Extending the reach of quantum computing for materials science with machine learning potentials
arXiv:2203.07219 · doi:10.1063/5.0099469
Abstract
Solving electronic structure problems represents a promising field of application for quantum computers. Currently, much effort has been spent in devising and optimizing quantum algorithms for quantum chemistry problems featuring up to hundreds of electrons. While quantum algorithms can in principle outperform their classical equivalents, the polynomially scaling runtime, with the number of constituents, can still prevent quantum simulations of large scale systems. We propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential, trained on quantum simulation data. The challenge of applying machine learning potentials in today's quantum setting arises from the several sources of noise affecting the quantum computations of electronic energies and forces. We investigate the trainability of a machine learning potential selecting various sources of noise: statistical, optimization and hardware noise.Finally, we construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule. This already would allow us to perform arbitrarily long and stable molecular dynamics simulations, outperforming all current quantum approaches to molecular dynamics and structure optimization.
arXiv comment12 pages, 8 figures
References in corpus (69)
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Quantum information with Rydberg atoms
- Machine learning and the physical sciences
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- A Quantum Engineer's Guide to Superconducting Qubits
- Noisy intermediate-scale quantum (NISQ) algorithms
- Perspective on density functional theory
- Quantum computational chemistry
- Machine Learning Force Fields
- Quantum Chemistry in the Age of Quantum Computing
- Simulated Quantum Computation of Molecular Energies
- Trapped-Ion Quantum Computing: Progress and Challenges
- Predicting Many Properties of a Quantum System from Very Few Measurements
- Quantum algorithms: an overview
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Machine learning for molecular simulation
- Big Data meets Quantum Chemistry Approximations: The -Machine Learning Approach
- Extending the computational reach of a noisy superconducting quantum processor
- Many-body quantum state tomography with neural networks
- A quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors
- Noise-Induced Barren Plateaus in Variational Quantum Algorithms
- Quantum algorithms for quantum chemistry and quantum materials science
- Machine Learning Unifies the Modelling of Materials and Molecules
- Elucidating Reaction Mechanisms on Quantum Computers
- Towards Practical Quantum Variational Algorithms
- Efficient variational quantum simulator incorporating active error minimisation
- Quantum optimization using variational algorithms on near-term quantum devices
- Practical Quantum Error Mitigation for Near-Future Applications
- How van der Waals interactions determine the unique properties of water
- Deep neural network solution of the electronic Schrödinger equation
- Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
- Generalized Unitary Coupled Cluster Wavefunctions for Quantum Computation
- Exponential suppression of bit or phase flip errors with repetitive error correction
- Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
- Ab initio thermodynamics of liquid and solid water
- Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions
- Mitigating measurement errors in multi-qubit experiments
- Semiconductor Qubits In Practice
- Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
- Identifying topological order through unsupervised machine learning
- Efficient and Noise Resilient Measurements for Quantum Chemistry on Near-Term Quantum Computers
- Fermionic neural-network states for ab-initio electronic structure
- Accelerated Variational Quantum Eigensolver
- Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
- Tapering off qubits to simulate fermionic Hamiltonians
- Efficient quantum measurement of Pauli operators in the presence of finite sampling error
- Evidence for supercritical behavior of high-pressure liquid hydrogen
- Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
- Efficient magic state factories with a catalyzed |CCZ> to 2|T> transformation
- Exponential Error Suppression for Near-Term Quantum Devices
- Decoding quantum errors with subspace expansions
- Measurement reduction in variational quantum algorithms
- Machine learning of noise-resilient quantum circuits
- A six-dimensional H2-H2 potential energy surface for bound state spectroscopy
- Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms
- Minimizing State Preparations in Variational Quantum Eigensolver by Partitioning into Commuting Families
- Minimizing estimation runtime on noisy quantum computers
- Error mitigation via verified phase estimation
- Precise measurement of quantum observables with neural-network estimators
- Efficient evaluation of quantum observables using entangled measurements
- TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo
- Neural Error Mitigation of Near-Term Quantum Simulations
- Resource Estimation for Quantum Variational Simulations of the Hubbard Model
- High pressure hydrogen by machine learning and quantum Monte Carlo
- Microcanonical and finite temperature ab initio molecular dynamics simulations on quantum computers
- Algorithmic Error Mitigation Scheme for Current Quantum Processors