27 citations · 100 across the 16 of their papers we have counts for
6 papers · 1 filter
Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity
Chuanzhou Zhu, Tianyu Wang, Peter L. McMahon +1
Optical neural networks (ONNs) have been developed to enhance processing speed and energy efficiency in machine learning by leveraging optical devices for nonlinear activation and…
Large-scale quantum reservoir computing using a Gaussian Boson Sampler
Valeria Cimini, Mandar M. Sohoni, Federico Presutti +7
A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at large scale in optics. Here we report on experiments in which we used a fre…
Exponential advantage in quantum sensing of correlated parameters
Sridhar Prabhu, Vladimir Kremenetski, Saeed A. Khan +2
Conventionally in quantum sensing, the goal is to estimate one or more unknown parameters that are assumed to be deterministic - that is, they do not change between shots of the qu…
Hamiltonian-reconstruction distance as a success metric for the Variational Quantum Eigensolver
Leo Joon Il Moon, Mandar M. Sohoni, Michael A. Shimizu +4
The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for quantum simulation that can be run on near-term quantum hardware. A challenge in VQE -- as wel…
Tunable spectral correlations of highly multimode visible light via broadband quantum frequency conversion
Federico Presutti, Logan G. Wright, Shi-Yuan Ma +4
Multimode squeezed states of light are a resource for achieving quantum advantage in computing and sensing, where spatial or temporal modes have been the experimental norm. In our…
Microwave signal processing using an analog quantum reservoir computer
Alen Senanian, Sridhar Prabhu, Vladimir Kremenetski +8
Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required…