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A 4,500-Qubit Annealer Remembered a Time Series
Untrained reverse-annealing dynamics processed temporal data without optimizing thousands of quantum parameters.
Summary
Untrained reverse-annealing dynamics processed temporal data without optimizing thousands of quantum parameters.
A quantum reservoir computer used the native many-body dynamics of a programmable superconducting annealer to process temporal data with as many as 4,500 qubits. The team proves that the annealer’s interactions are necessary for memory in this construction, then tests the hardware on standard memory tasks and chaotic time-series forecasting. The experiment is presented as the largest quantum-machine-learning run to date. It does not show an advantage over the best classical forecasting systems, and its contribution is scale and a train-light reservoir design.
Why it matters
Untrained reverse-annealing dynamics processed temporal data without optimizing thousands of quantum parameters.
Limits and context
- It does not show an advantage over the best classical forecasting systems, and its contribution is scale and a train-light reservoir design.
Key claims
Untrained reverse-annealing dynamics processed temporal data without optimizing thousands of quantum parameters.
Qualification: It does not show an advantage over the best classical forecasting systems, and its contribution is scale and a train-light reservoir design.
Evidence: source-2026-09-20-014
Sources
- arXiv preprint 2609.19308arXiv · primary research
Corrections
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