Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.
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GPU port of entropy-stable DG Euler solver with non-conservative buoyancy terms reaches nearly 70% of 64-bit peak on A100 volume kernels, delivers 10x speedup and 13x better energy efficiency versus CPU, and preserves symmetry-based flux savings.
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Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.
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GPU Performance of an Entropy-Stable Discontinuous Galerkin Euler Solver with Non-Conservative Terms
GPU port of entropy-stable DG Euler solver with non-conservative buoyancy terms reaches nearly 70% of 64-bit peak on A100 volume kernels, delivers 10x speedup and 13x better energy efficiency versus CPU, and preserves symmetry-based flux savings.