Quantum-Inspired Optimization of Machine Learning Model Architectures

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

The selection of an optimal machine learning (ML) model architecture is a critical, yet often computationally expensive, process. Traditional approaches frequently rely on manual design, grid search, or evolutionary algorithms, which can become intractable as model complexity increases. This paper proposes a novel approach to machine learning architecture design leveraging quantum-inspired optimization techniques. Specifically, we explore the application of algorithms inspired by quantum mechanics, such as amplitude amplification, to efficiently navigate the high-dimensional design space of ML architectures. Our methodology aims to identify architectures exhibiting both efficiency (e.g., reduced computational cost) and high performance (e.g., accuracy, F1-score) by simulating the probabilistic nature of quantum phenomena. The core idea is to treat architecture design as an optimization problem where the goal is to maximize a performance metric, and to use quantum-inspired algorithms to efficiently search for the optimal solution. We demonstrate the potential of this approach through a conceptual framework and outline future research directions.

DOI: 10.5281/zenodo.22184514

Related papers

Quantum-Inspired Optimization of Large-Scale Machine Learning Algorithms

Jincheng Zhang

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.

Sep 7, 20260 citations

Quantum-Inspired Optimization for Deep Neural Network Architecture Search

Jincheng Zhang

The process of designing optimal deep neural network architectures is a computationally expensive and often manual task. Traditional gradient-based optimization methods struggle to efficiently explore the vast architectural search space, leading to sub-optimal designs. This research proposes a novel approach leveraging quantum-inspired optimization techniques, specifically quantum annealing, to accelerate the architecture search process. We formulate the architecture search problem as a minimization task within a quantum annealing landscape. The objective function is designed to reflect the performance metrics of a given neural network architecture. Our approach offers a potentially more efficient method for discovering high-performing architectures by harnessing the principles of quantum mechanics, particularly superposition and tunneling, to navigate the complex search space. The core claim is that gradient-based optimization is often inefficient for this task, and quantum annealing provides a viable alternative. This paper outlines the theoretical framework and proposes an initial methodology for applying quantum annealing to deep neural network architecture search.

Sep 5, 20260 citations

Quantum-Inspired Optimization of Deep Neural Network Architectures

Jincheng Zhang

The design of deep neural network (DNN) architectures is a complex and computationally intensive task. Traditional methods often rely on manual tuning or random search, which are inefficient in exploring the exponentially large architectural space. This paper proposes a novel approach to DNN architecture optimization leveraging quantum-inspired algorithms. Specifically, we explore the application of algorithms inspired by quantum annealing and quantum Monte Carlo methods to efficiently search for optimal DNN configurations. Our framework aims to minimize the trade-off between accuracy, latency, and energy consumption – key considerations in real-world DNN deployments. We present a theoretical framework and outline a methodology for integrating these quantum-inspired techniques. The core idea is to map the architectural design problem onto a suitable quantum system, allowing for the parallel exploration of potential solutions. This research provides a new direction for automated DNN design, potentially leading to significant improvements in performance and efficiency.

Sep 2, 20260 citations

Quantum-Inspired Optimization for Large-Scale Machine Learning

Jincheng Zhang

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.

Sep 6, 20260 citations

Quantum-Inspired Optimization of Neural Network Architecture

Jincheng Zhang

This paper explores a novel approach to neural network architecture optimization inspired by quantum superposition and measurement. Traditional neural network architecture search methods, such as reinforcement learning or evolutionary algorithms, often suffer from computational bottlenecks and slow convergence rates. We propose a genetic algorithm that leverages quantum-inspired probability amplitudes to represent and evaluate neural network architectures. Each architecture is assigned a set of probability amplitudes, reflecting the potential for success. Through a simulated measurement process, the algorithm dynamically adjusts these amplitudes, favoring architectures with higher potential and accelerating the search for optimal configurations. The core idea is that quantum superposition allows us to explore a vast solution space concurrently, while the measurement process mimics the collapse of the wave function, focusing the search on promising candidates. This approach demonstrates the potential to significantly reduce the time and resources required for neural network architecture design, paving the way for more efficient and powerful deep learning models. We present a detailed theoretical framework and outline the key components of the algorithm, emphasizing its ability to mimic quantum phenomena for architectural optimization.

Aug 31, 20260 citations

Quantum-Inspired Optimization of Deep Learning Architectures

Jincheng Zhang

This paper proposes a novel approach to optimizing deep learning architectures, drawing inspiration from the principles of quantum mechanics. Traditional deep learning architecture optimization relies heavily on trial-and-error methods, often hampered by the vast search space and lack of efficient strategies. We introduce a quantum-inspired optimization algorithm that utilizes probabilistic simulations mirroring quantum superposition and interference to evaluate multiple network architectures concurrently. The core idea is to model the architecture search process as a quantum computation, where different architectures are represented as quantum states and evaluated through simulated interference effects. This allows for a significantly faster exploration of the architecture space compared to conventional methods. The algorithm aims to identify optimal architectures by exploiting the inherent parallelism and potential for constructive interference within the simulated quantum system. The primary contribution lies in translating quantum concepts into a practical optimization framework for deep learning, potentially leading to improved model accuracy, faster training times, and more efficient resource utilization. The theoretical framework is presented, along with a detailed explanation of the algorithm's components and a discussion of its potential benefits and limitations.

Aug 29, 20260 citations