QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
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A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.
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Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage
QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
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Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
A gate freezing method improves convergence of gradient-free optimizers Rotosolve, Fraxis, and FQS for parameterized quantum circuits by reallocating resources to poorly optimized gates using previous iteration information.