Hamiltonian simulation in the low energy subspace
Abstract
We study the problem of simulating the dynamics of spin systems when the initial state is supported on a subspace of low-energy of a Hamiltonian . This is a central problem in physics with vast applications in many-body systems and beyond, where the interesting physics takes place in the low-energy sector. We analyze error bounds induced by product formulas that approximate the evolution operator and show that these bounds depend on an effective low-energy norm of . We find improvements over the best previous complexities of product formulas that apply to the general case, and these improvements are more significant for long evolution times that scale with the system size and/or small approximation errors. To obtain these improvements, we prove exponentially-decaying upper bounds on the leakage to high-energy subspaces due to the product formula. Our results provide a path to a systematic study of Hamiltonian simulation at low energies, which will be required to push quantum simulation closer to reality.
I Introduction
The simulation of quantum systems is believed to be one of the most important applications of quantum computers Fey82 . Many quantum algorithms for simulating quantum dynamics exist Llo96 ; AT03 ; BAC07 ; WBH+10 ; BCC+14 ; BCC+15 ; LC17 ; LC19 ; Cam19 ; haah2018quantum , with applications in physics SOGKL02 ; JLP12 , quantum chemistry WBCH14 ; BBK+15 ; BBK++15 , and beyond CKS17 . While these algorithms are deemed efficient and run in time polynomial in factors such as system size, ongoing work has significantly improved the performance of such approaches. These improvements are important to explore the power of quantum computers and push quantum simulation closer to reality.
Leading Hamiltonian simulation methods are based on a handful of techniques. A main example is the product formula, which approximates the evolution of a Hamiltonian by short-time evolutions under the terms that compose Suz90 ; Suz91 ; BAC07 ; WBH+10 . Each such evolution can be decomposed as a sequence of two-qubit gates SOGKL02 to build up a quantum algorithm. Product formulas are attractive for various reasons: they are simple, intuitive, and their implementations may not require ancillary qubits, which contrasts other sophisticated methods as those in Refs. BCC+15 ; LC17 . Product formulas are also the basis of classical simulation algorithms including path-integral Monte Carlo NB98 .
Recent works provide refined error bounds of product formulas Som16 ; CS19 ; CST+19 ; CBC20 . These works regard various settings, such as when is a sum of spatially-local terms or when these terms satisfy Lie-algebraic properties. Nevertheless, while these improvements are important and necessary, a number of shortcomings remain. For example, the best-known complexities of product formulas scale poorly with the norm of or its terms, which can be very large or unbounded, even when the evolved quantum system does not explore high-energy states. These complexities may be improved under physically-relevant assumptions on energy scales. In fact, numerical simulations of few spin systems suggest that product formulas applied to low-energy states lead to much lower errors than that of the worst-case. Figure 1, for example, shows these errors for a 2x6 spin-1/2 Heisenberg model, suggesting that a complexity improvement is possible under a low-energy assumption on the initial state. Simulation results for related models present similar features. Nevertheless, our inability of simulating larger quantum systems with classical computers efficiently demands for analytical tools to actually demonstrate strict improvements on complexities of product formulas that apply generally.

To this end, we investigate the Hamiltonian simulation problem when the initial state is supported on a low-energy subspace. This is a central problem in physics that has vast applications, including the simulation of condensed matter systems for studying quantum phase transitions Sac11 , the simulation of quantum field theories JLP12 , the simulation of adiabatic quantum state preparation FGGS00 ; BKS10 , and more. We analyze the complexities of product formulas in this setting and show significant improvements with respect to the best known complexity bounds that apply to the general case.
II Results
II.1 Overview
Our main result is that, for a local Hamiltonian on spins with , the error induced by a -th order product formula is , where is a (short) time parameter and is an effective low-energy norm of . This norm depends on , which is an energy associated with the initial state, but also depends on and other parameters that define . The best known error bounds for product formulas that apply to the general case depend on the ’s CST+19 . (Throughout this paper, refers to the spectral norm.) Thus, an improvement in the complexity of product formulas is possible when , which can occur for sufficiently small values of and . Such values of appear in low-order product formulas (e.g., first order) or, for larger order, when the overall evolution time is sufficiently large and/or the desired approximation error is sufficiently small. We summarize some of the complexity improvements in Table I.
