1 citations · 1 across the 10 of their papers we have counts for
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Proof of the hiding conjecture for Gaussian boson sampling with an arbitrary number of squeezed input modes
Laura Shou, Alexey V. Gorshkov, Victor Galitski +1
Gaussian boson sampling (GBS) is a sampling task proposed to demonstrate quantum advantage. We consider Gaussian boson sampling on optical modes, with equally squeezed inpu…
Quantum-stabilized patterns in a vector Hopfield network
Richard D. Barney, Sharba Bhattacharjee, Victor Galitski +2
We introduce the quantum vector Hopfield network, in which patterns are formed by orientations of quantum vector spins; quantum dynamics arise intrinsically from the non-commutativ…
Entanglement and circuit complexity in finite-depth random linear optical networks
Laura Shou, Joseph T. Iosue, Yu-Xin Wang +2
We study the growth of entanglement and circuit complexity in random passive linear optical networks as a function of the circuit depth. For entanglement dynamics, we start with an…
Measurement-Induced Quantum Neural Network
Paul Argyle, Djamil Lakhdar-Hamina, Sarah H. Miller +1
We introduce a measurement-induced quantum neural network (MINN), an adaptive monitored-circuit architecture in which mid-circuit measurement outcomes determine the entangling gate…
Proof of Hiding Conjecture in Gaussian Boson Sampling
Laura Shou, Sarah H. Miller, Victor Galitski
Gaussian boson sampling (GBS) is a promising protocol for demonstrating quantum computational advantage. One of the key steps for proving classical hardness of GBS is the so-called…
Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware
Djamil Lakhdar-Hamina, Xingxin Liu, Richard Barney +4
We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision.…