State Preparation Boosters for Early Fault-Tolerant Quantum Computation

Quantum computing is believed to be particularly useful for the simulation of chemistry and materials, among the various applications. In recent years, there have been significant advancements in the development of near-term quantum algorithms for quantum simulation, including VQE and many of its variants. However, for such algorithms to be useful, they need to overcome several critical barriers including the inability to prepare high-quality approximations of the ground state. Current challenges to state preparation, including barren plateaus and the high-dimensionality of the optimization landscape, make state preparation through ansatz optimization unreliable. In this work, we introduce the method of ground state boosting, which uses a limited-depth quantum circuit to reliably increase the overlap with the ground state. This circuit, which we call a booster, can be used to augment an ansatz from VQE or be used as a stand-alone state preparation method. The booster converts circuit depth into ground state overlap in a controllable manner. We numerically demonstrate the capabilities of boosters by simulating the performance of a particular type of booster, namely the Gaussian booster, for preparing the ground state of N2 molecular system. Beyond ground state preparation as a direct objective, many quantum algorithms, such as quantum phase estimation, rely on high-quality state preparation as a subroutine. Therefore, we foresee ground state boosting and similar methods as becoming essential algorithmic components as the field transitions into using early fault-tolerant quantum computers.

[1]  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.

[2]  Dan-Bo Zhang,et al.  Quantum Gaussian filter for exploring ground-state properties , 2021, Physical Review A.

[3]  T. Back,et al.  Performance comparison of optimization methods on variational quantum algorithms , 2021, Physical Review A.

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

[5]  M. Benedetti,et al.  Filtering variational quantum algorithms for combinatorial optimization , 2021, Quantum Science and Technology.

[6]  Jakob S. Kottmann,et al.  Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation , 2021, Physical Review A.

[7]  Anurag Anshu,et al.  Improved approximation algorithms for bounded-degree local Hamiltonians , 2021, Physical review letters.

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

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

[10]  Jakob S. Kottmann,et al.  A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers , 2020, Chemical science.

[11]  Sukin Sim,et al.  Adaptive pruning-based optimization of parameterized quantum circuits , 2020, Quantum Science and Technology.

[12]  Jakob S. Kottmann,et al.  Reducing Qubit Requirements while Maintaining Numerical Precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach. , 2020, The journal of physical chemistry letters.

[13]  L. B. Kristensen,et al.  Quantum computation of eigenvalues within target intervals , 2020, Quantum Science and Technology.

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

[15]  F. Brandão,et al.  Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution , 2019, Nature Physics.

[16]  T. Martínez,et al.  Quantum Computation of Electronic Transitions Using a Variational Quantum Eigensolver. , 2019, Physical review letters.

[17]  Nicolas P. D. Sawaya,et al.  Quantum Chemistry in the Age of Quantum Computing. , 2018, Chemical reviews.

[18]  Ken M. Nakanishi,et al.  Subspace-search variational quantum eigensolver for excited states , 2018, Physical Review Research.

[19]  K. B. Whaley,et al.  Generalized Unitary Coupled Cluster Wave functions for Quantum Computation. , 2018, Journal of chemical theory and computation.

[20]  S. Brierley,et al.  Variational Quantum Computation of Excited States , 2018, Quantum.

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

[22]  Xiao Yuan,et al.  Variational ansatz-based quantum simulation of imaginary time evolution , 2018, npj Quantum Information.

[23]  Ryan Babbush,et al.  Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.

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

[25]  Alán Aspuru-Guzik,et al.  The theory of variational hybrid quantum-classical algorithms , 2015, 1509.04279.

[26]  Theodore J. Yoder,et al.  Fixed-point quantum search with an optimal number of queries. , 2014, Physical review letters.

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

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

[29]  I. Kassal,et al.  Polynomial-time quantum algorithm for the simulation of chemical dynamics , 2008, Proceedings of the National Academy of Sciences.

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

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

[32]  S. Yan Classical and Quantum Computation , 2015 .