Order | Previous result | Low-energy simulation |
---|---|---|
To obtain our results, we introduce the notion of effective Hamiltonians that are basically the ’s restricted to act on a low-energy subspace. The relevant norms of these effective operators is bounded by . One could then proceed to simulate the evolution using a product formula that involves effective Hamiltonians and obtain an error bound that matches ours. A challenge is that these effective Hamiltonians are generally non-local and difficult to compute. Methods such as the local Schrieffer-Wolff transformation glazek1993renormalization ; bravyi2011schrieffer work only at the perturbative regime and numerical renormalization group methods for spin systems gu2008tensor ; evenbly2009algorithms have been studied only for a handful of models, while a general analytical treatment does not exist. Thus, efficient methods to simulate time evolution of effective Hamiltonians are lacking. We address this challenge by showing that evolutions under the effective Hamiltonians can be approximated by evolutions under the original ’s with a suitable choice of . This result is key in our construction and may find applications elsewhere.
Our main contributions are based on a number of technical lemmas and corollaries that are given in the Methods section and proven in detail in Supplementary Information.
II.2 Product formulas and effective operators
For a time-independent Hamiltonian , where each is Hermitian, the evolution operator for time is . Product formulas provide a way of approximating as a product of exponentials, each being a short-time evolution under some . For integer and , a -th order product formula is a unitary
(1) |
where each is proportional to and . The number of terms in the product may depend on and , and we assume , . (The more general case is analyzed in Supplementary Information.) We define and also assume . The -th order product formula satisfies , where BAC07 . One way to construct is to apply a recursion in Refs. Suz90 ; Suz91 . These are known as Trotter-Suzuki approximations and satisfy the above assumptions.
By breaking the time interval into steps of sufficiently small size , product formulas can approximate as . We will refer to as the Trotter number, and this number will define the complexity of product formulas that simulate within given accuracy. Note that the total number of terms in the product formula is actually , where is the number of terms in the product decomposition of each .
Known error bounds for product formulas that apply to the general case grow with and can be large. However, error bounds for approximating the evolved state may be better under the additional assumption that is supported on a low-energy subspace. We then analyze the case where the initial state satisfies , where is the projector into the subspace spanned by eigenstates of of energies (eigenvalues) at most . We assume . Our results will be especially useful when vanishes asymptotically, and will specify the low-energy subspace.
The notion of effective operators will be useful in our analysis. Given a Hermitian operator and , the corresponding effective operator is , which is also Hermitian. We also define the unitaries and by replacing the ’s by ’s in . Note that and . Then, using the known error bound for product formulas, we obtain . This error bound is a significant improvement over the general case if , which may occur when . However, product approximations of require that each term is an exponential of some , which is not the case in . We will address this issue and show that the improved error bound is indeed attained by for a suitable .
II.3 Local Hamiltonians
We are interested in simulating the time evolution of a local -spin system on a lattice. Each local interaction term in is of strength bounded by and involves, at most, spins. We do not assume that these interactions are only within neighboring spins but define the degree as the maximum number of local interaction terms that involve any spin. Next, we write , where each is a sum of local, commuting terms Bro41 and . Each in a product formula can be decomposed as products of evolutions under the local, commuting terms with no error.
These local Hamiltonians appear as important condensed matter systems, including gapped and critical spin chains, topologically ordered systems, and models with long-range interactions LSM61 ; AKLT04 ; Kit03 ; LMG65 . For example, for a spin chain with nearest neighbour interactions, and each may refer to interaction terms associated with even and odd bonds, respectively. In this case, . We will present our results for the case and in the main text, which further imply Bro41 . Nevertheless, explicit dependencies of our results in , , , and other parameters that specify can be found in Supplementary Note 4.
Table II summarizes the relevant parameters for the simulation of local Hamiltonians with product formulas.
