Quantum Gaussian Processes Extended for Predicting Channel Observations

Researchers have developed a new framework for Quantum Gaussian Processes (QGPs) that extends their predictive capabilities beyond unitary quantum dynamics. This extended QGP framework can predict the expectation value of a Pauli observable at the output of an unknown quantum channel, even with limited measurements. The study proves the convergence of channel outputs to a QGP and derives a closed-form kernel, though its dimensional factor can limit learning for larger systems. To address this, an empirical Bayes heuristic is proposed to replace the dimensional factor with a learnable scale parameter, restoring learnability for global channels and improving predictions with increased shot budgets, as demonstrated in simulations up to 64 qubits. AI

IMPACT Extends predictive modeling capabilities for quantum systems, potentially aiding in the analysis of complex quantum evolutions and state preparation.

RANK_REASON Academic paper detailing a new methodology in quantum machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• Bayesian optimization
• Lebesgue measure
• Pauli observable
• Quantum channels
• quantum evolution
• Quantum Gaussian process
• XXZ dynamics

• paper
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