(ξ)(ξkl)+o(1).\displaystyle kh(\xi)-h^{\prime}(\xi)(\xi k-l)+o(1). (107)

In Type II limit, using (105), we obtain (41) as

log(mn)log(mlnk)\displaystyle\log\binom{m}{n}-\log\binom{m-l}{n-k}
=\displaystyle= nh(ξ)12logn12log(2πξ(1ξ))+o(1)\displaystyle nh(\xi)-\frac{1}{2}\log n-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}+o(1)
(n(β+δ)h(ββ+δ)12logn(β+δ)\displaystyle-\Big{(}n(\beta+\delta)h(\frac{\beta}{\beta+\delta})-\frac{1}{2}\log n(\beta+\delta)
12log(2πββ+δ(1ββ+δ))+o(1))\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\frac{\beta}{\beta+\delta}(1-\frac{\beta}{\beta+\delta})\bigr{)}+o(1)\Big{)}
=\displaystyle= n(h(ξ)(β+δ)h(ββ+δ))+12log(β+δ)\displaystyle n(h(\xi)-(\beta+\delta)h(\frac{\beta}{\beta+\delta}))+\frac{1}{2}\log(\beta+\delta)
12log(2πξ(1ξ))\displaystyle-\tfrac{1}{2}\log\bigl{(}2\pi\xi(1-\xi)\bigr{)}
+12log(2πββ+δ(1ββ+δ))+o(1).\displaystyle+\tfrac{1}{2}\log\bigl{(}2\pi\frac{\beta}{\beta+\delta}(1-\frac{\beta}{\beta+\delta})\bigr{)}+o(1). (108)

Appendix D Derivations of (57) and (66)

We derive the formulas (57) and (66). In Type II limit, using (105), we obtain (57) as

S(avπ[Ξn,m|k,l])\displaystyle S(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
=\displaystyle= x=0kp(x)(logdim𝒱(nx,x)logp(x))\displaystyle\sum_{x=0}^{k}p(x)\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)\bigr{)}
=(a)\displaystyle\stackrel{{\scriptstyle(a)}}{{=}} x=0kp(x)logdim𝒱(nx,x)+O(logk)\displaystyle\sum_{x=0}^{k}p(x)\log\dim\mathcal{V}_{(n-x,x)}+O(\log k)
=(b)\displaystyle\stackrel{{\scriptstyle(b)}}{{=}} x=0kp(x)logdim(nx)n2x+1nx+1+O(logk)\displaystyle\sum_{x=0}^{k}p(x)\log\dim\binom{n}{x}\frac{n-2x+1}{n-x+1}+O(\log k)
=(c)\displaystyle\stackrel{{\scriptstyle(c)}}{{=}} x=0kp(x)nh(xn)+O(logn)\displaystyle\sum_{x=0}^{k}p(x)nh(\frac{x}{n})+O(\log n)
=\displaystyle= x=0kp(x)nh(μ+xnμn)+O(logn)\displaystyle\sum_{x=0}^{k}p(x)nh(\mu+\frac{x-n\mu}{n})+O(\log n)
=\displaystyle= x=0kp(x)(nh(μ)+h(μ)(xnμ)\displaystyle\sum_{x=0}^{k}p(x)\Big{(}nh(\mu)+h^{\prime}(\mu)(x-n\mu)
+O((xnμn)2))+O(logn),\displaystyle+O((\frac{x-n\mu}{\sqrt{n}})^{2})\Big{)}+O(\log n), (109)

where each step is derived as follows. (a)(a) follows from the relation x=0kp(x)logp(x)logk-\sum_{x=0}^{k}p(x)\log p(x)\leq\log k, (b)(b) follows from the relation dim𝒱(nx,x)=(nx)n2x+1nx+1\dim\mathcal{V}_{(n-x,x)}=\binom{n}{x}\frac{n-2x+1}{n-x+1} in (29). (c)(c) follows from the relation (105).

