Production of photonic universal quantum gates enhanced by machine learning

Krishna Kumar Sabapathy, Haoyu Qi, Josh Izaac, and Christian Weedbrook
Phys. Rev. A 100, 012326 – Published 17 July 2019

Abstract

We introduce photonic architectures for universal quantum computation. The first step is to produce a resource state which is a superposition of the first four Fock states with a probability greater than or equal to 102, an increase by a factor of 104 over standard sequential photon-subtraction techniques. The resource state is produced with near-perfect fidelity from a quantum gadget that uses displaced squeezed vacuum states, interferometers, and photon-number-resolving detectors. The parameters of this gadget are trained using machine learning algorithms for variational circuits. We discuss in detail various aspects of the non-Gaussian state preparation resulting from the numerical experiments. We then propose a notion of resource farms where these gadgets are stacked in parallel, to increase the success probability further. We find a trade-off between the success probability of the farm, the error tolerance, and the number of gadgets. Using the resource states in conventional gate teleportation techniques, we can then implement weak tunable cubic phase gates. The numerical tools that have been developed could potentially be useful for other applications in photonics as well.

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  • Received 8 October 2018
  • Revised 11 April 2019

DOI:https://doi.org/10.1103/PhysRevA.100.012326

©2019 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Krishna Kumar Sabapathy, Haoyu Qi, Josh Izaac, and Christian Weedbrook

  • Xanadu, 777 Bay Street, Toronto, Ontario, Canada M5G 2C8

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Issue

Vol. 100, Iss. 1 — July 2019

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