Symbol | Meaning |
---|---|
Hamiltonian term strength | |
Low-energy parameter | |
() | Effective low-energy norm |
Number of spins | |
Total evolution time | |
Number of Trotter steps | |
, unit Trotter time | |
Total simulation error |
II.4 Main result
Theorem 1.
Let be a -local Hamiltonian as above, , , , and a -th order product formula as in Eq. (1). Then,
(2) |
where and the ’s are positive constants, .
The proof of Thm. 1 is in Supplementary Note 3 and we provide more details about it in the next section, but the basic idea is as follows. There are two contributions to Eq. (2) in our analysis. One comes from approximating the evolution operator with a product formula that involves the effective Hamiltonians and, as long as , this error is , as explained. The other comes from replacing such a product formula by the one with the actual Hamiltonians , i.e., . However, unlike , the evolution under each allows for leakage or transitions from the low-energy subspace to the subspace of energies higher than . In Supplementary Information, we use a result on energy distributions in Ref. AKL16 to show that this leakage can be bounded and decays exponentially with . Thus, this effective norm depends on and must also depend on , as the support on high-energy states can increase as increases, resulting in the linear contribution to in Thm. 1.
The factor in only becomes relevant when . This term appears in our analysis due to the requirement that both contributions to Eq. (2) discussed above are of the same order. Thus, as , we require to make the error due to leakage zero, which is unnecessary and unrealistic. This term plays a mild role when determining the final complexity of a product formula, as the goal will be to make as large as possible for a target approximation error. It may be possible that this term disappears in a more refined analysis.
Let be the Trotter number, i.e., the number of steps to approximate as . Since and if , the triangle inequality implies . Thus, for overall target error , it will suffice to satisfy . This condition and Thm. 1 can be used to determine as follows.
Each term of in Thm. 1 can be dominant depending on and . First, we consider the first two terms, and determine a condition in to satisfy , by omitting the factor. Then, we consider another term and determine a condition in to satisfy . These two conditions alone can be satisfied with a Trotter number
(3) |
Last, we reconsider the second term with , and we require . As the first two conditions are satisfied with a value for that is polynomial in and , this last condition only sets a correction to the first term in in Eq. (3) that is polylogarithmic in . Thus, the overall complexity of the product formula for local Hamiltonians is given by Eq. (3), where we need to replace by to account for the last correction. Note that the number of terms in each is constant under the assumptions and is proportional to the total number of exponentials in .
We give a general result on the complexity of product formulas that provides as a function of all parameters that specify in Thm. 2 of Supplementary Note 4.
II.5 The condition
The error bounds for product formulas used in Thm. 1 depend on the norm of the effective Hamiltonians . The assumption will then assure that , which is sufficient to demonstrate the complexity improvements in Eq. (3).
In general, can be met after a simple shifting , and the assumption seems irrelevant. However, this shifting could result in a value of (or ) that scales with some parameters such as the system size . In this case, the error bound in Thm. 1 would be comparable to that of the worst case (without the low-energy assumption) and would not provide an advantage.
Nevertheless, for many important spin Hamiltonians, the assumption is readily satisfied. The Heisenberg model of Fig. 1 is an example, where is a sum of terms like . More general (anisotropic) Heisenberg models as well as the so-called frustration-free Hamiltonians that are ubiquitous in many-body physics also satisfy the assumption BT09 ; BOO10 , where our results directly apply. For this class of models, the ground state energy is zero. This class contains interesting low-lying states in the subspace where, e.g., .
We provide more details on potential complexity improvements for the general case () at the end of Supplementary Note 3.
III Discussions
The best previous result for the complexity of product formulas (Trotter number) for local Hamiltonians of constant degree is , with CST+19 . Our result gives an improvement over this in various regimes. Note that, a general characteristic of our results is that they depend on , which is specified by the initial state. Here we assume that is a constant independent of other parameters that specify . The comparison for this case is in Table I. For , we obtain a strict improvement of order over the best-known result. For higher values of , the improvement appears for larger values of that may scale with , e.g., . In Supplementary Note 5, we provide a more detailed comparison between our results and the best previous results for product formulas as a function of and other parameters that specify .
