Versatile Quantum Machine Learning with an Ultra-low Power Photonic Quantum Reservoir Computer

arXiv:2609.24381 · quant-ph, physics.optics · Submitted 2026-09-21 · Read on arXiv

quant-ph, physics.optics

Submitted: 2026-09-21

Updated: 2026-09-21

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 21× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Feature Extraction — 6× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Digital Twin — 1× in this paper · explained in 4 episodes
• Temporal Correlations — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid
• Quantum reservoir computing with repeated measurements on superconducting devices

Related papers

• Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
• Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
• Polynomial-time classical and quantum simulation of quantum impurity models
• A quasiparticle-protected superconducting qubit
• Theory of quantum-enhanced interferometry with general Markovian light sources
• A convergent hierarchy of spectral gap certificates for qubit Hamiltonians