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

The Energy Conserving Descent (ECD) algorithm was recently proposed (De Luca & Silverstein, 2022) as a global non-convex optimization method. Unlike gradient descent, appropriately configured ECD dynamics escape strict local minima and converge to a global minimum, making it appealing for machine learning optimization. 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. For positive double-well objectives, we compute the expected hitting time from a local to the global minimum. We prove that both sECD and qECD yield exponential speedup over respective gradient descent baselines--stochastic gradient descent and its quantization. For objectives with tall barriers, qECD achieves a further speedup over sECD.

Automatic Knowledge Extraction with Human Interface

OrbWeaver, an automatic knowledge extraction system paired with a human interface, streamlines the use of unintuitive natural language processing software for modeling systems from their documentation. OrbWeaver enables the indirect transfer of knowledge about legacy systems by leveraging open source tools in document understanding and processing as well as using web based user interface constructs. By design, OrbWeaver is scalable, extensible, and usable; we demonstrate its utility by evaluating its performance in processing a corpus of documents related to advanced persistent threats in the cyber domain. The results indicate better knowledge extraction by revealing hidden relationships, linking co-related entities, and gathering evidence.