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Hamiltonian simulation for low-energy states with optimal time dependence (2404.03644v2)

Published 4 Apr 2024 in quant-ph

Abstract: We consider the task of simulating time evolution under a Hamiltonian $H$ within its low-energy subspace. Assuming access to a block-encoding of $H'=(H-E)/\lambda$ for some $E \in \mathbb R$, the goal is to implement an $\epsilon$-approximation to $e{-itH}$ when the initial state is confined to the subspace corresponding to eigenvalues $[-1, -1+\Delta/\lambda]$ of $H'$. We present a quantum algorithm that uses $O(t\sqrt{\lambda\Gamma} + \sqrt{\lambda/\Gamma}\log(1/\epsilon))$ queries to the block-encoding for any $\Gamma$ such that $\Delta \leq \Gamma \leq \lambda$. When $\log(1/\epsilon) = o(t\lambda)$ and $\Delta/\lambda = o(1)$, this result improves over generic methods with query complexity $\Omega(t\lambda)$. Our quantum algorithm leverages spectral gap amplification and the quantum singular value transform. Using standard access models for $H$, we show that the ability to efficiently block-encode $H'$ is equivalent to $H$ being what we refer to as a "gap-amplifiable" Hamiltonian. This includes physically relevant examples such as frustration-free systems, and it encompasses all previously considered settings of low-energy simulation algorithms. We also provide lower bounds for low-energy simulation. In the worst case, we show that the low-energy condition cannot be used to improve the runtime of Hamiltonian simulation. For gap-amplifiable Hamiltonians, we prove that our algorithm is tight in the query model with respect to $t$, $\Delta$, and $\lambda$. In the practically relevant regime where $\log (1/\epsilon) = o(t\Delta)$ and $\Delta/\lambda = o(1)$, we also prove a matching lower bound in gate complexity (up to log factors). To establish the query lower bounds, we consider $\mathrm{PARITY}\circ\mathrm{OR}$ and degree bounds on trigonometric polynomials. To establish the lower bound on gate complexity, we use a circuit-to-Hamiltonian reduction acting on a low-energy state.

