Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent
Not on the map. Why?
Abstract (arXiv)
We present the first analytical study of ECD, focusing on the one-dimensional setting for this first installment. We formalize a stochastic ECD dynamics (sECD) with energy-preserving noise, as well as a quantum analog of the ECD Hamiltonian (qECD), providing the foundation for a quantum algorithm through Hamiltonian simulation in a tractable model where the barrier-crossing mechanism can be computed explicitly. For one-dimensional double-well objectives in the under-guessing regime, we compute the expected dynamical hitting times from a local minimum to the global minimum. We prove that both sECD and qECD exhibit exponential improvements in continuous hitting time relative to their respective gradient-based baselines, stochastic gradient descent (SGD) and quantum tunneling walk (QTW). For objectives with tall barriers, qECD admits a further hitting time improvement over sECD. Mechanistically, ECD sidesteps the exponential cost associated with rare-escape events of SGD from local minima by moving from dissipative to energy-conserving dynamics.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 14 Apr 2026.
Builds on
6 of 16 references.
References · 6
- 2018An Alternative View: When Does SGD Escape Local Minima?cited by 115
- 2020On Learning Rates and Schr"odinger Operatorscited by 33
- 2021Power-law escape rate of SGDcited by 29
- 2022Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimizationcited by 10
- 2022On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walkscited by 9
- 2023Improving Energy Conserving Descent for Machine Learning: Theory and Practicecited by 3
Built on by
Not cited yet.