Demonstration of a photonic indistinguishability witness on a programmable quantum photonic processor

Somhorst, F. H. B.; Meer, Reinier van der; Hooijschuur, Peter; Venderbosch, Pim; Goede, Michiel de; Kassenberg, Ben; Snijders, Henk; Taballione, Caterina; Epping, Jörn P.; Vlekkert, H.H. van den; Walk, Nathan; Pinkse, Pepijn W. H.; Renema, Jelmer J.

doi:10.1364/bgppm.2022.jtu2a.25

Cited by 1 publication

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References 7 publications

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“…Computers like this are currently in operation and are being offered for researchers. The most known photonic processor from Xanadu, described by Somhorst et al in [6] or Madsen et al…”

Section: Introductionmentioning

confidence: 99%

Deep learning as a tool of quantum error reduction in quantum image processing

Werner,

Wereszczyński,

Potempa

et al. 2023

Preprint

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Quantum image representation is a widely researched area of quantum computing. Currently developed methods use angle parameter of the rotation gate (e.g., the FRQI method), sequences of qubits (ex. NEQR method) or phase shift (ex. LPIQE method) for storing color information of pixels. All of those methods are affected by decoherence and other classical and quantum noise, which is an inseparable part of quantum computing in a NISQ (Noisy Intermediate Scale Quantum) era. These all phenomenons influence the measurements, its probability distribution over the measurement basis and, as the result, makes the extracted images not even similar to those, which was stored in quantum computers. Since this process is, in its foundation, quantum as well, the computational reversal of this process is possible. There are a lot of methods for error correction, mitigation and reduction, but all of them use quantum computer time, or additional qubits to achieve desired result. We report a successful use of the Generative Adversarial Network tuned for image-to-image translation in conjunction with PDU method, for error reduction in images encoded using LIPQE (Local Phase Image Quantum Encoding) method.

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“…Computers like this are currently in operation and are being offered for researchers. The most known photonic processor from Xanadu, described by Somhorst et al in [6] or Madsen et al…”

Section: Introductionmentioning

confidence: 99%

Deep learning as a tool of quantum error reduction in quantum image processing

Werner,

Wereszczyński,

Potempa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Demonstration of a photonic indistinguishability witness on a programmable quantum photonic processor

Abstract: We experimentally demonstrate an efficient, semi-device-independent photonic indistinguishability witness by certifying three-photon indistinguishability on a programmable integrated quantum photonic processor.

Cited by 1 publication

References 7 publications

Deep learning as a tool of quantum error reduction in quantum image processing

Deep learning as a tool of quantum error reduction in quantum image processing

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