- Open Access
Heisenberg-Limited Ground-State Energy Estimation for Early Fault-Tolerant Quantum Computers
PRX Quantum 3, 010318 – Published 2 February, 2022
DOI: https://doi.org/10.1103/PRXQuantum.3.010318
Abstract
Under suitable assumptions, the quantum-phase-estimation (QPE) algorithm is able to achieve Heisenberg-limited precision scaling in estimating the ground-state energy. However, QPE requires a large number of ancilla qubits and a large circuit depth, as well as the ability to perform inverse quantum Fourier transform, making it expensive to implement on an early fault-tolerant quantum computer. We propose an alternative method to estimate the ground-state energy of a Hamiltonian with Heisenberg-limited precision scaling, which employs a simple quantum circuit with one ancilla qubit, and a classical postprocessing procedure. Besides the ground-state energy, our algorithm also produces an approximate cumulative distribution function of the spectral measure, which can be used to compute other spectral properties of the Hamiltonian.
Physics Subject Headings (PhySH)
Popular Summary
Fault-tolerant quantum computers have the potential to significantly accelerate quantum many-body simulations. This paper concerns the estimation of the ground-state energy of a Hamiltonian on early fault-tolerant quantum devices. These devices are expected to be much more robust and powerful than the current noisy intermediate-scale quantum devices but have a limited number of logical qubits and limited circuit depths. Most existing fault-tolerant quantum algorithms for this task require a significant number of qubits and/or large circuit depths, which can be impractical in the early fault-tolerant setting.
Provided that a good initial guess of the ground state is available, our work can estimate the ground-state energy using only one ancilla qubit. Our algorithm achieves the optimal Heisenberg-limited precision scaling both in terms of the circuit depth and the total evolution time. The key technique is to perform the time evolution with a random duration followed by measurements. This allows us to stochastically compute a cumulative distribution function of the spectral measure, which encodes the information of the ground-state energy. The techniques developed in this work can be useful in performing many other important tasks on early fault-tolerant quantum computers, such as computing the spectral measure and the many-body Green’s function.
Article Text
References (74)
- D. Aharonov, D. Gottesman, S. Irani, and J. Kempe, The power of quantum systems on a line, Commun. Math. Phys. 287, 41 (2009).
- J. Kempe, A. Kitaev, and O. Regev, The complexity of the local Hamiltonian problem, SIAM J. Comput. 35, 1070 (2006).
- A. Y. Kitaev, A. Shen, and M. N. Vyalyi, Classical and Quantum Computation, Graduate Studies in Mathematics (American Mathematical Soc., 2002), Vol. 47.
- R. Oliveira and B. M. Terhal, The complexity of quantum spin systems on a two-dimensional square lattice, Preprint ArXiv:quant-ph/0504050 (2005).
- N. M. Tubman, C. Mejuto-Zaera, J. M. Epstein, D. Hait, D. S. Levine, W. Huggins, Z. Jiang, J. R. McClean, R. Babbush, M. Head-Gordon, et al., Postponing the orthogonality catastrophe: Efficient state preparation for electronic structure simulations on quantum devices, Preprint ArXiv:1809.05523 (2018).
- R. Babbush, J. McClean, D. Wecker, A. Aspuru-Guzik, and N. Wiebe, Chemical basis of Trotter-Suzuki errors in quantum chemistry simulation, Phys. Rev. A 91, 022311 (2015).
- K. Sugisaki, S. Nakazawa, K. Toyota, K. Sato, D. Shiomi, and T. Takui, Quantum chemistry on quantum computers: A method for preparation of multiconfigurational wave functions on quantum computers without performing post–Hartree-Fock calculations, ACS Cent. Sci. 5, 167 (2018).
- S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan, Quantum computational chemistry, Rev. Mod. Phys. 92, 015003 (2020).
- Y. Atia and D. Aharonov, Fast-forwarding of Hamiltonians and exponentially precise measurements, Nat. Commun. 8, 1572 (2017).
- V. Giovannetti, S. Lloyd, and L. Maccone, Quantum Metrology, Phys. Rev. Lett. 96, 010401 (2006).
- V. Giovannetti, S. Lloyd, and L. Maccone, Advances in quantum metrology, Nat. Photonics 5, 222 (2011).
- M. Zwierz, C. A. Pérez-Delgado, and P. Kok, General Optimality of the Heisenberg Limit for Quantum Metrology, Phys. Rev. Lett. 105, 180402 (2010).
- M. Zwierz, C. A. Pérez-Delgado, and P. Kok, Ultimate limits to quantum metrology and the meaning of the Heisenberg limit, Phys. Rev. A 85, 042112 (2012).
- Y. Aharonov and D. Bohm, Time in the quantum theory and the uncertainty relation for time and energy, Phys. Rev. 122, 1649 (1961).
- Y. Aharonov, S. Massar, and S. Popescu, Measuring energy, estimating Hamiltonians, and the time-energy uncertainty relation, Phys. Rev. A 66, 052107 (2002).
- A. M. Childs, J. Preskill, and J. Renes, Quantum information and precision measurement, J. Mod. Opt. 47, 155 (2000).
