Open quantum systems as a platform for quantum machine learning

Quantum machine learning (QML) is an emerging field with significant applications, even on current noisy intermediate-scale quantum (NISQ) computers. An alternative to gate-based QML is quantum reservoir computing (QRC), which utilizes the dynamics of complex physical systems for computation. While classical reservoir computing has been well-studied, its quantum counterpart is gaining attention due to advantages like exponential phase-space scaling and entanglement. This presentation introduces QRC, discussing measures of expressivity, quantumness, and performance, with a focus on dephasing and quantum measurement impacts. It also explores implementations using interconnected nanolasers in quantum-photonic neural networks.