Next, we show (66). In Type II limit, to evaluate the value Hsε(avπ[Ξn,m|k,l])H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]), we introduce the following quantities.

Hsε,1(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon,1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
:=\displaystyle:= max{λ|\displaystyle\max\Bigl{\{}\lambda\Big{|}
Pn,m,k,l[{xlogdim𝒱(nx,x)λ}]ϵ}.\displaystyle P_{n,m,k,l}[\{x\mid\log\dim\mathcal{V}_{(n-x,x)}\leq\lambda\}]\leq\epsilon\Bigr{\}}. (110)
Hsε,2(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon,2}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
:=\displaystyle:= max{λ\displaystyle\max\Bigl{\{}\lambda\mid
Pn,m,k,l[{x|logp(x|n,m,k,l)λ}]ϵ}.\displaystyle P_{n,m,k,l}[\{x\Big{|}-\log p(x\,|\,n,m,k,l)\leq\lambda\}]\leq\epsilon\Bigr{\}}. (111)

We have

Hsε1,1(avπ[Ξn,m|k,l])Hsε1(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon_{1},1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\leq H_{s}^{\varepsilon_{1}}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
\displaystyle\leq Hsε1+ε2,1(avπ[Ξn,m|k,l])+Hsε2,2(avπ[Ξn,m|k,l]).\displaystyle H_{s}^{\varepsilon_{1}+\varepsilon_{2},1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])+H_{s}^{\varepsilon_{2},2}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}]). (112)

Also, we have

0\displaystyle 0 Hsε,2(avπ[Ξn,m|k,l])\displaystyle\leq H_{s}^{\varepsilon,2}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
logεk=logϵ+logk.\displaystyle\leq-\log\frac{\varepsilon}{k}=-\log\epsilon+\log k. (113)

Thus,

Hsε1,1(avπ[Ξn,m|k,l])Hsε1(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon_{1},1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])\leq H_{s}^{\varepsilon_{1}}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
\displaystyle\leq Hsε1+ε2,1(avπ[Ξn,m|k,l])logϵ2+logk.\displaystyle H_{s}^{\varepsilon_{1}+\varepsilon_{2},1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])-\log\epsilon_{2}+\log k. (114)

Also, we denote by Φ\Phi and Φ~\tilde{\Phi} the cdfs of the standard Gaussian distribution and the Rayleigh distribution, respectively.

By [41, Lemma 4.7.1], we have under the limit nn\to\infty with fixed ratio t=x/nt=x/n that

log(nx)\displaystyle\log\binom{n}{x} =h(t)n+O(logn),\displaystyle=h(t)n+O(\log n),
h(t)\displaystyle h(t) :=tlogt(1t)log(1t).\displaystyle:=-t\log t-(1-t)\log(1-t).

Then, we can approximate logdim𝒱(nx,x)\log\dim\mathcal{V}_{(n-x,x)} to h(xn)n+O(logn)h(\frac{x}{n})n+O(\log n) when xx behaves linearly with respect to nn. Hence,

Hsε,1(avπ[Ξn,m|k,l])=logdim𝒱(nxn(ϵ),xn(ϵ))\displaystyle H_{s}^{\varepsilon,1}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])=\log\dim\mathcal{V}_{(n-x_{n}(\epsilon),x_{n}(\epsilon))}
=\displaystyle= h(xn(ϵ)n)n+O(logn)\displaystyle h(\frac{x_{n}(\epsilon)}{n})n+O(\log n)
=\displaystyle= nh(μ)+h(μ)(xn(ϵ)nμ)\displaystyle nh(\mu)+h^{\prime}(\mu)(x_{n}(\epsilon)-n\mu)
+O((xn(ϵ)nμn)2)+O(logn).\displaystyle+O((\frac{x_{n}(\epsilon)-n\mu}{\sqrt{n}})^{2})+O(\log n). (115)

