Zobrazeno 1 - 10
of 502
pro vyhledávání: '"McMahon, P. L."'
Autor:
Momeni, Ali, Rahmani, Babak, Scellier, Benjamin, Wright, Logan G., McMahon, Peter L., Wanjura, Clara C., Li, Yuhang, Skalli, Anas, Berloff, Natalia G., Onodera, Tatsuhiro, Oguz, Ilker, Morichetti, Francesco, del Hougne, Philipp, Gallo, Manuel Le, Sebastian, Abu, Mirhoseini, Azalia, Zhang, Cheng, Marković, Danijela, Brunner, Daniel, Moser, Christophe, Gigan, Sylvain, Marquardt, Florian, Ozcan, Aydogan, Grollier, Julie, Liu, Andrea J., Psaltis, Demetri, Alù, Andrea, Fleury, Romain
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one
Externí odkaz:
http://arxiv.org/abs/2406.03372
Autor:
Roy, Saswata, Senanian, Alen, Wang, Christopher S., Wetherbee, Owen C., Zhang, Luojia, Cole, B., Larson, C. P., Yelton, E., Arora, Kartikeya, McMahon, Peter L., Plourde, B. L. T., Royer, Baptiste, Fatemi, Valla
Spins and oscillators are foundational to much of physics and applied sciences. For quantum information, a spin 1/2 exemplifies the most basic unit, a qubit. High angular momentum spins (HAMSs) and harmonic oscillators provide multi-level manifolds (
Externí odkaz:
http://arxiv.org/abs/2405.15695
Autor:
Moon, Leo Joon Il, Sohoni, Mandar M., Shimizu, Michael A., Viswanathan, Praveen, Zhang, Kevin, Kim, Eun-Ah, McMahon, Peter L.
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 well as any other heuristic algorithm for finding ground states of Hamilt
Externí odkaz:
http://arxiv.org/abs/2403.11995
Autor:
Onodera, Tatsuhiro, Stein, Martin M., Ash, Benjamin A., Sohoni, Mandar M., Bosch, Melissa, Yanagimoto, Ryotatsu, Jankowski, Marc, McKenna, Timothy P., Wang, Tianyu, Shvets, Gennady, Shcherbakov, Maxim R., Wright, Logan G., McMahon, Peter L.
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip photonics i
Externí odkaz:
http://arxiv.org/abs/2402.17750
Autor:
Presutti, Federico, Wright, Logan G., Ma, Shi-Yuan, Wang, Tianyu, Malia, Benjamin K., Onodera, Tatsuhiro, McMahon, Peter L.
Multimode squeezed states of light have been proposed as a resource for achieving quantum advantage in computing and sensing. Recent experiments that demonstrate multimode Gaussian states to this end have most commonly opted for spatial or temporal m
Externí odkaz:
http://arxiv.org/abs/2401.06119
Autor:
Senanian, Alen, Prabhu, Sridhar, Kremenetski, Vladimir, Roy, Saswata, Cao, Yingkang, Kline, Jeremy, Onodera, Tatsuhiro, Wright, Logan G., Wu, Xiaodi, Fatemi, Valla, McMahon, Peter L.
Publikováno v:
Nature Communications 15, 7490 (2024)
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 runs of the quantum processor and takes place in the classical domain,
Externí odkaz:
http://arxiv.org/abs/2312.16166
Publikováno v:
Phys. Rev. A 109, 062616 (2024)
A variational quantum algorithm for numerically solving partial differential equations (PDEs) on a quantum computer was proposed by Lubasch et al. In this paper, we generalize the method introduced by Lubasch et al. to cover a broader class of nonlin
Externí odkaz:
http://arxiv.org/abs/2311.01531
Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of human biological "hardware" is distinct, neuromorphic engineers need to
Externí odkaz:
http://arxiv.org/abs/2310.18335
Autor:
McMahon, Peter L.
Publikováno v:
Nature Reviews Physics (2023)
There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has be
Externí odkaz:
http://arxiv.org/abs/2308.00088
Analog physical neural networks, which hold promise for improved energy efficiency and speed compared to digital electronic neural networks, are nevertheless typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR)
Externí odkaz:
http://arxiv.org/abs/2307.15712