Quantum neuroplastic learning for time-series processing

Abstract

Quantum machine learning uses quantum states and dynamics to represent and process data. In quantum reservoir computing, the physical system is typically fixed and a classical readout is trained. Inspired by the formation, strengthening and pruning of synapses during biological learning, we propose quantum neuroplastic learning (QNL), which reorganizes the couplings among the qubits of a quantum reservoir, much as experience rewires connections among neurons. Eligibility traces guide local edits to couplings, fields, gate angles and evolution time, and an edit is retained when a statistical bound on its improvement exceeds a complexity cost. We evaluate QNL under restarting and online monitored protocols on short- and long-term memory, nonlinear autoregressive processing and forecasting, and channel equalization tasks. In simulation, QNL enhances memory capacity, develops long-term recall and sustains adaptation across multiple tasks, with the largest benefit for tasks that require extended memory. On the IBM superconducting processor \texttt{ibm\_fez}, with six seven-qubit reservoirs running in parallel on 42 qubits (1.28 million shots in total), configurations learned in simulation and frozen before execution reduced the channel-equalization symbol error rate from 9.7\% to 2.2\% and achieved a lower error rate than a matched-budget tuned baseline.