- A. Y. Kitaev, Quantum measurements and the Abelian stabilizer problem, Preprint ArXiV:quant-ph/9511026 (1995).
- W. J. Huggins, J. Lee, U. Baek, B. O’Gorman, and K. B. Whaley, A non-orthogonal variational quantum eigensolver, New J. Phys. 22, 073009 (2020).
- J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New J. Phys. 18, 023023 (2016).
- P. J. O’Malley, R. Babbush, I. D. Kivlichan, J. Romero, J. R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, N. Ding, et al., Scalable Quantum Simulation of Molecular Energies, Phys. Rev. X 6, 031007 (2016).
- A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, J. L. O’Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
- D. S. Abrams and S. Lloyd, Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors, Phys. Rev. Lett. 83, 5162 (1999).
- Y. Ge, J. Tura, and J. I. Cirac, Faster ground state preparation and high-precision ground energy estimation with fewer qubits, J. Math. Phys. 60, 022202 (2019).
- L. Lin and Y. Tong, Near-optimal ground state preparation, Quantum 4, 372 (2020).
- D. Poulin and P. Wocjan, Preparing Ground States of Quantum Many-Body Systems on a Quantum Computer, Phys. Rev. Lett. 102, 130503 (2009).
- R. Babbush, J. R. McClean, M. Newman, C. Gidney, S. Boixo, and H. Neven, Focus beyond quadratic speedups for error-corrected quantum advantage, PRX Quantum 2, 010103 (2021).
- K. E. Booth, B. O’Gorman, J. Marshall, S. Hadfield, and E. Rieffel, Quantum-accelerated constraint programming, Preprint ArXiv:2103.04502 (2021).
- E. T. Campbell, Early fault-tolerant simulations of the Hubbard model, Quantum Sci. Technol. 7, 015007 (2021).
- D. Layden, First-order Trotter error from a second-order perspective, Preprint ArXiv:2107.08032 (2021).
- I. D. Kivlichan, C. Gidney, D. W. Berry, N. Wiebe, J. McClean, W. Sun, Z. Jiang, N. Rubin, A. Fowler, A. Aspuru-Guzik, et al., Improved fault-tolerant quantum simulation of condensed-phase correlated electrons via trotterization, Quantum 4, 296 (2020).
- R. Cleve, A. Ekert, C. Macchiavello, and M. Mosca, Quantum algorithms revisited, Proc. R. Soc. London, Ser. A 454, 339 (1998).
- M. A. Nielsen and I. Chuang, Quantum computation and quantum information, Am. J. Phys. 70, 558 (2002).
- E. Knill, G. Ortiz, and R. D. Somma, Optimal quantum measurements of expectation values of observables, Phys. Rev. A 75, 012328 (2007).
- D. Nagaj, P. Wocjan, and Y. Zhang, Fast amplification of QMA, Quantum Inf. Comput. 9, 1053 (2009).
- D. Poulin and P. Wocjan, Sampling from the Thermal Quantum Gibbs State and Evaluating Partition Functions with a Quantum Computer, Phys. Rev. Lett. 103, 220502 (2009).
- R. Babbush, C. Gidney, D. W. Berry, N. Wiebe, J. McClean, A. Paler, A. Fowler, and H. Neven, Encoding Electronic Spectra in Quantum Circuits with Linear Complexity, Phys. Rev. X 8, 041015 (2018).
- Y. R. Sanders, D. W. Berry, P. C. Costa, L. W. Tessler, N. Wiebe, C. Gidney, H. Neven, and R. Babbush, Compilation of fault-tolerant quantum heuristics for combinatorial optimization, PRX Quantum 1, 020312 (2020).
- D. Wang, O. Higgott, and S. Brierley, Accelerated Variational Quantum Eigensolver, Phys. Rev. Lett. 122, 140504 (2019).
- N. Wiebe, C. Granade, A. Kapoor, and K. M. Svore, Bayesian inference via rejection filtering, Preprint ArXiv:1511.06458 (2015).
- R. B. Griffiths and C.-S. Niu, Semiclassical Fourier Transform for Quantum Computation, Phys. Rev. Lett. 76, 3228 (1996).
- D. W. Berry, B. L. Higgins, S. D. Bartlett, M. W. Mitchell, G. J. Pryde, and H. M. Wiseman, How to perform the most accurate possible phase measurements, Phys. Rev. A 80, 052114 (2009).
- B. L. Higgins, D. W. Berry, S. D. Bartlett, H. M. Wiseman, and G. J. Pryde, Entanglement-free Heisenberg-limited phase estimation, Nature 450, 393 (2007).
- S. Lu, M. C. Bañuls, and J. I. Cirac, Algorithms for quantum simulation at finite energies, Preprint ArXiv:2006.03032 (2020).
- T. E. O’Brien, S. Polla, N. C. Rubin, W. J. Huggins, S. McArdle, S. Boixo, J. R. McClean, and R. Babbush, Error mitigation via verified phase estimation, Preprint ArXiv:2010.02538 (2020).
