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Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent

arXiv 2604.13022 ↗First posted 14 Apr 2026quant-ph cs.LG math.OCcited by 0

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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.

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References · 6

  1. 2018An Alternative View: When Does SGD Escape Local Minima?cited by 115
  2. 2020On Learning Rates and Schr"odinger Operatorscited by 33
  3. 2021Power-law escape rate of SGDcited by 29
  4. 2022Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimizationcited by 10
  5. 2022On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walkscited by 9
  6. 2023Improving Energy Conserving Descent for Machine Learning: Theory and Practicecited by 3

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