Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.
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Simulations show CMA-ES outperforms Nelder-Mead and other algorithms for quantum device calibration across low- and high-dimensional regimes.
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Non-Clifford Benchmarking via Ensemble Feature Selection
Ensemble Feature Selection trains a ridge-regression linear estimator on an ensemble of noisy channels to estimate process infidelity of non-Clifford gates, validated against IRB on IBM hardware with 0.01 precision over 0.02-0.2 infidelity range.
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Benchmarking Optimization Algorithms for Automated Calibration of Quantum Devices
Simulations show CMA-ES outperforms Nelder-Mead and other algorithms for quantum device calibration across low- and high-dimensional regimes.