Combining (114) and (D), we have (66), i.e.,

Hsε(avπ[Ξn,m|k,l])\displaystyle H_{s}^{\varepsilon}(\operatorname{av}_{\pi}[\Xi_{n,m|k,l}])
=\displaystyle= nh(μ)+h(μ)(xn(ϵ)nμ)\displaystyle nh(\mu)+h^{\prime}(\mu)(x_{n}(\epsilon)-n\mu)
+O((xn(ϵ)nμn)2)+O(logn).\displaystyle+O((\frac{x_{n}(\epsilon)-n\mu}{\sqrt{n}})^{2})+O(\log n). (116)

Appendix E von Neumann entropy S(avf[ψ])S(\operatorname{av}_{f}[\psi]) in special case αγ=0\alpha\gamma=0

The aim of this appendix is to show (59) by assuming (48) and αγ=0\alpha\gamma=0. In this appendix, we directly derive (59) without use of (57). For this aim, to address a more detailed analysis than Section 5, we consider Type II limit in the case αγ=0\alpha\gamma=0, corresponding to the case KL=0KL=0. We use Proposition 14, which discusses the tight exponential evaluation of the tail probability. Using it, we derive the asymptotic behavior of the von Neumann entropy S(avf[ψ])S(\operatorname{av}_{f}[\psi]).

Before taking the limit nn\to\infty, the parameters concerned are (n,m,k,l)(n,m,k,l) satisfying K:=kl=0K:=k-l=0 or L:=l=0L:=l=0. Let us focus on the former case k=lk=l, and also assume mnmm\leq n-m.

Then, we prepare the following proposition, which was shown in [26].

Proposition 13.

Under the assumption mnmm\leq n-m and k=lk=l, we have

p(x)=(nx)(nm)(lx)(mx)n2x+1nx+1(nlxml)\displaystyle p(x)=\frac{\binom{n}{x}}{\binom{n}{m}}\frac{\binom{l}{x}}{\binom{m}{x}}\frac{n-2x+1}{n-x+1}\binom{n-l-x}{m-l} (117)

for xlmx\leq l\wedge m, and p(x)=0p(x)=0 otherwise.

Below we assume l>0l>0, M:=ml>0M:=m-l>0 and N:=nmk+l>0N:=n-m-k+l>0 because of (48). In this section, we consider the Type II limit with γ=0\gamma=0, i.e., the limit nn\to\infty fixed ratios

γ=kln=0,α=ln>0,β=Mn>0,\displaystyle\gamma=\tfrac{k-l}{n}=0,\ \alpha=\tfrac{l}{n}>0,\ \beta=\tfrac{M}{n}>0,
δ=Nn>0,ξ=α+β=mn1/2.\displaystyle\delta=\tfrac{N}{n}>0,\ \xi=\alpha+\beta=\tfrac{m}{n}\leq 1/2.

The quantities are then given by

D=14α(1ξ),μ=114α(1ξ)2,ν=1+14α(1ξ)2,σ=αβδD.\displaystyle\begin{split}&D=1-4\alpha(1-\xi),\\ &\mu=\frac{1-\sqrt{1-4\alpha(1-\xi)}}{2},\\ &\nu=\frac{1+\sqrt{1-4\alpha(1-\xi)}}{2},\quad\sigma=\sqrt{\frac{\alpha\beta\delta}{D}}.\end{split} (118)

Note that the assumption 0<ξ1/20<\xi\leq 1/2 and 0<α<1/20<\alpha<1/2 yields D,σ>0D,\sigma>0 and

μ<12<ν.\displaystyle\mu<\tfrac{1}{2}<\nu. (119)

