Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics

We present a novel, nonparametric form for compactly representing entangled many-body quantum states, which we call a “Gaussian process state.” In contrast to other approaches, we define this state explicitly in terms of a configurational data set, with the probability amplitudes statistically infer...

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Bibliographic Details
Published in:Physical review. X 2020-11, Vol.10 (4), p.041026, Article 041026
Main Authors: Glielmo, Aldo, Rath, Yannic, Csányi, Gábor, De Vita, Alessandro, Booth, George H.
Format: Article
Language:English
Subjects:
Algorithms
Amplitudes
Complexity
Condensed matter physics
Configurations
Datasets
Gaussian process
Mathematical analysis
Numerical methods
Optimization
Quantum chemistry
Quantum theory
Representations
Statistical analysis
Statistical models
Wave functions
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Summary:We present a novel, nonparametric form for compactly representing entangled many-body quantum states, which we call a “Gaussian process state.” In contrast to other approaches, we define this state explicitly in terms of a configurational data set, with the probability amplitudes statistically inferred from this data according to Bayesian statistics. In this way, the nonlocal physical correlated features of the state can be analytically resummed, allowing for exponential complexity to underpin the ansatz, but efficiently represented in a small data set. The state is found to be highly compact, systematically improvable, and efficient to sample, representing a large number of known variational states within its span. It is also proven to be a “universal approximator” for quantum states, able to capture any entangled many-body state with increasing data-set size. We develop two numerical approaches which can learn this form directly—a fragmentation approach and direct variational optimization—and apply these schemes to the fermionic Hubbard model. We find competitive or superior descriptions of correlated quantum problems compared to existing state-of-the-art variational ansatzes, as well as other numerical methods.
ISSN:2160-3308
2160-3308
DOI:10.1103/PhysRevX.10.041026