Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers

Abstract

We demonstrate an end-to-end workflow to model chemical reaction barriers with the quantum-classical auxiliary field quantum Monte Carlo (QC-AFQMC) algorithm with quantum tomography using matchgate shadows. The workflow operates within an accelerated quantum supercomputing environment with the IonQ Forte quantum computer and NVIDIA GPUs on Amazon Web Services. We present several algorithmic innovations and an efficient GPU-accelerated execution, which achieves a several orders of magnitude speedup over the state-of-the-art implementation of QC-AFQMC. We apply the algorithm to simulate the oxidative addition step of the nickel-catalyzed Suzuki-Miyaura reaction using 24 qubits of IonQ Forte with 16 qubits used to represent the trial state, plus 8 additional ancilla qubits for error mitigation, resulting in the largest QC-AFQMC with matchgate shadow experiments ever performed on quantum hardware. We achieve a $9\times$ speedup in collecting matchgate circuit measurements, and our distributed-parallel post-processing implementation attains a $656\times$ time-to-solution improvement over the prior state-of-the-art. Chemical reaction barriers for the model reaction evaluated with active-space QC-AFQMC are within the uncertainty interval of $\pm 4$ kcal/mol from the reference CCSD(T) result when matchgates are sampled on the ideal simulator and within 10 kcal/mol from reference when measured on QPU. This work marks a step towards practical quantum chemistry simulations on quantum devices while identifying several opportunities for further development.

References in corpus (46)

• A variational eigenvalue solver on a quantum processor
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Simulated Quantum Computation of Molecular Energies
• Recent developments in the PySCF program package
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Hamiltonian Simulation by Qubitization
• Scalable Quantum Simulation of Molecular Energies
• Towards Quantum Chemistry on a Quantum Computer
• Hartree-Fock on a superconducting qubit quantum computer
• Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
• Quantum Monte Carlo method using phase-free random walks with Slater determinants
• Automated Selection of Active Orbital Spaces
• Even more efficient quantum computations of chemistry through tensor hypercontraction
• Provably efficient machine learning for quantum many-body problems
• Unbiasing Fermionic Quantum Monte Carlo with a Quantum Computer
• Resource-Efficient Quantum Algorithm for Protein Folding
• Efficient numerical computation of the Pfaffian for dense and banded skew-symmetric matrices
• Errors in trapped-ion quantum gates due to spontaneous photon scattering
• Ab initio computations of molecular systems by the auxiliary-field quantum Monte Carlo method
• Fermionic partial tomography via classical shadows
• Quantum Orbital-Optimized Unitary Coupled Cluster Methods in the Strongly Correlated Regime: Can Quantum Algorithms Outperform their Classical Equivalents?
• Robust shadow estimation
• QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo
• Measurements as a roadblock to near-term practical quantum advantage in chemistry: resource analysis
• Block2: a comprehensive open source framework to develop and apply state-of-the-art DMRG algorithms in electronic structure and beyond
• Classical Shadows With Noise
• autoCAS: a program for fully automated multi-configurational calculations
• Benchmarking a trapped-ion quantum computer with 30 qubits
• Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer
• Matchgate Shadows for Fermionic Quantum Simulation
• Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
• Prospects of Quantum Computing for Molecular Sciences
• Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
• Deterministic correction of qubit loss
• Single-ancilla ground state preparation via Lindbladians
• A Projector Quantum Monte Carlo Method for non-linear wavefunctions
• Photon scattering errors during stimulated Raman transitions in trapped-ion qubits
• Group-theoretic error mitigation enabled by classical shadows and symmetries
• Chemically Aware Unitary Coupled Cluster with ab initio Calculations on System Model H1: A Refrigerant Chemicals Application
• Evaluating a quantum-classical quantum Monte Carlo algorithm with Matchgate shadows
• Unbiasing Fermionic Auxiliary-Field Quantum Monte Carlo with Matrix Product State Trial Wavefunctions
• The prospects of Monte Carlo antibody loop modelling on a fault-tolerant quantum computer
• Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures
• Beyond MP2 initialization for unitary coupled cluster quantum circuits
• Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic
• Stability of classical shadows under gate-dependent noise