Tag: high-dimensional-optimization
All the articles with the tag "high-dimensional-optimization".
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On Quantum Algorithms for Neural-Network Training: A Conjecture on Fault-Tolerant Speedups
The Suhubdy Conjecture — a three-form mathematical conjecture for a fault-tolerant quantum algorithm that accelerates — or replaces — backpropagation for empirical-risk minimisation in neural networks. Positioned against the Baur–Strassen cheap-gradient bound, Jordan and Gilyén–Arunachalam–Wiebe gradient-estimation algorithms, Abbas et al. (NeurIPS 2023) impossibility, quantum SDP and interior-point solvers, the Zhang–Leng–Li and Garg–Kothari–Netrapalli–Sherif lower bounds, and the 2024–2026 line on quantum non-logconcave sampling. Companion arXiv-style preprint (.tex source and .pdf) provided.