A more recent method for Hamiltonian simulation uses a truncated Taylor series expansion of BCC+15 . Here, is the number of “segments”, and is approximated as . A main advantage of this method is that, unlike product formulas, its complexity in terms of is logarithmic, a major advantage if precise computations are needed. The complexity of this method for the low-energy subspace of can only be mildly improved. A small allows for a truncation value that is smaller than that for the general case BCC+15 . Nevertheless, the complexity of this method is dominated by , which depends on a certain 1-norm of that is independent of . Furthermore, quantum signal processing, an approach for Hamiltonian simulation also based on certain polynomial approximations of , was recently considered for simulation in the low-energy subspace low2017hamiltonian . While the low-energy constraint may also result in some mild (constant) improvement, the overall complexity of quantum signal processing also depends on . For local Hamiltonians where , and for constant , the overall complexity of these methods is , where we disregarded logarithmic factors in , , and . Our results on product formulas provide an improvement over these methods in various regimes, e.g., when is constant.
The obtained complexities are an improvement as long as the energy of the initial state is sufficiently small. As we discussed, the assumption was used and, while our results readily apply to a large class of spin models, it may be in conflict with ensuring small values of in some cases. It will be important to understand this in more detail (see Supplementary Note 3), which may be related to the fact that, for general Hamiltonians (), an improvement in the low-energy simulation could imply an improvement in the high-energy simulation by considering instead. Indeed, certain spin models possess a symmetry that connects the high-energy and low-energy subspaces via a simple transformation. Whether such “high-energy” simulation improvement is possible or not remains open. Additionally, known complexities of product formulas are polynomial in . This is an issue if precise computations are required as in the case of quantum field theories or QED. Whether this complexity can be improved in terms of precision as in Refs. BCC+14 ; BCC+15 ; LC17 ; low2019well is also open.
Our work is an initial attempt to this problem. We expect to motivate further studies on improved Hamiltonian simulation methods in this setting by refining our analyses, assuming other structures such as interactions that are geometrically local, or improving other simulation approaches.
IV Methods
IV.1 Leakage to high-energy states
A key ingredient for Thm. 1 is a property of local spin systems, where the leakage to high-energy states due to the evolution under any can be bounded. Let be the projector into the subspace spanned by eigenstates of energies greater than . Then, for a state that satisfies , we consider a question on the support of on states with energies greater than . This question arises naturally in Hamiltonian complexity and beyond, and Lemma 1 below may be of independent interest. A generalization of this lemma will allow one to address the Hamiltonian simulation problem in the low-energy subspace beyond spin systems.
Lemma 1 (Leakage to high energies).
Let be a -local Hamiltonian of constant degree as above, , and . Then, and ,
(4) |
where and are positive constants.
The proof is in Supplementary Note 1. It follows from a result in Ref. AKL16 on the action of a local interaction term on a quantum state of low-energy, in combination with a series expansion of . While the local interaction term could generate support on arbitrarily high-energy states, that support is suppressed by a factor that decays exponentially in .
Another key ingredient for proving Thm. 1 is the ability to replace evolutions under the ’s in a product formula by those under their effective low-energy versions (and vice versa) with bounded error. This is addressed by Lemma 2 below, which is a consequence of Lemma 1. The proof is in Supplementary Note 2, where we also provide tighter bounds that depend on .
Lemma 2.
Let be a -local Hamiltonian of constant degree as above, , and . Then, and ,
(5) |
and
(6) |
where and are positive constants.
IV.2 Relevance to the main result
The consequences of these lemmas for Hamiltonian simulation are many-fold and we only sketch those that are relevant for Thm. 1. Consider any product formula of the form . Then, there exists a sequence of energies such that the action of on the initial low-energy state can be well approximated by that of on the same state. Furthermore, each in can be replaced by and later by within the same error order, as long as .
In particular, for sufficiently small evolution times and , the resulting effective norm satisfies for local Hamiltonians. This is formalized by several corollaries in Supplementary Note 3. Starting from , we can construct the product formula . Lemmas 1 and 2 imply that both product formulas produce approximately the same state when acting on , for a suitable choice of as in Thm. 1. If is a product formula approximation of , it follows that .