Also, by Theorem 4, the expectation and the variance asymptotically behave as

𝔼[X]=nμ+ϕ+o(1),\displaystyle\mathbb{E}[X]=n\mu+\phi+o(1), (120)
ϕ:=σ2μ12μ=1D(αβδD1D2),\displaystyle\phi:=\frac{\sigma^{2}-\mu}{1-2\mu}=\frac{1}{\sqrt{D}}(\frac{\alpha\beta\delta}{D}-\frac{1-\sqrt{D}}{2}),
limn𝕍[Xn]=limn𝔼[(Xnμn)2]=σ2=αβδD.\displaystyle\lim_{n\to\infty}\mathbb{V}\Bigl{[}\frac{X}{\sqrt{n}}\Bigr{]}=\lim_{n\to\infty}\mathbb{E}\Bigl{[}\Big{(}\frac{X-n\mu}{\sqrt{n}}\Big{)}^{2}\Bigr{]}=\sigma^{2}=\frac{\alpha\beta\delta}{D}. (121)

Then, we show the following proposition to address the asymptotic expansion of S(avf[ψ])S(\operatorname{av}_{f}[\psi]) by using the formulas (120) and (121). This proposition is the precise statement of (59).

Proposition 14.

In Type II limit with the condition αβδ>0\alpha\beta\delta>0, γ=0\gamma=0 and 0<ξ1/20<\xi\leq 1/2, we have

S(avf[ψ])=nC1+C2+C3ϕ+C4σ2+o(1),\displaystyle S(\operatorname{av}_{f}[\psi])=nC_{1}+C_{2}+C_{3}\phi+C_{4}\sigma^{2}+o(1), (122)

where

C1:=\displaystyle C_{1}:= h(ξ)+ξh(μξ)αh(μα)\displaystyle h(\xi)+\xi h\Bigl{(}\frac{\mu}{\xi}\Bigr{)}-\alpha h\Bigl{(}\frac{\mu}{\alpha}\Bigr{)}
(1αμ)h(β1αμ),\displaystyle-(1-\alpha-\mu)h\Bigl{(}\frac{\beta}{1-\alpha-\mu}\Bigr{)},
C2:=\displaystyle C_{2}:= 12logαβξ2(1ξ)(1β1αμ),\displaystyle\frac{1}{2}\log\frac{\alpha\beta}{\xi^{2}(1-\xi)}\Bigl{(}1-\frac{\beta}{1-\alpha-\mu}\Bigr{)},
C3:=\displaystyle C_{3}:= h(μξ)h(μα)+h(β1αμ)\displaystyle h^{\prime}\Bigl{(}\frac{\mu}{\xi}\Bigr{)}-h^{\prime}\Bigl{(}\frac{\mu}{\alpha}\Bigr{)}+h\Bigl{(}\frac{\beta}{1-\alpha-\mu}\Bigr{)}
β1αμh(β1αμ),\displaystyle-\frac{\beta}{1-\alpha-\mu}h^{\prime}\Bigl{(}\frac{\beta}{1-\alpha-\mu}\Bigr{)},
C4:=\displaystyle C_{4}:= 12ξh′′(μξ)12αh′′(μα)\displaystyle\frac{1}{2\xi}h^{\prime\prime}\Bigl{(}\frac{\mu}{\xi}\Bigr{)}-\frac{1}{2\alpha}h^{\prime\prime}\Bigl{(}\frac{\mu}{\alpha}\Bigr{)}
β22(1αμ)3h′′(β1αμ).\displaystyle-\frac{\beta^{2}}{2(1-\alpha-\mu)^{3}}h^{\prime\prime}\Bigl{(}\frac{\beta}{1-\alpha-\mu}\Bigr{)}.

In fact, the leading coefficient C1C_{1} equals h(μ)h(\mu) as follows.

Lemma 15.

We have C1=h(μ)=μlogμ(1μ)log(1μ)C_{1}=h(\mu)=-\mu\log\mu-(1-\mu)\log(1-\mu).

Proof.

