Exploring the non-convexity in machine learning using quantum-inspired optimization
The escalating complexity of modern machine learning necessitates solving challenging non-convex optimization problems, particularly in high-dimensional regimes and scenarios contaminated by gross outliers. Traditional approaches, relying on convex relaxations or specialized local search heuristics, frequently succumb to suboptimal local minima and fail to recover the true underlying discrete structures. In this paper, we propose treating these non-convex challenges as a global search problem and introduce a unified framework based on Quantum-Inspired Evolutionary Optimization (QIEO). By leveraging a probabilistic representation inspired by quantum superposition, QIEO maintains a global v...
| Authors | Kandula Eswara Sai Kumar, Parth Dhananjay Danve, Abhishek Chopra et al. |
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| Primary category | cs.CE |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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