Quantum algorithm for ground state energy estimation using circuit depth with exponentially improved dependence on precision

A milestone in the field of quantum computing will be solving problems in quantum chemistry and materials faster than state-of-the-art classical methods. The current understanding is that achieving quantum advantage in this area will require some degree of fault-tolerance. While hardware is improving towards this milestone, optimizing quantum algorithms also brings it closer to the present. Existing methods for ground state energy estimation are costly in that they require a number of gates per circuit that grows exponentially with the desired number of bits in precision. We reduce this cost exponentially, by developing a ground state energy estimation algorithm for which this cost grows linearly in the number of bits of precision. Relative to recent resource estimates of ground state energy estimation for the industrially-relevant molecules of ethylene-carbonate and PF$_6^-$, the estimated gate count and circuit depth is reduced by a factor of 43 and 78, respectively. Furthermore, the algorithm can use additional circuit depth to reduce the total runtime. These features make our algorithm a promising candidate for realizing quantum advantage in the era of early fault-tolerant quantum computing.

[1]  Edward F. Valeev,et al.  Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry , 2022, Nature Communications.

[2]  Peter D. Johnson,et al.  On proving the robustness of algorithms for early fault-tolerant quantum computers , 2022, 2209.11322.

[3]  Yu Tong Designing algorithms for estimating ground state properties on early fault-tolerant quantum computers , 2022, Quantum Views.

[4]  G. Chan,et al.  The Chromium Dimer: Closing a Chapter of Quantum Chemistry , 2022, Journal of the American Chemical Society.

[5]  Eric R. Anschuetz,et al.  Quantum variational algorithms are swamped with traps , 2022, Nature communications.

[6]  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.

[7]  Jérôme F Gonthier,et al.  Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation , 2022, 2203.07275.

[8]  Peter D. Johnson,et al.  State Preparation Boosters for Early Fault-Tolerant Quantum Computation , 2022, Quantum.

[9]  Joonho Lee,et al.  Reliably assessing the electronic structure of cytochrome P450 on today’s classical computers and tomorrow’s quantum computers , 2022, Proceedings of the National Academy of Sciences of the United States of America.

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

[11]  Peter D. Johnson,et al.  Computing Ground State Properties with Early Fault-Tolerant Quantum Computers , 2021, Quantum.

[12]  Isaac H. Kim,et al.  Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules , 2021, Physical Review Research.

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

[14]  Peter D. Johnson,et al.  Reducing runtime and error in VQE using deeper and noisier quantum circuits , 2021, 2110.10664.

[15]  I. Chuang,et al.  Grand Unification of Quantum Algorithms , 2021, PRX Quantum.

[16]  Yudong Cao,et al.  Minimizing Estimation Runtime on Noisy Quantum Computers , 2021, PRX Quantum.

[17]  Jhonathan Romero,et al.  Identifying challenges towards practical quantum advantage through resource estimation: the measurement roadblock in the variational quantum eigensolver , 2020 .

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

[19]  A. Scemama,et al.  A Mountaineering Strategy to Excited States: Highly-Accurate Energies and Benchmarks for Medium Size Molecules. , 2019, Journal of chemical theory and computation.

[20]  E. Campbell Random Compiler for Fast Hamiltonian Simulation. , 2018, Physical review letters.

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

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

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

[24]  Alexandru Paler,et al.  Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity , 2018, Physical Review X.

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

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

[27]  Frank Neese,et al.  Software update: the ORCA program system, version 4.0 , 2018 .

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

[29]  Alán Aspuru-Guzik,et al.  Exploiting Locality in Quantum Computation for Quantum Chemistry. , 2014, The journal of physical chemistry letters.

[30]  A. Ambainis On Physical Problems that are Slightly More Difficult than QMA , 2013, 2014 IEEE 29th Conference on Computational Complexity (CCC).

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

[32]  Frank Neese,et al.  The ORCA program system , 2012 .

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

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

[35]  A. Eremenko,et al.  Uniform approximation of sgn x by polynomials and entire functions , 2006, math/0604324.

[36]  M. Head‐Gordon,et al.  Simulated Quantum Computation of Molecular Energies , 2005, Science.

[37]  N. Fisher,et al.  Probability Inequalities for Sums of Bounded Random Variables , 1994 .

[38]  M. Powell,et al.  Approximation theory and methods , 1984 .

[39]  Van Vleck Nonorthogonality and Ferromagnetism , 1936 .