2 citations · 3 across the 15 of their papers we have counts for
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A charge selection rule fixes what a squeezed-light reservoir computer can compute and afford
Daniel Soh
Reading an optical quantum reservoir costs repetitions growing super-exponentially with feature order: what it can afford is set by its detector, not its optics. For reservoirs enc…
A Single Atom in Front of a Mirror is a Universal Reservoir Computer
Peter J. Ehlers, Phi Hung Nguyen, Kanu Sinha +4
Universal approximation in reservoir computing is typically associated with a class of reservoirs. We show that universality can be associated with a single reservoir, considering…
Computational Superiority of Non-Markovian Kerr Feedback in Continuous-Variable Quantum Reservoir Computing
Daniel Soh
A linear optical medium can delay, mix, and superpose light, but never make two pulses multiply: multiplication is nonlinear, and a linear system has no such operation. This roots…
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…
Minimalistic and Scalable Quantum Reservoir Computing Enhanced with Feedback
Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin +1
Quantum Reservoir Computing (QRC) leverages quantum systems to perform complex computational tasks with exceptional efficiency and reduced energy consumption. We introduce a minima…
Optical Gottesman-Kitaev-Preskill qubit generation via approximate squeezed coherent state superposition breeding
Andrew J. Pizzimenti, Daniel Soh
Gottesman-Kitaev-Preskill (GKP) qubits, known for their exceptional error-correction capabilities, are highly coveted in quantum computing. However, generating optical GKP qubits h…