V Data Availability statement
All relevant data used for Fig. 1 are available from the authors.
VI Code Availability statement
The code for the simulation results in Fig. 1 is available from the authors.
VII Acknowledgements
We acknowledge support from the LDRD program at LANL, the U.S. Department of Energy, Office of Science, High Energy Physics and Office of Advanced Scientific Computing Research, under the QuantISED grant KA2401032 and Accelerated Research in Quantum Computing (ARQC). Los Alamos National Laboratory is managed by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy under Contract No. 89233218CNA000001.
This work is also supported by the Quantum Science Center (QSC), a National Quantum Information Science Research Center of the U.S. Department of Energy (DOE).
VIII Author Contributions
All authors contributed equally to this work.
IX Competing Interests
The authors declare no competing interests.
X Figure legends
Figure 1: Worst case vs. low-energy Trotter errors: Errors induced by product formulas for a 2x6
Heisenberg spin-1/2 model. The Hamiltonian is , where , , and are the Pauli operators for the th spin, and , where and are the interaction terms represented by blue and red bonds, respectively.
(a) The evolution operator for time , , is approximated by the first order product formula . The plot shows the largest approximation errors when acting on various low-energy subspaces associated with increasing energies, labeled by , and in the worst case. (b) Similar results for when the evolution operator is approximated by the second order product formula .
Table I: Improvements of low-energy simulation: Comparison between the best-known complexity CST+19 and the complexity of low-energy simulation for -th order product formulas. Results show the Trotter number for constant and local Hamiltonians on spins with constant degree and strength bounded by , and . is the approximation error. The notation hides polylogarithmic factors in .
Table II: Notation: The parameters of the Hamiltonian (local and constant-degree) and product formula simulation.
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Supplementary Information
In the following, we let be a -local Hamiltonian of spins, where each interaction term involves at most spins. We will write , where each is a sum of at most -local and commuting interaction terms. The ’s may be obtained via graph coloring [32], where a graph can be constructed with vertices that are labeled according to the subset of spins in each interaction term and the edges connect vertices associated with the same spins, but more efficient constructions may be possible. Indeed, in many interesting examples such as spins on the square lattice, is already given in the desired form. The degree of , i.e., the highest number of interaction terms that act non-trivially on any spin, is . The strength of each local interaction term is bounded by , hence and . Throughout this paper, refers to the spectral norm (largest eigenvalue for positive semidefinite and Hermitian operators). The total number of local terms in is then upper bounded by and . We will assume that contains exactly terms and thus with no loss of generality (e.g., we can add or subtract trivial terms to and each spin appears, at least, in one term). Furthermore, following the coloring procedure described above, we may assume [32].
For , the operators and are the projectors into the subspaces spanned by the eigenstates of with energies (eigenvalues) at most and larger than , respectively. For a given , the -effective (or simply effective) Hamiltonians are then defined as , , and . We assume , and thus , .
Supplementary Notes
Supplementary Note 1: Proof of Lemma 1
We employ Theorem 2.1 in Ref. [37] that, for an operator , states
(7) |
Here, , where is an upper bound on the sum of the strengths of the interactions associated with any spin. In our case, we take and . If is the subset of local interaction terms in that do not commute with , is the sum of the strengths of the terms in . For any , we note that is a sum of, at most, terms, each of strength bounded by and containing, at most, spins. For each such term, , and we obtain
(8) | ||||
(9) |
We now consider the Taylor series expansion of the exponential,
(10) |
The triangle inequality and Eq. (9) imply
(11) | ||||
(12) | ||||
(13) | ||||
(14) |
where .
In particular, when and are constants, we have , and Eq. (14) implies
(15) |
where and are also positive constants.
∎
Supplementary Note 2: Proof of Lemma 2
To prove the first result, we will use the identity
(16) |
We note that, from the assumptions, and for all . Then, we can express as
We can simplify this expression since . Observing that , , and using standard properties of the spectral norm, we obtain
(17) | ||||
(18) | ||||
(19) |
where , , and Eq. (18) follows from Lemma 1. Note that
(20) | ||||
(21) | ||||
(22) | ||||
(23) |
We obtain
(24) |
To simplify our analysis, we will use a looser upper bound in the statement of the Lemma 2, which follows directly from Eq. (24) and :
(25) |
In particular, when and are constants, we have , and Eq. (25) implies
(26) |
where and are also constants.
