Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Incremental improvement to quantum fast-weight programmers for time-series tasks, offering modest stability and performance gains.
Self-Modulating QFWP improves convergence stability and prediction performance for quantum sequential learning by adaptively balancing new information and memory retention, with numerical gains across varying qubit counts and sequence lengths.
Recent advances in quantum machine learning have motivated efficient models for sequential data processing. In this paper, we propose Self-Modulating Quantum Fast Weight Programmers, or Self-Modulating QFWP, which extends Quantum Fast Weight Programmers by introducing adaptive modulation over both newly generated fast-weight updates and historical fast-weight memory. Numerical results show that the proposed mechanism improves convergence stability and prediction performance across varying model settings, including different numbers of qubits and input sequence lengths. We further provide theoretical arguments explaining how self-modulation balances new information injection with memory retention, thereby enhancing temporal information propagation. These results suggest that Self-Modulating QFWP is a compact and effective framework for quantum machine learning on time-series data.