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Peter McMahon on Muck Rack

Peter McMahon

As seen in: Nature, Eyes on Earth, IEEE Spectrum, ACS Publications, Science Magazine, Cell Press, AskMen, Calgary Herald, Omaha World-Herald, Buffalo News and
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Articles

Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering

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By Saeed Khan, Sridhar Prabhu, Logan Wright, Peter McMahon
| Nature Verified
Abstract Combining quantum sensing with quantum computing can lead to quantum computational sensing (QCS) protocols that are able to more efficiently extract task-specific information from physical signals than is possible otherwise. In this paper, we present, in theory and numerical simulations, the application of two quantum algorithms—quantum signal processing and quantum neural networks—to various binary and multiclass machine-learning classification tasks in sensing.
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Large-scale quantum reservoir computing using a Gaussian Boson Sampler

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By Valeria Cimini, Mandar M. Sohoni, Federico Presutti, Benjamin K. Malia, Shi-Yuan Ma, Ryotatsu Yanagimoto, Tianyu Wang, Tatsuhiro Onodera, Logan Wright, Peter McMahon
| Nature Verified
Abstract A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at a large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with > 400 modes as a quantum reservoir. We evaluated the accuracy of our GBS-based reservoir computer on a variety of benchmark tasks.
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Training of physical neural networks - Nature

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By Ali Momeni, Babak Rahmani, Benjamin Scellier, Peter McMahon, Yuhang Li, Ilker Oguz, Francesco Morichetti, Philipp del Hougne, Manuel Le Gallo, Abu Sebastian, Azalia Mirhoseini, Danijela Marković, Daniel Brunner, Christophe Moser, Sylvain Gigan, Florian Marquardt, Aydogan Ozcan, Julie Grollier, Andrea Liu, Demetri Psaltis, Andrea Alu
| Nature Verified
Abstract Physical neural networks (PNNs) are a class of neural-like networks that make use of analogue physical systems to perform computations. Although at present confined to small-scale laboratory demonstrations, PNNs could one day transform how artificial intelligence (AI) calculations are performed. Could we train AI models many orders of magnitude larger than present ones? Could we perform model inference locally and privately on edge devices?
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