- A. Russo, K. Rudinger, B. Morrison, and A. Baczewski, Evaluating energy differences on a quantum computer with robust phase estimation, Preprint ArXiv:2007.08697 (2020).
- T. E. O’Brien, B. Tarasinski, and B. M. Terhal, Quantum phase estimation of multiple eigenvalues for small-scale (noisy) experiments, New J. Phys. 21, 023022 (2019).
- R. Somma, G. Ortiz, J. E. Gubernatis, E. Knill, and R. Laflamme, Simulating physical phenomena by quantum networks, Phys. Rev. A 65, 042323 (2002).
- In this paper, we use the following asymptotic notations besides the usual notation: we write if ; if and ; if .
- S. Chakraborty, A. Gilyén, and S. Jeffery, The power of block-encoded matrix powers: Improved regression techniques via faster hamiltonian simulation, Preprint ArXiv:1804.01973 (2018).
- G. H. Low and I. L. Chuang, Hamiltonian simulation by qubitization, Quantum 3, 163 (2019).
- A. Gilyén, Y. Su, G. H. Low, and N. Wiebe, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (ACM, Phoenix, AZ, USA, 2019), p. 193.
- G. H. Low and I. L. Chuang, Optimal Hamiltonian Simulation by Quantum Signal Processing, Phys. Rev. Lett. 118, 010501 (2017).
- C. Bittel and M. Kliesch, Training variational quantum algorithms is NP-hard—even for logarithmically many qubits and free fermionic systems, Preprint ArXiv:2101.07267 (2021).
- M. Motta, C. Sun, A. T. K. Tan, M. J. O’Rourke, E. Ye, A. J. Minnich, F. G. S. L. Brandão, and G. K.-L. Chan, Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution, Nat. Phys. 16, 205 (2019).
- R. M. Parrish and P. L. McMahon, Quantum filter diagonalization: Quantum eigendecomposition without full quantum phase estimation, Preprint ArXiv:1909.08925 (2019).
- N. H. Stair, R. Huang, and F. A. Evangelista, A multireference quantum Krylov algorithm for strongly correlated electrons, J. Chem. Theory Comput. 16, 2236 (2020).
- J. Emerson, S. Lloyd, D. Poulin, and D. Cory, Estimation of the local density of states on a quantum computer, Phys. Rev. A 69, 050305 (2004).
- R. D. Somma, Quantum eigenvalue estimation via time series analysis, New J. Phys. 21, 123025 (2019).
- D. W. Berry, A. M. Childs, and R. Kothari, in 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (IEEE, 2015), p. 792.
- M. Suzuki, General theory of fractal path integrals with applications to many-body theories and statistical physics, J. Math. Phys. 32, 400 (1991).
- D. W. Berry, A. M. Childs, Y. Su, X. Wang, and N. Wiebe, Time-dependent Hamiltonian simulation with l1-norm scaling, Quantum 4, 254 (2020).
- E. Campbell, Random Compiler for Fast Hamiltonian Simulation, Phys. Rev. Lett. 123, 070503 (2019).
- C.-F. Chen, H.-Y. Huang, R. Kueng, and J. A. Tropp, Quantum simulation via randomized product formulas: Low gate complexity with accuracy guarantees, Preprint ArXiv:2008.11751 (2020).
- A. M. Childs and Y. Su, Nearly Optimal Lattice Simulation by Product Formulas, Phys. Rev. Lett. 123, 050503 (2019).
- A. M. Childs, Y. Su, M. C. Tran, N. Wiebe, and S. Zhu, Theory of Trotter Error with Commutator Scaling, Phys. Rev. X 11, 011020 (2021).
- M. C. Tran, S.-K. Chu, Y. Su, A. M. Childs, and A. V. Gorshkov, Destructive Error Interference in Product-Formula Lattice Simulation, Phys. Rev. Lett. 124, 220502 (2020).
- C. Yi and E. Crosson, Spectral analysis of product formulas for quantum simulation, Preprint ArXiv:2102.12655 (2021).
- S. Boixo and R. D. Somma, Parameter estimation with mixed-state quantum computation, Phys. Rev. A 77, 052320 (2008).
- C.-H. Rhee and P. W. Glynn, in Proceedings of the 2012 Winter Simulation Conference (WSC) (IEEE, Berlin, Germany, 2012), p. 1.
- C.-H. Rhee and P. W. Glynn, Unbiased estimation with square root convergence for SDE models, Oper. Res. 63, 1026 (2015).
- M. B. Giles, Multilevel Monte Carlo methods, Acta Numer. 24, 259 (2015).
- L. Lin and Y. Tong, Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems, Quantum 4, 361 (2020).
- Y. Su, H.-Y. Huang, and E. T. Campbell, Nearly tight Trotterization of interacting electrons, Preprint ArXiv:2012.09194 (2020).
- I. D. Kivlichan, J. McClean, N. Wiebe, C. Gidney, A. Aspuru-Guzik, G. K.-L. Chan, and R. Babbush, Quantum Simulation of Electronic Structure with Linear Depth and Connectivity, Phys. Rev. Lett. 120, 110501 (2018).
