Latent Space Purification via Neural Density Operators

Giacomo Torlai and Roger G. Melko
Phys. Rev. Lett. 120, 240503 – Published 15 June 2018
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Abstract

Machine learning is actively being explored for its potential to design, validate, and even hybridize with near-term quantum devices. A central question is whether neural networks can provide a tractable representation of a given quantum state of interest. When true, stochastic neural networks can be employed for many unsupervised tasks, including generative modeling and state tomography. However, to be applicable for real experiments, such methods must be able to encode quantum mixed states. Here, we parametrize a density matrix based on a restricted Boltzmann machine that is capable of purifying a mixed state through auxiliary degrees of freedom embedded in the latent space of its hidden units. We implement the algorithm numerically and use it to perform tomography on some typical states of entangled photons, achieving fidelities competitive with standard techniques.

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  • Received 6 February 2018

DOI:https://doi.org/10.1103/PhysRevLett.120.240503

© 2018 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsQuantum Information, Science & Technology

Authors & Affiliations

Giacomo Torlai and Roger G. Melko

  • Department of Physics and Astronomy, University of Waterloo, Ontario N2L 3G1, Canada, and Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada

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Issue

Vol. 120, Iss. 24 — 15 June 2018

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