To prove the second result, we use Lemma 1 together with Eq. (25) and standard properties of the spectral norm, and obtain
(27) | ||||
(28) | ||||
(29) | ||||
(30) | ||||
(31) | ||||
(32) | ||||
(33) |
In particular, when and are constants, we have , and Eq. (33) implies
(34) |
where and are also positive constants.
∎
Supplementary Note 3: Approximation errors for product formulas
We consider generic product formulas of terms,
(35) | ||||
(36) |
where , , and . We also define
(37) | ||||
(38) |
where , and for all . Using Lemmas 1 and 2, we now prove a number of results (corollaries) on the approximation errors for these product formulas that will be required for the proof of Thm. 1. First, we will prove that produces approximately the same state as when the initial state is supported on the low-energy subspace and for a suitable choice of . Next, we will show that the approximation error from replacing by is of the same order as that of the first approximation for a suitable choice of and effective Hamiltonians. A similar result is obtained if we further replace by . Combining these results we show that the state produced by approximates that produced by for a suitable choice of .
Corollary 1.
Let , , , and . Then, if satisfies and ,
(39) |
Proof.
We use the identity
(40) |
Since , we can use the triangle inequality and Lemma 1 to obtain
(41) | ||||
(42) | ||||
(43) | ||||
(44) |
∎
Corollary 2.
Let , , , and . Then, if satisfies , , and ,
(45) |
Proof.
We use the identity
(46) |
Since , we can use the triangle inequality and Eq. (25) in Lemma 2 to obtain
(47) | ||||
(48) | ||||
(49) | ||||
(50) |
∎
Corollary 3.
Let , , , and . Then, if satisfies , , and ,
(51) |
Proof.
We use the identity
(52) |
Since , we can use the triangle inequality and Eq. (33) in Lemma 2 to obtain
(53) | ||||
(54) | ||||
(55) | ||||
(56) |
∎
Corollary 4.
Let , , , , and . Then, if ,
(57) |
Proof.
To prove the previous corollaries, we often used , but we note that a different upper bound such as may provide improved results (e.g., a smaller value of in Cor. 4), especially if . However, while this other upper bound could be used to improve the constants hidden by the notation in the complexity of our method, we were unable to improve its asymptotic scaling from using .
Proof of Thm. 1
For some , let be the evolution operator with the effective Hamiltonian and , , be the corresponding -th order product formula obtained by replacing in of Eq. 1. Since the condition implies , we obtain
(63) | ||||
(64) |
where is an upper bound of the error induced by product formulas using effective operators and is a constant [23]. This error bound grows with . It does not exploit any structure of the effective Hamiltonians so it may be possible to improve it under further constraints.
Additionally,
(65) |
and then
(66) |
The other contribution to the error is due to Cor. 4, which can be turned around to obtain a bound on the error that depends on . Let , , and be the number of terms in the product . Then, Cor. 4 implies
(67) |
and
(68) |
This error bound decreases when increases. It is now valid for all but can be irrelevant (larger than 1) when, for example, . We assume that our product formula is such that for a constant and let . Then
(69) |
Thus, for any , the triangle inequality implies .
For given , we can search for that minimizes the overall error bound. Let that be . The global minimum for the error is then and, for all , it satisfies
(70) |
Then, we can fix any value of and obtain a bound for the overall error from computing . In particular, we choose
(71) |
and, assuming , , we obtain
(72) | ||||
(73) | ||||
(74) |
The constraint in is to avoid errors larger than 1: it is sufficient for and for . Therefore, if ,
(75) | ||||
(76) | ||||
(77) | ||||
(78) | ||||
(79) |
where was determined in Eq. (71) and is a constant. While our choice of in Eq. (71) does not correspond to the global minimum of the overall error, an exact calculation show that it is not far from and provides the same asymptotic complexity for our method. Nevertheless, if one is interested in optimizing some constants hidden by the notation in the overall complexity, using instead will be a better choice. Also note that, in general, we can additionally require for all ; this condition is not satisfied by Eq. (71). But since our results will be particularly useful when , we do not expect any improvement from this additional requirement and only consider Eq. (71) in the following.
