Extending the reach of quantum computing for materials science with
machine learning potentials
- URL: http://arxiv.org/abs/2203.07219v1
- Date: Mon, 14 Mar 2022 15:59:30 GMT
- Title: Extending the reach of quantum computing for materials science with
machine learning potentials
- Authors: Julian Schuhmacher, Guglielmo Mazzola, Francesco Tacchino, Olga
Dmitriyeva, Tai Bui, Shanshan Huang, Ivano Tavernelli
- Abstract summary: We propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential.
We investigate the trainability of a machine learning potential selecting various sources of noise.
We construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule.
- Score: 0.3352108528371308
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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