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International Conference on Emerging Smart Computing and Informatics (ESCI)

Quantum-Inspired Optimization Algorithms for Large-Scale Machine Learning Tasks

Satyawan Changadev Hembade, Sagarkumar S. Badhiye, Madhuri Ninawe, Piyush Pal, Payal Sunil Lahane, Amol Kadam

Published Mar 11, 2026• 0 citations
Publisher
DOI
10.1109/esci68015.2026.11493352

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Large-scale machine learning (ML) models, particularly deep neural networks, often present significant challenges for traditional optimization algorithms. These algorithms, rooted in classical mechanics, frequently encounter issues such as getting trapped in local optima, high computational cost, and difficulty in handling complex, non-convex landscapes. This paper investigates the application of quantum-inspired optimization techniques to address these challenges. We explore how concepts derived from quantum phenomena – such as quantum annealing and quantum walks – can be adapted to efficiently navigate and optimize large-scale ML models. The core idea is to leverage the probabilistic and superposition properties inherent in quantum mechanics to escape local optima and accelerate the convergence process. We present a framework for designing and implementing quantum-inspired optimization strategies, focusing on the theoretical foundations and potential benefits. The proposed approach aims to provide a novel and effective solution for optimizing complex ML models, ultimately improving model performance and reducing training time. This work contributes to the growing field of quantum machine learning and offers a promising avenue for tackling the optimization problems inherent in large-scale ML.
The rapid growth of machine learning (ML) has been accompanied by a significant challenge: the scaling of optimization algorithms to handle the immense parameter spaces of modern models, such as deep neural networks. Traditional optimization methods, including gradient descent and its variants, often encounter difficulties in efficiently exploring these spaces, leading to slow training times and potential stagnation. This paper investigates the application of quantum-inspired optimization techniques to address this scaling problem. We explore algorithms inspired by quantum annealing and other quantum phenomena, adapting their core principles to classical computation. Specifically, we propose a novel optimization framework utilizing a stochastic search strategy based on quantum superposition and measurement, designed to navigate high-dimensional parameter spaces more effectively. The efficacy of this approach is evaluated through theoretical analysis and conceptual simulations, demonstrating its potential to accelerate the training of large-scale machine learning models. We demonstrate how these methods can improve convergence speed and potentially escape local minima more effectively than conventional techniques. The core contribution lies in providing a theoretical foundation and a practical framework for applying quantum-inspired concepts to optimization within the context of machine learning.
Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time O(D)O(D)O(D) in the data dimension DDD, whereas a naive classical implementation relies on handling exponentially many candidate nodes and hence takes exp⁡[O(D)]\exp[O(D)]exp[O(D)] time. In this work, we construct and analyze a quantum-inspired fully classical algorithm for the same sampling task. We show that our algorithm runs in time O(poly⁡(D))O(\operatorname{poly}(D))O(poly(D)), thereby removing the exponential dependence on DDD from the previous classical approach. Numerical simulations show that the proposed sampler achieves empirical risk comparable to exact sampling from the optimized distribution and substantially lower than sampling from the non-optimized uniform distribution, while also exhibiting exponentially improved runtime scaling compared with the conventional classical implementation. These successful dequantization results show that sparse subnetwork selection via optimized sampling can be achieved classically with polynomial data-dimension scaling on conventional computers without quantum hardware, providing an alternative to the existing quantum algorithm.