Quantum Monte Carlo tunneling from quantum chemistry to quantum annealing

Abstract

Quantum Tunneling is ubiquitous across different fields, from quantum chemical reactions, and magnetic materials to quantum simulators and quantum computers. While simulating the real-time quantum dynamics of tunneling is infeasible for high-dimensional systems, quantum tunneling also shows up in quantum Monte Carlo (QMC) simulations that scale polynomially with system size. Here we extend a recent results obtained for quantum spin models {[{Phys. Rev. Lett.} {\bf 117}, 180402 (2016)]}, and study high-dimensional continuos variable models for proton transfer reactions. We demonstrate that QMC simulations efficiently recover ground state tunneling rates due to the existence of an instanton path, which always connects the reactant state with the product. We discuss the implications of our results in the context of quantum chemical reactions and quantum annealing, where quantum tunneling is expected to be a valuable resource for solving combinatorial optimization problems.

References in corpus (7)

• Nuclear quantum effects in water
• Quantum annealing with antiferromagnetic fluctuations
• Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics
• Quantitative Protein Dynamics from Dominant Folding Pathways
• Adiabatic optimization versus diffusion Monte Carlo
• Dominant Reaction Pathways in High Dimensional Systems
• Quantum Monte Carlo Annealing with Multi-Spin Dynamics

Cited by in corpus (20)

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• Scaling overhead of embedding optimization problems in quantum annealing
• Quantum computing for chemistry and physics applications from a Monte Carlo perspective
• Projective quantum Monte Carlo simulations guided by unrestricted neural network states
• Sampling, rates, and reaction currents through reverse stochastic quantization on quantum computers
• Understanding Quantum Tunneling using Diffusion Monte Carlo Simulations
• Phase transition of Frustrated Ising model via D-wave Quantum Annealing Machine
• Transit facility allocation: Hybrid quantum-classical optimization
• On the dynamics of Simulated Quantum Annealing in random Ising chains
• Supervised learning of random quantum circuits via scalable neural networks
• Preparing ground states of the XXZ model using the quantum annealing with inductively coupled superconducting flux qubits
• Tunneling in projective quantum Monte Carlo simulations with guiding wave functions
• Theoretical survey of unconventional quantum annealing methods applied to adifficult trial problem
• From quantum-enhanced to quantum-inspired Monte Carlo
• Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states
• Finding Hadamard matrices by a quantum annealing machine