12 citations · 32 across the 6 of their papers we have counts for
7 papers
Operator-language Feynman rules for driven-dissipative quantum systems: from mean field to non-Gaussian photon correlations
Peter Ehlers, Phi Hung Nguyen, Daniel Soh
We construct Feynman rules for Lindblad master equations in the operator language of quantum optics: a fixed algebra of elementary operator moves and closed-form propagators for pe…
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…
Optical Echo State Network Reservoir Computing
Ishwar S Kaushik, Peter J Ehlers, Daniel Soh
We propose an innovative design for an optical Echo State Network (ESN), an advanced type of reservoir computer known for its universal computational capabilities. Our design enabl…
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…
Stochastic Reservoir Computers
Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh
Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner when compared to typical neural netw…
Practical Few-Atom Quantum Reservoir Computing
Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin +1
Quantum Reservoir Computing (QRC) harnesses quantum systems to tackle intricate computational problems with exceptional efficiency and minimized energy usage. This paper presents a…
Improving the Performance of Echo State Networks Through State Feedback
Peter J. Ehlers, Hendra I. Nurdin, Daniel Soh
Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time serie…