Variational Quantum Unsampling on an Photonic Quantum Processor
ORAL
Abstract
Quantum algorithms for Noisy Intermediate-Scale Quantum (NISQ) processors have emerged as promising routes towards demonstrating practical advantage over classical machines. In these systems samples are typically drawn from probability distributions which — under plausible complexity-theoretic conjectures — cannot be efficiently generated classically. Rather than first define a physical system and then determine computational features of the output state, we ask the converse question: given direct access to the quantum state, what features of the generating system can we efficiently learn? Here, we introduce the Variational Quantum Unsampling (VQU) protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuits outputs. We experimentally demonstrate this protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications; including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation.
*This work was supported by the AFOSR MURI for Optimal Measurements for Scalable Quantum Technologies (FA9550-14-1-0052) and by the AFOSR program FA9550-16-1-0391, supervised by Gernot Pomrenke. J.C. is supported by EU H2020 Marie Sklodowska-Curie grant number 751016.
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Presenters
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Jacques Carolan
- Research Laboratory of Electronics, Massachusetts Institute of Technology
- Massachusetts Institute of Technology MIT