Programmable Coherent Memory Kernels for Quantum Reservoir Computing in Waveguide QED
quant-ph
Submitted: 2026-09-22
Updated: 2026-09-22
License: http://creativecommons.org/licenses/by/4.0/
The gist: Reservoir computing requires not only long memory, but also memory distributed over the timescales relevant to a given task.
Terminology
Abstract
Reservoir computing requires not only long memory, but also memory distributed over the timescales relevant to a given task. In most quantum reservoir computing architectures, however, the memory profile emerges from intrinsic dynamics and cannot be directly engineered. Here we introduce a waveguide-QED quantum reservoir in which coherent memory return is controlled by the coupling geometry. The reservoir consists of a driven Kerr-nonlinear resonator coupled to a one-dimensional waveguide at spatially separated points. An unmeasured propagating field carries information about past inputs and coherently returns it to the resonator after a finite propagation time, forming a non-Markovian feedback channel without intermediate measurement or classical reinjection. Delay-resolved memory benchmarks reveal two complementary functions: waveguide propagation determines when past information returns, whereas Kerr dynamics convert the returned field into nonlinear temporal features accessible through a linear readout. NARMA- n prediction shows that the optimal feedback delay shifts systematically with task order and approximately tracks the explicit input-product lag. These results establish coherent delayed feedback as a mechanism for engineering task-relevant memory in quantum reservoir computing.
Sources
- Quantum Reservoir Computing for Statistical Classification in a Superconducting Quantum Circuit
- An introduction to reservoir computing
- Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing
- Giant-atom quantum acoustodynamics in hybrid superconducting-phononic integrated circuits
- Efficient three-qubit gates with giant atoms
- QwaveMPS: An efficient open-source Python package for simulating non-Markovian waveguide-QED using matrix product states
- SeeMPS: A Python-based Matrix Product State and Tensor Train Library
- Memory-Nonlinearity Trade-off across Quantum Reservoir Computing Frameworks
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