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Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
We propose a novel algorithm that extends the methods of ball smoothing and Gaussian smoothing for noisy derivative-free optimization by accounting for the heterogeneous curvature of the objective function.
Hyperspectral In-Memory Computing with Optical Frequency Combs and Programmable Optical Memories
The rapid advancements in machine learning across numerous industries have amplified the demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing architectures.
Efficient sampling of ground and low-energy Ising spin configurations with a coherent Ising machine
We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model.
Quantum Physics Optics
