118 citations · 473 across the 22 of their papers we have counts for
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Gated InAs quantum dots embedded in surface acoustic wave cavities for low-noise optomechanics
Zixuan Wang, Ryan A. DeCrescent, Poolad Imany +5
Self-assembled InAs quantum dots (QDs) are promising optomechanical elements due to their excellent photonic properties and sensitivity to local strain fields. Microwave-frequency…
Programmable Superconducting Optoelectronic Single-Photon Synapses with Integrated Multi-State Memory
Bryce A. Primavera, Saeed Khan, Richard P. Mirin +2
The co-location of memory and processing is a core principle of neuromorphic computing. A local memory device for synaptic weight storage has long been recognized as an enabling el…
Monolithic Integration of Superconducting-Nanowire Single-Photon Detectors with Josephson Junctions for Scalable Single-photon Sensing
Saeed Khan, Bryce A. Primavera, Richard P. Mirin +2
We demonstrate superconducting single-photon detectors that integrate signals locally at each pixel. This capability is realized by the monolithic integration of superconducting-na…
Monolithic Polarizing Circular Dielectric Gratings on Bulk Substrates for Improved Photon Collection from InAs Quantum Dots
Ryan A. DeCrescent, Zixuan Wang, Poolad Imany +3
III-V semiconductor quantum dots (QDs) are near-ideal and versatile single-photon sources. Because of the capacity for monolithic integration with photonic structures as well as op…
A 64-pixel mid-infrared single-photon imager based on superconducting nanowire detectors
Benedikt Hampel, Richard P. Mirin, Sae Woo Nam +1
A large-format mid-infrared single-photon imager with very low dark count rates would enable a broad range of applications in fields like astronomy and chemistry. Superconducting n…
A superconducting-nanowire single-photon camera with 400,000 pixels
Bakhrom G. Oripov, Dana S. Rampini, Jason Allmaras +4
For the last 50 years, superconducting detectors have offered exceptional sensitivity and speed for detecting faint electromagnetic signals in a wide range of applications. These d…
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh +3
We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware. MGD utilizes zero-order optimiza…