The paper establishes the first finite-time convergence rate of 1/T^{2/13} for classical Adam (with bias correction, no extra steps) in nonsmooth nonconvex optimization under heavy-tailed noise with β1=β2.
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DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.
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Adam Converges in Nonsmooth Nonconvex Optimization
The paper establishes the first finite-time convergence rate of 1/T^{2/13} for classical Adam (with bias correction, no extra steps) in nonsmooth nonconvex optimization under heavy-tailed noise with β1=β2.
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Dissipative Quantum Multiplicative Weights with Sampling Feedback: A Classically Hard Primitive Realized via Engineered Open-System Dynamics
DQMW-Sample realizes a classically hard online learning primitive via dissipative quantum dynamics with sublinear regret and proven hardness for classical simulation including PH collapse.