Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems.

: The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wavefunction ansatz. Compared to physically motivated ansatzes, hardware heuristic ansatzes usually lead to a shallower circuit, but it may still be too deep for an NISQ device. Inspired by the quantum neural network, we propose a new hardware heuristic ansatz where the circuit depth can be significantly reduced by introducing ancilla qubits, which makes a practical simulation of a chemical reaction with more than 20 atoms feasible on a currently available quantum computer. More importantly, the expressibility of this new ansatz can be improved by increasing either the depth or the width of the circuit, which makes it adaptable to different hardware environments. These results open a new avenue to develop practical applications of quantum computation in the NISQ era.

[1]  Chaoyang Lu,et al.  Scalable quantum computational chemistry with superconducting qubits , 2022 .

[2]  Y. M. Rhee,et al.  Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers , 2022, npj Quantum Information.

[3]  Michael J. Hoffmann,et al.  Purification-based quantum error mitigation of pair-correlated electron simulations , 2022, Nature Physics.

[4]  Jie Liu,et al.  Q2Chemistry: A quantum computation platform for quantum chemistry , 2022, JUSTC.

[5]  Jie Liu,et al.  Quantum algorithms for electronic structures: basis sets and boundary conditions. , 2022, Chemical Society reviews.

[6]  Jakob S. Kottmann,et al.  A quantum computing view on unitary coupled cluster theory. , 2021, Chemical Society reviews.

[7]  Joonho Lee,et al.  Unbiasing fermionic quantum Monte Carlo with a quantum computer , 2021, Nature.

[8]  Haibin Zhang,et al.  Strong Quantum Computational Advantage Using a Superconducting Quantum Processor. , 2021, Physical review letters.

[9]  K. Itoh,et al.  Materials challenges and opportunities for quantum computing hardware , 2021, Science.

[10]  M. Cerezo,et al.  Variational quantum algorithms , 2020, Nature Reviews Physics.

[11]  Kenneth R. Brown,et al.  Materials challenges for trapped-ion quantum computers , 2020, Nature Reviews Materials.

[12]  Hiroshi C. Watanabe,et al.  Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters , 2020, npj Computational Materials.

[13]  Patrick J. Coles,et al.  Noise-induced barren plateaus in variational quantum algorithms , 2020, Nature Communications.

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

[15]  Marco Pistoia,et al.  Computational Investigations of the Lithium Superoxide Dimer Rearrangement on Noisy Quantum Devices. , 2019, The journal of physical chemistry. A.

[16]  Jian-Wei Pan,et al.  Quantum computational advantage using photons , 2020, Science.

[17]  José Miguel Hernández-Lobato,et al.  Depth Uncertainty in Neural Networks , 2020, NeurIPS.

[18]  Pauline J Ollitrault,et al.  Hardware efficient quantum algorithms for vibrational structure calculations , 2020, Chemical science.

[19]  T. Osborne,et al.  Training deep quantum neural networks , 2020, Nature Communications.

[20]  B. Bauer,et al.  Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. , 2020, Chemical reviews.

[21]  Nicholas J. Mayhall,et al.  Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm , 2019, npj Quantum Information.

[22]  Alán Aspuru-Guzik,et al.  Quantum computational chemistry , 2018, Reviews of Modern Physics.

[23]  John C. Platt,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[24]  Gavin E. Crooks,et al.  Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition , 2019, 1905.13311.

[25]  Peter D. Johnson,et al.  Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms , 2019, Advanced Quantum Technologies.

[26]  Harper R. Grimsley,et al.  An adaptive variational algorithm for exact molecular simulations on a quantum computer , 2018, Nature Communications.

[27]  Alán Aspuru-Guzik,et al.  Quantum Chemistry in the Age of Quantum Computing. , 2018, Chemical reviews.

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

[29]  M. Becker,et al.  Beyond olefins: new metathesis directions for synthesis. , 2018, Chemical Society reviews.

[30]  Scott N. Genin,et al.  Qubit Coupled Cluster Method: A Systematic Approach to Quantum Chemistry on a Quantum Computer. , 2018, Journal of chemical theory and computation.

[31]  Ivano Tavernelli,et al.  Quantum algorithms for electronic structure calculations: Particle-hole Hamiltonian and optimized wave-function expansions , 2018, Physical Review A.

[32]  T. Monz,et al.  Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator , 2018, Physical Review X.

[33]  R. Pooser,et al.  Cloud Quantum Computing of an Atomic Nucleus. , 2018, Physical Review Letters.

[34]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[35]  Sandeep Sharma,et al.  PySCF: the Python‐based simulations of chemistry framework , 2017, 1701.08223.

[36]  J. McClean,et al.  Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz , 2017, Quantum Science and Technology.

[37]  Sandeep Sharma,et al.  PySCF: the Python‐based simulations of chemistry framework , 2018 .

[38]  Katherine Bourzac,et al.  Chemistry is quantum computing’s killer app , 2017 .

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

[40]  M. Yung,et al.  Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure , 2015, 1506.00443.

[41]  S. Debnath,et al.  Demonstration of a small programmable quantum computer with atomic qubits , 2016, Nature.

[42]  Li-Juan Yu,et al.  Can DFT and ab initio methods describe all aspects of the potential energy surface of cycloreversion reactions? , 2016 .

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

[44]  Lars Goerigk,et al.  Accurate reaction barrier heights of pericyclic reactions: Surprisingly large deviations for the CBS‐QB3 composite method and their consequences in DFT benchmark studies , 2015, J. Comput. Chem..

[45]  M. Hastings,et al.  Gate count estimates for performing quantum chemistry on small quantum computers , 2013, 1312.1695.

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

[47]  A. Akimov,et al.  The PYXAID Program for Non-Adiabatic Molecular Dynamics in Condensed Matter Systems. , 2013, Journal of chemical theory and computation.

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

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

[50]  H. Sommers,et al.  Average fidelity between random quantum states , 2003, quant-ph/0311117.

[51]  Andrew G. Leach,et al.  A Standard Set of Pericyclic Reactions of Hydrocarbons for the Benchmarking of Computational Methods: The Performance of ab Initio, Density Functional, CASSCF, CASPT2, and CBS-QB3 Methods for the Prediction of Activation Barriers, Reaction Energetics, and Transition State Geometries , 2003 .

[52]  C. Monroe,et al.  Architecture for a large-scale ion-trap quantum computer , 2002, Nature.

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

[54]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[55]  E. Fradkin,et al.  Jordan-Wigner transformation for quantum-spin systems in two dimensions and fractional statistics. , 1989, Physical review letters.