Peter McMahon
@peterlmcmahon
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Researcher working on the physics of computation, including quantum, optical, and neuromorphic computing (not necessarily all at the same time!).
Joined March 2019
The application form is available at . The ACP summer workshops are largely intended for theorists, although experimentalists are also welcome to apply. 2/2
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Randles & van Enk have a pedagogical gem of a paper on the arXiv tonight, explaining multiphoton interference effects in a combination of Mach-Zehnder and Hong-Ou-Mandel-type interferometers in terms of Feynman paths:
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Our work on quantum-limited stochastic optical neural networks using just ~1 photon per neuron activation, described in when we released a preprint, is out in final form now in Nature Communications:
The energy efficiency of computing is ultimately limited by noise, with quantum noise as the fundamental floor. What happens if we operate an optical neural network with such low power that each neuron activation is caused by just a single photon? 1/5
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Hopfield's work on Ising Machines for optimization has had an enormous influence on the field, arguably founding it, and it's great to see it be recognized in the Nobel announcement!
Also great to see Ising Machines getting a citation in the extended press release (Ref. 21 where our brilliant colleague @peterlmcmahon is a co-author):
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Our work on quantum reservoir computing with microwave signals, described in when we released a preprint last December, is out in final form now in Nature Communications:
*Is there a path to achieving a quantum computational advantage for machine learning with NISQ systems on problems of practical interest?* The community consensus seems to be _no_ for problems involving classical data (which I agree with). 1/
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Call for Papers: ML with New Compute Paradigms (MLNCP) Workshop at NeurIPS 2024.
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*Call for papers* for _AI and Optical Data Sciences VI_ @ Photonics West 2025 in San Francisco, deadline approaching: 17 July 2024.
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My lab is looking to recruit a postdoc on the topic of accelerating deep-neural-network training using unconventional (analog) hardware. If you have experience in deep learning, or analog-electronic/photonic/other-physical neural networks, please feel free to email me your CV!
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*CLEO 2024* Below are the 3 talks being presented by our lab at @CLEOConf this week, on optical neural networks - both free-space and integrated - and multimode squeezed light. Please feel free to come say "hi" if you see @MartinMStein, Federico or me at the coffee breaks!
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Daniel Brunner (@dbrunner_lab) has written a great summary of the revival of *integrated holographic photonics* aka *lithography-free photonics*, covering recent work from UPenn (x2), our group (, and FEMTO-ST/EPFL:
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Our paper is out on the arXiv today: If you'd like to try out our method, our code is available at Congratulations to co-first authors Leo Moon and @MandarSohoni2, and the rest of the team! 3/
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Also, a shout out to @MandarSohoni2 for all his hard work on recording, preparing, and uploading the videos! 2/
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Our paper is on the arXiv tonight: Congratulations to co-first authors @tatsuhiro_onod and @MartinMStein, as well as @LoganGWright1, and the rest of the team! 11/
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