Ground state preparation with shallow variational warm-start

Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground state preparation scheme with shallow variational warm-start to tackle the bottlenecks of current algorithms, i.e., demand for prior ground state energy information and lack of demonstration of efficient initial state preparation. Particularly, our methods would not experience the instability for small spectral gap $\Delta$ during pre-encoding the phase factors since our methods involve only $\widetilde{O}(1)$ factors while $\widetilde{O}(\Delta^{-1})$ is requested by the near-optimal methods. We demonstrate the effectiveness of our methods via extensive numerical simulations on spin-$1/2$ Heisenberg models. We also show that the shallow warm-start procedure can process chemical molecules by conducting numerical simulations on the hydrogen chain model. Moreover, we extend research on the Hubbard model, demonstrating superior performance compared to the prevalent variational quantum algorithms.

[1]  Zhiyan Ding,et al.  Even Shorter Quantum Circuit for Phase Estimation on Early Fault-Tolerant Quantum Computers with Applications to Ground-State Energy Estimation , 2022, PRX Quantum.

[2]  Xin Wang,et al.  Quantum Phase Processing: Transform and Extract Eigen-Information of Quantum Systems , 2022, ArXiv.

[3]  Peter D. Johnson,et al.  Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision , 2022, 2209.06811.

[4]  Yu Tong,et al.  Ground state preparation and energy estimation on early fault-tolerant quantum computers via quantum eigenvalue transformation of unitary matrices , 2022, PRX Quantum.

[5]  A. Montanaro,et al.  Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer , 2021, Nature Communications.

[6]  M. Berta,et al.  A randomized quantum algorithm for statistical phase estimation , 2021, Physical review letters.

[7]  A. Fedorov,et al.  Variational quantum eigensolver techniques for simulating carbon monoxide oxidation , 2021, Communications Physics.

[8]  Lin Lin,et al.  Heisenberg-Limited Ground-State Energy Estimation for Early Fault-Tolerant Quantum Computers , 2021, PRX Quantum.

[9]  C. Gogolin,et al.  Local, expressive, quantum-number-preserving VQE ansätze for fermionic systems , 2021, New Journal of Physics.

[10]  Lea M. Trenkwalder,et al.  Reinforcement learning for optimization of variational quantum circuit architectures , 2021, NeurIPS.

[11]  Patrick J. Coles,et al.  Cost function dependent barren plateaus in shallow parametrized quantum circuits , 2021, Nature Communications.

[12]  Jakub Marecek,et al.  Warm-starting quantum optimization , 2020, Quantum.

[13]  N. Yamamoto,et al.  Expressibility of the alternating layered ansatz for quantum computation , 2020, Quantum.

[14]  Xin Wang,et al.  Variational quantum Gibbs state preparation with a truncated Taylor series , 2020, Physical Review Applied.

[15]  Masoud Mohseni,et al.  Observation of separated dynamics of charge and spin in the Fermi-Hubbard model , 2020, 2010.07965.

[16]  Edward F. Valeev,et al.  Fundamentals of Molecular Integrals Evaluation , 2020, 2007.12057.

[17]  C. Gogolin,et al.  Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer , 2020, Physical Review Research.

[18]  Kevin J. Sung,et al.  Hartree-Fock on a superconducting qubit quantum computer , 2020, Science.

[19]  Lin Lin,et al.  Near-optimal ground state preparation , 2020, Quantum.

[20]  K. B. Whaley,et al.  Efficient phase-factor evaluation in quantum signal processing , 2020, Physical Review A.

[21]  Chris Cade,et al.  Strategies for solving the Fermi-Hubbard model on near-term quantum computers , 2019, 1912.06007.

[22]  Rolando D. Somma,et al.  Quantum eigenvalue estimation via time series analysis , 2019, New Journal of Physics.

[23]  Ryan Babbush,et al.  Qubitization of Arbitrary Basis Quantum Chemistry Leveraging Sparsity and Low Rank Factorization , 2019, Quantum.

[24]  S. Sanvito,et al.  Machine learning density functional theory for the Hubbard model , 2018, Physical Review B.

[25]  B. Terhal,et al.  Quantum phase estimation of multiple eigenvalues for small-scale (noisy) experiments , 2018, New Journal of Physics.

[26]  Nathan Wiebe,et al.  Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics , 2018, STOC.

[27]  S. Brierley,et al.  Accelerated Variational Quantum Eigensolver. , 2018, Physical review letters.

[28]  Jonathan Romero,et al.  Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer , 2018, Quantum Science and Technology.

[29]  J. Ignacio Cirac,et al.  Faster ground state preparation and high-precision ground energy estimation with fewer qubits , 2017, Journal of Mathematical Physics.

[30]  I. Chuang,et al.  Hamiltonian Simulation by Qubitization , 2016, Quantum.

[31]  Daniel S. Levine,et al.  Postponing the orthogonality catastrophe: efficient state preparation for electronic structure simulations on quantum devices , 2018, 1809.05523.

[32]  H. Neven,et al.  Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.

[33]  H. Neven,et al.  Low-Depth Quantum Simulation of Materials , 2018 .