An upper bound for can be given in terms of three factors , , and , which can be easily computed from the parameters that define as . This is the expression provided in Thm. 1 in the main text. Using the properties , , and , the factors satisfy
(80) | ||||
(81) | ||||
(82) |
When , , , and are constants, we obtain , and .
∎
Note that the error approaches zero as but not as , as in the case of -th order product formulas. This is due to the appearance of in . This logarithmic factor is the result of our requirement that the “leakage” vanishes as , implying . At the same time, when , no evolution occurs and one would have expected that in this limit. This suggests that it might be possible to obtain a tighter expression for than that in Eq. (71), one that does not diverge in the limit , but leave this as an open problem. Nevertheless, our purpose is to make as large as possible in the product formula and the limit is not of our concern. To this end, our previous analyses will suffice.
For the special case of Trotter-Suzuki product formulas, the constant that appears in has been previously studied in Ref. [4]. In this case, the number of terms satisfies and , for some constant . Thus, we can take and for .
The assumption implies and thus the error bound in Eq. (64). In general, can be met after a simple shifting , and the assumption seems irrelevant. However, this shifting could result on a value of and that scales, for example, with or . If this is the case, the error bound of Eq. (64) could be comparable to that in the worst case (without the low-energy assumption), and would not result in a complexity improvement.
To clarify this point further, consider a local spin Hamiltonian where and let be the relevant low-energy parameter; that is, the initial state is supported on the subspace associated with eigenvalues of in the range , where is the lowest eigenvalue. The previous analyses can be extended to this general case as follows. First, we let be the projector into the subspace associated with the eigenvalues of in the range , where is chosen as in Thm. 1. (As before, the effective Hamiltonians are . ) We also define
(83) | ||||
(84) |
which is an effective low-energy norm (obtained by a minimization that eliminates a constant term in ), and and are the largest and smallest eigenvalues of , respectively. Then, the error bound induced by product formulas in the more general case where is
(85) |
where . This is because this error bound depends on nested commutators of the ’s [23], and shifting by a constant does not change the commutators. This error has to be compared with Eq. (64). A complexity improvement with respect to the worst case (without the low-energy assumption) might follow if ; e.g., when is constant.
Characterizing those Hamiltonians for which occurs is an interesting but open problem. Nevertheless, for many important local Hamiltonians (e.g., frustration-free Hamiltonians), the condition is readily satisfied. In these examples, (or ) can be much less than , resulting in a complexity improvement. The Heisenberg model of Fig. 1 is an example where our results apply.
Supplementary Note 4: Complexity of product formulas for local Hamiltonians
We now determine the complexity of product formulas, which is the total number of exponentials of the ’s to approximate the evolution operator within given precision . We give an explicit dependence of this complexity in terms of the relevant parameters that specify . If a quantum algorithm is constructed to implement such product formula, then our result will determine the complexity of the quantum algorithm (number of two-qubit gates) from multiplying it by the complexity of implementing the exponential of . The latter is linear in following the results in Ref. [12].
Theorem 2.
Let , , , a -local Hamiltonian as above, , and a -th order product formula as in Eq. 1. Then, there exists
(86) |
such that
(87) |
The notation hides a polylogarithmic factor in .
Proof.