A direct calculation (regarding μ\mu as an indeterminate) yields

C1h(μ)=(1μ)log(1μ)(1ξμ)(αμ)+μlogμ(ξμ)(1αμ)(1α)log(1αμ)(αμ)ξlog(1ξμ)(αμ)αlogα(1ξ)log(1ξ)+(ξα)log(ξα).\displaystyle\begin{split}&C_{1}-h(\mu)\\ =&(1-\mu)\log(1-\mu)(1-\xi-\mu)(\alpha-\mu)\\ &+\mu\log\mu(\xi-\mu)(1-\alpha-\mu)\\ &-(1-\alpha)\log(1-\alpha-\mu)(\alpha-\mu)\\ &-\xi\log(1-\xi-\mu)(\alpha-\mu)\\ &-\alpha\log\alpha-(1-\xi)\log(1-\xi)\\ &+(\xi-\alpha)\log(\xi-\alpha).\end{split} (123)

Since μ\mu is a solution of the cubic equation (1t)(1ξt)(αt)=t(ξt)(1αt)(1-t)(1-\xi-t)(\alpha-t)=t(\xi-t)(1-\alpha-t) for tt, the first line of (123) can be computed as

(1μ)log(1μ)(1ξμ)(αμ)+μlogμ(ξμ)(1αμ)=12log(1μ)(1ξμ)(αμ)+12logμ(ξμ)(1αμ).\displaystyle\begin{split}&(1-\mu)\log(1-\mu)(1-\xi-\mu)(\alpha-\mu)\\ &+\mu\log\mu(\xi-\mu)(1-\alpha-\mu)\\ =&\tfrac{1}{2}\log(1-\mu)(1-\xi-\mu)(\alpha-\mu)\\ &+\tfrac{1}{2}\log\mu(\xi-\mu)(1-\alpha-\mu).\end{split} (124)

Then we can proceed as

C1h(μ)=12logμ(1μ)+(12ξ)log(ξμ)(1ξμ)+(α12)log(αμ)(1αμ)αlogα(1ξ)log(1ξ)+(ξα)log(ξα).\displaystyle\begin{split}&C_{1}-h(\mu)\\ =&\tfrac{1}{2}\log\mu(1-\mu)+(\tfrac{1}{2}-\xi)\log(\xi-\mu)(1-\xi-\mu)\\ &+(\alpha-\tfrac{1}{2})\log(\alpha-\mu)(1-\alpha-\mu)-\alpha\log\alpha\\ &-(1-\xi)\log(1-\xi)+(\xi-\alpha)\log(\xi-\alpha).\end{split} (125)

Then, using μμ2=α(1ξ)\mu-\mu^{2}=\alpha(1-\xi), (αμ)(1αμ)=α(ξα)(\alpha-\mu)(1-\alpha-\mu)=\alpha(\xi-\alpha) and (ξμ)(1ξμ)=(1ξ)(ξα)(\xi-\mu)(1-\xi-\mu)=(1-\xi)(\xi-\alpha), we can check C1h(μ)=0C_{1}-h(\mu)=0. ∎

To show Proposition 14, we compute

logdim𝒱(nx,x)logp(x)\displaystyle\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)
=\displaystyle= log(nm)(mx)(lx)1(nlxml)1,\displaystyle\log\binom{n}{m}\binom{m}{x}\binom{l}{x}^{-1}\binom{n-l-x}{m-l}^{-1},

which follows from the combination of (29) and (117). If x/nx/n is fixed and nn\to\infty, then

log(nx)=nh(xn)+12logn2πx(nx)+o(1)\displaystyle\log\tbinom{n}{x}=nh(\tfrac{x}{n})+\tfrac{1}{2}\log\tfrac{n}{2\pi x(n-x)}+o(1)
=\displaystyle= nh(xn)12logn12log(2πxn(1xn))+o(1),\displaystyle nh(\tfrac{x}{n})-\tfrac{1}{2}\log n-\tfrac{1}{2}\log\bigl{(}\tfrac{2\pi x}{n}(1-\tfrac{x}{n})\bigr{)}+o(1),

where h(t):=tlogt(1t)log(1t)h(t):=-t\log t-(1-t)\log(1-t) is the binary entropy. Then, a direct computation yields

