27 citations · 100 across the 16 of their papers we have counts for
19 papers
Lithography-free in situ photonic inverse design
Yiqi Zhao, Martin M. Stein, Fan O. Wu +6
Inverse design has emerged as a powerful engineering approach in nanophotonics, leading to devices with unintuitive designs that extend beyond human intuition. However, inverse-des…
Programmable nonlinearity within nanophotonic waveguides
Benjamin A. Ash, Ryotatsu Yanagimoto, Mandar M. Sohoni +8
Nonlinear nanophotonics enables the engineering of optical functions using light-matter interactions in nanoscale structures with carefully designed geometries. Phase matching in n…
Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout
Tianyang Chen, Mandar M. Sohoni, Saeed A. Khan +5
Optical readout in low-light imaging is fundamentally limited by measurement noise, including photon shot noise, detector noise, and quantization error. In this regime, downstream…
Physical Foundation Models: Fixed hardware implementations of large-scale neural networks
Logan G Wright, Tianyu Wang, Tatsuhiro Onodera +1
Foundation models are deep neural networks (such as GPT-5, Gemini~3, and Opus~4) trained on large datasets that can perform diverse downstream tasks -- text and code generation, qu…
TRON: Trainable, architecture-reconfigurable random optical neural networks
Ziao Wang, Fei Xia, Logan G. Wright +5
Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates.…
Ultra-low-light computer vision using trained photon correlations
Mandar M. Sohoni, Jérémie Laydevant, Mathieu Ouellet +7
Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage o…
Machine vision with small numbers of detected photons per inference
Shi-Yuan Ma, Jérémie Laydevant, Mandar M. Sohoni +3
Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision sys…
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…
Solving the compute crisis with physics-based ASICs
Maxwell Aifer, Zach Belateche, Suraj Bramhavar +11
Escalating artificial intelligence (AI) demands expose a critical "compute crisis" characterized by unsustainable energy consumption, prohibitive training costs, and the approachin…
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…
Programmable on-chip nonlinear photonics
Ryotatsu Yanagimoto, Benjamin A. Ash, Mandar M. Sohoni +7
Nonlinear photonics uses coherent interactions between optical waves to engineer functionality that is not possible with purely linear optics. Traditionally, the function of a nonl…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
Training of Physical Neural Networks
Ali Momeni, Babak Rahmani, Benjamin Scellier +25
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…
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…
Arbitrary control over multimode wave propagation for machine learning
Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash +10
Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Inste…
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…
The hardware is the software
Jeremie Laydevant, Logan G. Wright, Tianyu Wang +1
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 h…