A Programmable Linear Optical Quantum Reservoir with Measurement Feedback for Time Series Analysis

TL;DR

This paper introduces a scalable linear optical quantum reservoir computing architecture with measurement feedback, demonstrating effective time-series analysis and nonlinear forecasting using current photonic technology.

Contribution

It presents a novel, hardware-friendly quantum reservoir design utilizing measurement-conditioned feedback and coarse-grained features for enhanced temporal processing.

Findings

Memory performance peaks near the stability boundary.

Achieves competitive accuracy on benchmark time-series tasks.

Compatible with existing photonic technology.

Abstract

Feedback-driven quantum reservoir computing has so far been studied primarily in gate-based architectures, motivating alternative scalable, hardware-friendly physical platforms. Here we investigate a linear-optical quantum reservoir architecture for time-series processing based on multiphoton interference in a reconfigurable interferometer network equipped with threshold detectors and measurement-conditioned feedback. The reservoir state is constructed from coarse-grained coincidence features, and the feedback updates only a structured, budgeted subset of programmable phases, enabling recurrence without training internal weights. By sweeping the feedback strength, we identify three dynamical regimes and find that memory performance peaks near the stability boundary. We quantify temporal processing via linear memory capacity and validate nonlinear forecasting on benchmarks, namely Mackey-Glass series, NARMA$-n$ and non-integrable Ising dynamics. The proposed architecture is compatible with current photonic technology and lowers the experimental barrier to feedback-driven QRC for time-series analysis with competitive accuracy.