Speaker
Description
Quantum reservoir computing offers a near-term route to temporal learning by using fixed quantum dynamics as a processor and training only a classical readout. However, moving from classical to quantum reservoirs introduces two central difficulties: the need for an input-update mechanism that provides nonlinearity and fading memory, and the tension between extracting observables and preserving the quantum state that stores temporal information. In this extended abstract, based on two recent works, we present measurements as a design principle to address both issues. Mid-circuit measurement and reset implement the trace-and-replace update required by amplitude encoding, yielding an online protocol in which the input register is refreshed while the memory register carries information forward. At the same time, indirect measurements of the memory register provide online access to reservoir observables without projectively measuring the memory state. We show that their strength should not simply be minimized: finite measurement backaction can enhance temporal-processing performance, this advantage persists under finite measurement resources, and can outperform feedback-based strategies on memory tasks. These ingredients are combined into a dynamic-circuit implementation of online amplitude-encoded quantum reservoir computing, demonstrated on superconducting quantum hardware. Overall, the results support a view of measurement not only as a readout mechanism, but as an architectural resource for quantum temporal learning.
| Topic | Quantum algorithms for machine learning applications |
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