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References (49)
  1. Fast-forwarding of hamiltonians and exponentially precise measurements. Nature communications, 8(1):1572, 2017.
  2. Simulated quantum computation of molecular energies. Science, 309(5741):1704–1707, 2005.
  3. Adiabatic quantum computation. Reviews of Modern Physics, 90(1):015002, 2018.
  4. Linear combination of hamiltonian simulation for nonunitary dynamics with optimal state preparation cost. Phys. Rev. Lett., 131:150603, Oct 2023.
  5. Ryan Babbush et al. Exponentially more precise quantum simulation of fermions i: Quantum chemistry in second quantization. New Journal of Physics, 18(3):033032, (2016).
  6. Ryan Babbush et al. Exponentially more precise quantum simulation of fermions in the configuration interaction representation. Quantum Science and Technology, 3(1):015006, (2017).
  7. Efficient quantum algorithms for simulating sparse hamiltonians. Communications in Mathematical Physics, 270(2):359–371, (2007).
  8. Tight bounds on quantum searching. Fortschritte der Physik: Progress of Physics, 46(4-5):493–505, 1998.
  9. Black-box hamiltonian simulation and unitary implementation. Quantum Information & Computation, 12(1-2):29–62, (2012).
  10. Exponential improvement in precision for simulating sparse hamiltonians. In Proceedings of the forty-sixth annual ACM symposium on Theory of computing, pages 283–292, 2014.
  11. Simulating hamiltonian dynamics with a truncated taylor series. Physical review letters, 114(9):090502, (2015).
  12. Polynomials and polynomial inequalities, volume 161. Springer Science & Business Media, 2012.
  13. Eigenpath traversal by phase randomization. Quantum Inf. Comput., 9(9&10):833–855, 2009.
  14. Complexity of stoquastic frustration-free hamiltonians. Siam journal on computing, 39(4):1462–1485, (2010).
  15. Earl Campbell. A random compiler for fast hamiltonian simulation. Physical review letters, 123:070503, (2019).
  16. Spatial search by quantum walk. Physical Review A, 70(2):022314, 2004.
  17. Ground-state spaces of frustration-free hamiltonians. Journal of mathematical physics, 53(10), 2012.
  18. Quantum algorithm for systems of linear equations with exponentially improved dependence on precision. SIAM Journal on Computing, 46(6):1920–1950, (2017).
  19. Theory of trotter error with commutator scaling. Physical Review X, 11(1):011020, (2021).
  20. Solving frustration-free spin systems. Physical review letters, 105:060504, (2010).
  21. Richard P Feynman. Simulating physics with computers. International Journal of Theoretical Physics, 21(6–7):467–488, 1982.
  22. Quantum computation by adiabatic evolution. Preprint at https://arxiv.org/abs/quant-ph/0001106, 2000.
  23. András Gilyén. personal communication, 2023.
  24. Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, pages 193–204, 2019.
  25. Fast-forwarding quantum evolution. Quantum, 5:577, November 2021.
  26. A theory of digital quantum simulations in the low-energy subspace. Preprint at https://arxiv.org/abs/2312.08867, 2023.
  27. Quantum algorithm for simulating real time evolution of lattice hamiltonians. In 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), pages 350–360. IEEE, (2018).
  28. Negative weights make adversaries stronger. In Proceedings of the thirty-ninth annual ACM symposium on Theory of computing, pages 526–535, 2007.
  29. Quantum algorithms from fluctuation theorems: Thermal-state preparation. Quantum, 6:825, 2022.
  30. Quantum algorithms for quantum field theories. Science, 336:1130, (2012).
  31. Stephen P. Jordan. Fast quantum computation at arbitrarily low energy. Phys. Rev. A, 95:032305, Mar 2017.
  32. Hamiltonian simulation by uniform spectral amplification. Preprint at https://arxiv.org/abs/1707.05391, 2017.
  33. Optimal hamiltonian simulation by quantum signal processing. Physical review letters, 118(1):010501, (2017).
  34. Hamiltonian simulation by qubitization. Quantum, 3:163, 2019.
  35. Seth Lloyd. Universal quantum simulators. Science, 273(5278):1073–1078, (1996).
  36. Guang Hao Low. Hamiltonian simulation with nearly optimal dependence on spectral norm. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019, page 491–502, New York, NY, USA, 2019.
  37. Towards quantum chemistry on a quantum computer. Nature chemistry, 2(2):106–111, 2010.
  38. Tight bounds for quantum phase estimation and related problems. Preprint at https://arxiv.org/abs/2305.04908, 2023.
  39. Quantum computation and quantum information. Cambridge university press, 2010.
  40. Ben W Reichardt. Span programs are equivalent to quantum query algorithms. SIAM Journal on Computing, 43(3):1206–1219, 2014.
  41. Subir Sachdev. Quantum phase transitions. Physics world, 12(4):33, 1999.
  42. Spectral gap amplification. SIAM Journal on Computing, 42(2):593–610, 2013.
  43. Computational difficulty of finding matrix product ground states. Physical review letters, 100(25):250501, 2008.
  44. Simulating physical phenomena by quantum networks. Physical Review A, 65(4):042323, (2002).
  45. Rolando D Somma. Quantum simulations of one dimensional quantum systems, 2016.
  46. Rolando D Somma. A trotter-suzuki approximation for lie groups with applications to hamiltonian simulation. Journal of Mathematical Physics, 57(6):062202, (2016).
  47. Hamiltonian simulation in the low-energy subspace. npj Quantum Information, 7(1):119, 2021.
  48. Faster digital quantum simulation by symmetry protection. PRX Quantum, 2(1):010323, 2021.
  49. Higher order decompositions of ordered operator exponentials. Journal of Physics A: Mathematical and Theoretical, 43(6):065203, (2010).
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