logdim𝒱(nx,x)logp(x)=nh(mn)+mh(xm)lh(xl)(nlx)h(mlnlx)12lognml(nlx)12logmn(1mn)xm(1xm)+12logxl(1xl)mlnlx(1mlnlx)+o(1).\displaystyle\begin{split}&\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)\\ =&nh(\tfrac{m}{n})+mh(\tfrac{x}{m})-lh(\tfrac{x}{l})-(n-l-x)h(\tfrac{m-l}{n-l-x})\\ &-\tfrac{1}{2}\log\tfrac{nm}{l(n-l-x)}-\tfrac{1}{2}\log\tfrac{m}{n}(1-\tfrac{m}{n})\tfrac{x}{m}(1-\tfrac{x}{m})\\ &+\tfrac{1}{2}\log\tfrac{x}{l}(1-\tfrac{x}{l})\tfrac{m-l}{n-l-x}(1-\tfrac{m-l}{n-l-x})+o(1).\end{split} (126)

Hereafter we use the variables and ratios

z:=xnμ,ξ=mn,α=ln,β=mln.\displaystyle z:=x-n\mu,\quad\xi=\tfrac{m}{n},\quad\alpha=\tfrac{l}{n},\quad\beta=\tfrac{m-l}{n}. (127)

We can then rewrite (126) as

logdim𝒱(nx,x)logp(x)\displaystyle\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)
=nh(ξ)+nξh(μξ+znξ)nαh(μα+zαn)n(1αμzn)h(β1αμzn)12logξα(1αμzn)12logξ(1ξ)(μα+znα)(1μαznα)+12log(μα+znα)(1μαznα)β1αμzn(1β1αμzn)\displaystyle\begin{split}=&nh(\xi)+n\xi h(\tfrac{\mu}{\xi}+\tfrac{z}{n\xi})-n\alpha h(\tfrac{\mu}{\alpha}+\tfrac{z}{\alpha n})\\ &-n(1-\alpha-\mu-\tfrac{z}{n})h(\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}})\\ &-\tfrac{1}{2}\log\tfrac{\xi}{\alpha(1-\alpha-\mu-\frac{z}{n})}\\ &-\tfrac{1}{2}\log\xi(1-\xi)(\tfrac{\mu}{\alpha}+\tfrac{z}{n\alpha})(1-\tfrac{\mu}{\alpha}-\tfrac{z}{n\alpha})\\ &+\tfrac{1}{2}\log(\tfrac{\mu}{\alpha}+\tfrac{z}{n\alpha})(1-\tfrac{\mu}{\alpha}-\tfrac{z}{n\alpha})\\ &\cdot\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}}(1-\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}})\end{split}
=nh(ξ)+nξh(μξ+znξ)nαh(μα+znα)n(1αμzn)h(β1αμzn)+12logαβξ2(1ξ)+12log(1β1αμzn).\displaystyle\begin{split}=&nh(\xi)+n\xi h(\tfrac{\mu}{\xi}+\tfrac{z}{n\xi})-n\alpha h(\tfrac{\mu}{\alpha}+\tfrac{z}{n\alpha})\\ &-n(1-\alpha-\mu-\tfrac{z}{n})h(\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}})\\ &+\tfrac{1}{2}\log\tfrac{\alpha\beta}{\xi^{2}(1-\xi)}+\tfrac{1}{2}\log(1-\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}}).\end{split} (128)
Lemma 16.

In the limit z0z\to 0 and nn\to\infty, we have

logdim𝒱(nx,x)logp(x)=v(z)+O(z3n2),v(z):=C1n+C2+C3z+C5zn+C4z2n+C6z2n2,\displaystyle\begin{split}&\log\dim\mathcal{V}_{(n-x,x)}-\log p(x)=v(z)+O\Bigl{(}\frac{z^{3}}{n^{2}}\Bigr{)},\\ &v(z):=C_{1}n+C_{2}+C_{3}z+C_{5}\frac{z}{n}+C_{4}\frac{z^{2}}{n}+C_{6}\frac{z^{2}}{n^{2}},\end{split} (129)

where the coefficients C1C_{1} through C4C_{4} are given as in Proposition 14, and the rest are given by

