Breaking concentration barriers for quantum extreme learning on digital quantum processors

• URL: http://arxiv.org/abs/2603.13005v1
• Date: Fri, 13 Mar 2026 14:10:09 GMT
• Title: Breaking concentration barriers for quantum extreme learning on digital quantum processors
• Abstract summary: We present and experimentally demonstrate a Quantum Extreme Learning Machine (QELM) tailored to state-of-the-art superconducting platforms.<n>QELM employs up to 124 qubits and circuits with more than 5,000 two-qubit gates on IBM Quantum computers.<n>We develop a local eigentask analysis that enables computationally efficient feature selection and effective information retrieval.
• Score: 0.7830152529643848
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their practical applicability is hindered by hardware noise and concentration effects that can erase input-output distinguishability at large system sizes. In this work, we present and experimentally demonstrate a Quantum Extreme Learning Machine (QELM) tailored to state-of-the-art superconducting platforms, employing up to 124 qubits and circuits with more than 5,000 two-qubit gates on IBM Quantum computers. We introduce a practical multi-objective hyperparameter tuning strategy that jointly monitors observable variability, capacity, and task performance to identify noise-robust operating points. In addition, we develop a local eigentask analysis that enables computationally efficient feature selection and effective information retrieval. We report evidence of a regime of optimality that is identifiable at small scales and transferable across tasks and larger systems, and we achieve performances competitive with leading classical baselines on representative benchmarks for time-series forecasting and satellite image classification. Together, our results establish a viable and robust framework for large-scale, pre-fault-tolerant quantum machine learning and provide a foundation for extending reservoir-based methods to more expressive architectures and real-world scenarios.

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