[34]  M. Elimelech,et al.  Role of Ionic Charge Density in Donnan Exclusion of Monovalent Anions by Nanofiltration. , 2018, Environmental science & technology.

[35]  Dmitri Maslov,et al.  Toward the first quantum simulation with quantum speedup , 2017, Proceedings of the National Academy of Sciences.

[36]  Alán Aspuru-Guzik,et al.  Quantum Simulation of Electronic Structure with Linear Depth and Connectivity. , 2017, Physical review letters.

[37]  I. Chuang,et al.  Hamiltonian Simulation by Uniform Spectral Amplification , 2017, 1707.05391.

[38]  David M. Ceperley,et al.  Towards the solution of the many-electron problem in real materials: equation of state of the hydrogen chain with state-of-the-art many-body methods , 2017, 1705.01608.

[39]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

[40]  I. Chuang,et al.  Optimal Hamiltonian Simulation by Quantum Signal Processing. , 2016, Physical review letters.

[41]  Marcos Rigol,et al.  Observation of spatial charge and spin correlations in the 2D Fermi-Hubbard model , 2016, Science.

[42]  M. Hastings,et al.  Progress towards practical quantum variational algorithms , 2015, 1507.08969.

[43]  Andrey E. Antipov,et al.  Solutions of the Two-Dimensional Hubbard Model: Benchmarks and Results from a Wide Range of Numerical Algorithms , 2015, 1505.02290.

[44]  Andrew M. Childs,et al.  Hamiltonian Simulation with Nearly Optimal Dependence on all Parameters , 2015, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science.

[45]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[46]  M. Gutzwiller,et al.  The Hubbard model at half a century , 2013, Nature Physics.

[47]  P. Love,et al.  The Bravyi-Kitaev transformation for quantum computation of electronic structure. , 2012, The Journal of chemical physics.

[48]  M. Reiher,et al.  Spin in density‐functional theory , 2012, 1206.2234.

[49]  M. W. Johnson,et al.  Quantum annealing with manufactured spins , 2011, Nature.

[50]  Simeng Yan,et al.  Spin-Liquid Ground State of the S = 1/2 Kagome Heisenberg Antiferromagnet , 2010, Science.

[51]  H. M. Wiseman,et al.  How to perform the most accurate possible phase measurements , 2009, 0907.0014.

[52]  D. Poulin,et al.  Preparing ground States of quantum many-body systems on a quantum computer. , 2008, Physical review letters.

[53]  C. Sherrill An Introduction to Hartree-Fock Molecular Orbital Theory , 2009 .

[54]  D. Berry,et al.  Entanglement-free Heisenberg-limited phase estimation , 2007, Nature.

[55]  John F. Stanton,et al.  Applications of Post‐Hartree—Fock Methods: A Tutorial , 2007 .

[56]  D. Scalapino Numerical Studies of the 2D Hubbard Model , 2006, cond-mat/0610710.

[57]  E. Knill,et al.  Optimal quantum measurements of expectation values of observables , 2006, quant-ph/0607019.

[58]  Y. Omar Indistinguishable particles in quantum mechanics: an introduction , 2005, quant-ph/0511002.

[59]  P. Moral,et al.  Sequential Monte Carlo samplers , 2002, cond-mat/0212648.

[60]  E. V. C. Silva,et al.  Thermodynamics of the limiting cases of the XXZ model without Bethe ansatz , 2001 .

[61]  G. Brassard,et al.  Quantum Amplitude Amplification and Estimation , 2000, quant-ph/0005055.

[62]  S. Lloyd,et al.  Quantum Algorithm Providing Exponential Speed Increase for Finding Eigenvalues and Eigenvectors , 1998, quant-ph/9807070.

[63]  E. Gadioli,et al.  Introductory Nuclear Physics , 1997 .

[64]  L. Faddeev How algebraic Bethe ansatz works for integrable model , 1996, hep-th/9605187.

[65]  Alexei Y. Kitaev,et al.  Quantum measurements and the Abelian Stabilizer Problem , 1995, Electron. Colloquium Comput. Complex..

[66]  W. Gilks,et al.  Adaptive Rejection Metropolis Sampling Within Gibbs Sampling , 1995 .

[67]  J. J. Sakurai,et al.  Modern Quantum Mechanics, Revised Edition , 1995 .

[68]  E. Dagotto Correlated electrons in high-temperature superconductors , 1993, cond-mat/9311013.

[69]  E. Celeghini,et al.  Heisenberg XXZ model and quantum Galilei group , 1992, hep-th/9204054.

[70]  Peter J. Knowles,et al.  A determinant based full configuration interaction program , 1989 .

[71]  R. Parr Density-functional theory of atoms and molecules , 1989 .

[72]  R. Feynman Simulating physics with computers , 1999 .

[73]  F. Barahona On the computational complexity of Ising spin glass models , 1982 .

[74]  John A. Pople,et al.  Self‐consistent molecular orbital methods. XVIII. Constraints and stability in Hartree–Fock theory , 1977 .

[75]  Richard Phillips Feynman,et al.  Energy Spectrum of the Excitations in Liquid Helium , 1956 .