Let be the Trotter number, i.e., the number of “segments” in the product formula, each approximating the evolution for short time . We assume and for simplicity, and the result for simply follows from replacing . Note that and . We use the identity
(88) | ||||
(89) |
Using the triangle inequality and for unitary operators, we obtain
(90) | ||||
(91) |
where and is a constant that can be determined from the error bounds of product formulas – see Thm. 1, Eq. (79). Thus, for overall error bounded by , it suffices to choose or, equivalently,
(92) |
To set some conditions in , in addition to , we note that each of the terms in Eq. (71) that define the effective norm can be dominant depending on , , and other parameters. Then, to obtain the overall complexity of product formulas, we will analyze four different cases as follows. In the first case, we assume that is the dominant term in , and we require
(93) |
which is satisfied as long as
(94) |
In the second case, we assume that is the dominant term in Eq. (71) and impose
(95) |
which implies
(96) |
In the third case, we assume that is the dominant term in Eq. (71) and impose
(97) |
which implies
(98) |
In the fourth case, we assume that is the dominant term in Eq. (71) and impose
(99) |
under the assumption . Equivalently, if and defining the function , , we impose
(100) |
where
(101) |
To set a fourth condition in we could then compute from the inputs of the problem and find a range of values of for which Eq. (100) is satisfied. We can also obtain such a range analytically as follows. The function increases from , attains its maximum at (hence for all ), and then decreases to . Additionally, for all . In particular, if then Eq. (100) is readily satisfied for all and no additional condition in will be required in this case (this happens, for example, for sufficiently small values of ). More generally, for a given , we can solve for . If there are two solutions for , we consider the smaller one () and the relevant range for to satisfy Eq. (100) is . It will then suffice to impose that belongs to a range , where . To this end, we define and, under the assumption , we have . Additionally,
(102) | ||||
(103) | ||||
(104) | ||||
(106) | ||||
(107) |
where we used and for . This is the condition of Eq. (100). Then, for the fourth condition in , we impose or, equivalently,
(108) | ||||
(109) |
Except for a mild polylogarithmic correction in – the third factor – this condition is similar to the first and third ones.
Then, if and additionally satisfies Eqs. (94), (96), (98), and (109), we obtain the desired condition of Eq. (92). The product formulas under consideration [Eq. 1] are such that and , and we consider the case where is a constant. The conditions in allow us to obtain a sufficient condition for the Trotter number as follows:
(110) | ||||
(111) |
where the notation hides a polylogarithmic factor in coming from Eq. (109). For the case when is , , , and hence and , and considering the asymptotic limit, we obtain
(112) |
∎
Supplementary Note 5: Comparison with known results on product formulas
We compare our result on the complexity of product formulas with those in Ref. [23] that are the state of the art. When no assumption is made for the initial state, the Trotter number stated in Ref. [23] for -local Hamiltonians is
(113) |
Here, is the 1-norm of , given by in our case, and is the so-called induced 1-norm of . The latter is defined as follows. We write , where each includes the -local interaction terms of qubits labeled as in . Then,
(114) |
That is, we fix certain qubit and consider all the interaction terms that contain that qubit. For a degree Hamiltonian with -local interaction terms (not necessarily geometrically local), each of strength at most , . Furthermore, can be upper bounded as . As a result, for a -local Hamiltonian as above, the best known upper bound for the Trotter number is
(115) |
To compare our main result with Eq. (115), we express Eq. (111) in terms of and . We recall that the total number of local terms in is , , and we assume that , , and for the -th order product formula. Then, in the asymptotic limit,
(116) |
The results for various values of and are in Table 3.
Comparison of asymptotic complexities (Trotter number) as a function of | ||
---|---|---|
Order | Previous result () | Low-energy simulation () |
) | ||
) |
By setting a constraint on the initial state, our low-energy simulation result provides an advantage in certain regimes where may grow with . In the following, we will assume that, e.g., and fix the value of , to simplify the expressions. Under these assumptions, for and , the terms in both columns of Table 3 are of order . For and , the terms in both columns of Table 3 are of order . For and the terms in both columns of Table 3 are of order . For general and , both complexities are comparable and of order .
When is fixed and grows, our complexities may be worse than those obtained in Ref. [23]. One reason for this is because the effective norms may grow large in this case and the error bound that we use for product formulas using effective operators do not exploit any structure such as the locality of interactions. Better error bounds may be possible in this case, resulting in improved complexities. However, even if is large, our results regain an advantage at certain values of , in particular if we scale with . Doing so will set a bound on the effective norm so that the second term in our complexities stops dominating.
The special case where , , and is also a constant independent of other parameters that specify can be directly obtained from Table 2 and is given in Table 1 in the main text.