Speakers
Description
We present a large-scale experimental realization of quantum extreme learning machines (QELMs) on superconducting quantum processors, scaling up to 124 qubits and circuits with more than 5,000 two-qubit gates. While quantum reservoir approaches offer enhanced expressivity via high-dimensional Hilbert spaces, their scalability can be hindered by hardware noise and concentration phenomena that suppress input–output distinguishability. We overcome this limitation by identifying a principled operating regime that balances expressivity, complexity, and statistical resolvability, and exhibits strong transferability across system sizes and tasks. Furthermore, we introduce a local eigentask framework for noise-aware feature selection with polynomial overhead, enabling efficient extraction of statistically resolvable observables in large-scale settings. We validate our approach on time-series forecasting and satellite image classification, achieving competitive performance with strong classical baselines under realistic experimental conditions, including demonstrations on processors using up to 124 qubits. These results establish QELMs as a viable and scalable paradigm for quantum machine learning on near-term hardware, and delineate a practical route toward large-scale quantum information processing.
| Topic | Hybrid quantum-classical approaches for learning and optimization |
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| Additional Topic | Encoding and processing of data in quantum systems |