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Updated: May 5, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
Published on: May 30, 2014
Programmable 200 GOPS Hopfield-inspired photonic Ising machine
Nayem Al-Kayed1,2, Charles St-Arnault2,3, Hugh Morison1,2
1Centre for Nanophotonics, Department of Physics, Engineering Physics, and Astronomy, Queen's University, Kingston, Ontario, Canada.
Abstract:
Ising machines offer a compelling approach to addressing NP-hard problems1, but physical realizations that are simultaneously scalable, reconfigurable, fast and stable remain elusive. Quantum annealers, such as D-Wave's cryogenic hardware, target combinatorial optimization tasks, but quadratic scaling of qubit requirements with problem size limits their scalability on dense graphs2. Here we introduce a programmable, stable, room-temperature optoelectronic oscillator (OEO)-based Ising machine with linear scaling in spin representation. Inspired by Hopfield networks3, our architecture solves fully connected problems with up to 256 spins (65,536 couplings) and >41,000 spins (205,000+ couplings) if sparse. Our system makes use of cascaded thin-film lithium niobate (TFLN) modulators, a semiconductor optical amplifier (SOA) and a digital signal processing (DSP) engine in a recurrent time-encoded loop, demonstrating potential >200 giga operations per second (GOPS) for spin coupling and nonlinearity. This platform achieves the largest spin configuration in an OEO-based photonic Ising machine, enabled by high intrinsic speed. We experimentally demonstrate best-in-class solution quality for max-cut problems of arbitrary graph topologies (2,000 and 20,000 spins) among photonic Ising machines and obtain ground-state solutions for number partitioning4 and lattice protein folding5-benchmarks previously unaddressed by photonic systems. Our system uses inherent noise from high baud rates to escape local minima and accelerate convergence. Finally, we show that embedding DSP-traditionally used in optical communications-within optical computation enhances convergence and solution quality, opening new frontiers in scalable, ultrafast computing for optimization, neuromorphic processing and analogue artificial intelligence.
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