C5:=12β(1αμ)(1αμβ),C6:=14(1(1αμ)21(1αμβ)2).\displaystyle\begin{split}C_{5}&:=-\frac{1}{2}\frac{\beta}{(1-\alpha-\mu)(1-\alpha-\mu-\beta)},\\ C_{6}&:=\frac{1}{4}\Bigl{(}\frac{1}{(1-\alpha-\mu)^{2}}-\frac{1}{(1-\alpha-\mu-\beta)^{2}}\Bigr{)}.\end{split} (130)

The formula actually holds for any smooth function h(t)h(t).

Proof.

By the Taylor expansion of the binary entropy hh, for ζ=ξ,α\zeta=\xi,\alpha, we have

nξh(μζ+znζ)\displaystyle n\xi h(\tfrac{\mu}{\zeta}+\tfrac{z}{n\zeta})
=\displaystyle= ξh(μζ)n+zh(μζ)+12ζh′′(μζ)z2n+O(z3n2),\displaystyle\xi h(\tfrac{\mu}{\zeta})n+zh^{\prime}(\tfrac{\mu}{\zeta})+\tfrac{1}{2\zeta}h^{\prime\prime}(\tfrac{\mu}{\zeta})\tfrac{z^{2}}{n}+O(\tfrac{z^{3}}{n^{2}}),
n(1αμzn)h(β1αμzn)\displaystyle n(1-\alpha-\mu-\tfrac{z}{n})h(\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}})
=\displaystyle= (1αμzn)h(β1αμ)n\displaystyle(1-\alpha-\mu-\tfrac{z}{n})h(\tfrac{\beta}{1-\alpha-\mu})n
(h(β1αμ)β1αμh(β1αμ))z\displaystyle-\bigl{(}h(\tfrac{\beta}{1-\alpha-\mu})-\tfrac{\beta}{1-\alpha-\mu}h^{\prime}(\tfrac{\beta}{1-\alpha-\mu})\bigr{)}z
+12β2(1αμ)3h′′(β1αμ)z2n+O(z3n2).\displaystyle+\tfrac{1}{2}\tfrac{\beta^{2}}{(1-\alpha-\mu)^{3}}h^{\prime\prime}(\tfrac{\beta}{1-\alpha-\mu})\tfrac{z^{2}}{n}+O(\tfrac{z^{3}}{n^{2}}).

We also have

log(1β1αμzn)\displaystyle\log(1-\tfrac{\beta}{1-\alpha-\mu-\frac{z}{n}})
=\displaystyle= log(1β1αμ)β(1αμ)(1αμβ)zn\displaystyle\log(1-\tfrac{\beta}{1-\alpha-\mu})-\tfrac{\beta}{(1-\alpha-\mu)(1-\alpha-\mu-\beta)}\tfrac{z}{n}
+12(1(1αμ)21(1αμβ)2)z2n2+O(z3n3).\displaystyle+\tfrac{1}{2}\bigl{(}\tfrac{1}{(1-\alpha-\mu)^{2}}-\tfrac{1}{(1-\alpha-\mu-\beta)^{2}}\bigr{)}\tfrac{z^{2}}{n^{2}}+O(\tfrac{z^{3}}{n^{3}}).

Replacing the terms in (128) by these expressions, we obtain (130). ∎

Now we can show Proposition 14.

Proof of Proposition 14.

Let us denote pn(x):=p(x|n,m,k,l)p_{n}(x):=p(x\,|\,n,m,k,l). By Lemma 16, for ϵ>0\epsilon>0, there exists a constant C7C_{7} such that any z((μϵ)n,(μ+ϵ)n)z\in\bigl{(}(\mu-\epsilon)n,(\mu+\epsilon)n\bigr{)} satisfies

|(logdim𝒱(nx,x)logpn(x))v(z)|<C7|z|3n2.\displaystyle\left|\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}-\log p_{n}(x)\bigr{)}-v(z)\right|<C_{7}\frac{\left|z\right|^{3}}{n^{2}}. (131)

Since the values maxx(logdim𝒱(nx,x)logpn(x))\max_{x}(\log\dim\mathcal{V}_{(n-x,x)}-\log p_{n}(x)) and maxxv(xnμ)\max_{x}v(x-n\mu) behave linearly for nn and Proposition 5 guarantees that the probability x:|xnμ|>ϵpn(x)\sum_{x:\,\left|x-n\mu\right|>\epsilon}p_{n}(x) goes to zero exponentially, we have

0=\displaystyle 0= limn(x:|xnμ|>ϵ(logdim𝒱(nx,x)\displaystyle\lim_{n\to\infty}\Bigl{(}\sum_{x:\,\left|x-n\mu\right|>\epsilon}\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}
logpn(x))pn(x)),\displaystyle-\log p_{n}(x)\bigr{)}p_{n}(x)\Bigr{)}, (132)
0=\displaystyle 0= limn(x:|xnμ|>ϵv(xnμ)pn(x)).\displaystyle\lim_{n\to\infty}\Bigl{(}\sum_{x:\,\left|x-n\mu\right|>\epsilon}v(x-n\mu)p_{n}(x)\Bigr{)}. (133)

Since (120), (121) and (129) imply

0=\displaystyle 0= limn(𝔼[v(Xnμ)]\displaystyle\lim_{n\to\infty}\Bigl{(}\mathbb{E}[v(X-n\mu)]
(nC1+C2+C3ϕ+C4σ2)).\displaystyle-(nC_{1}+C_{2}+C_{3}\phi+C_{4}\sigma^{2})\Bigr{)}.

(133) implies

0=\displaystyle 0= limn((x:|xnμ|ϵv(xnμ)pn(x))\displaystyle\lim_{n\to\infty}\Bigl{(}\Bigl{(}\sum_{x:\,\left|x-n\mu\right|\leq\epsilon}v(x-n\mu)p_{n}(x)\Bigr{)}
(nC1+C2+C3ϕ+C4σ2)).\displaystyle-(nC_{1}+C_{2}+C_{3}\phi+C_{4}\sigma^{2})\Bigr{)}. (134)

Moreover, we have

x:|xnμ|ϵ((logdim𝒱(nx,x)logpn(x))v(xnμ))pn(x)(a)x:|xnμ|ϵC7|xnμ|3n2pn(x)C7𝔼[|Xnμn|3]1nn(b)0,\displaystyle\begin{split}&\sum_{x:\,\left|x-n\mu\right|\leq\epsilon}\Bigl{(}\bigl{(}\log\dim\mathcal{V}_{(n-x,x)}-\log p_{n}(x)\bigr{)}\\ &-v(x-n\mu)\Bigr{)}p_{n}(x)\\ \stackrel{{\scriptstyle(a)}}{{\leq}}&\sum_{x:\,\left|x-n\mu\right|\leq\epsilon}C_{7}\,\tfrac{\left|x-n\mu\right|^{3}}{n^{2}}\,p_{n}(x)\\ \leq&C_{7}\,\mathbb{E}\Big{[}\left|\tfrac{X-n\mu}{\sqrt{n}}\right|^{3}\Bigr{]}\,\frac{1}{\sqrt{n}}\xrightarrow[\ n\to\infty\ ]{(b)}0,\end{split} (135)

where (a)(a) and (b)(b) follow from (131) and (53), respectively. The combination of (132), (134), and (135) implies (122). ∎

Acknowledgement

The author was supported in part by the National Natural Science Foundation of China under Grant 62171212. He is grateful to Professor Akito Hora and Professor Shintaro Yanagida for helpful discussions on the topic of this paper. In particular, he is thankful for Professor Shintaro Yanagida to providing Figure 1 and helping his description of a part of this paper.

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