IEEE Nanotechnology Magazine

Quantum Computing for Optimization With Ising Machine

Yen-Jui Chang, Chin-Fu Nien, Kuei-Po Huang, Yun-Ting Zhang, Chien‐Hung Cho, Ching‐Ray Chang

Published Mar 28, 2024• 5 citations

Abstract

Optimization problems, which involve finding the best solution from a set of possible solutions, are ubiquitous in various fields, from finance to engineering. Traditional algorithms sometimes struggle with these problems, especially when the solution space is vast, or the landscape is filled with numerous local minima. Quantum-inspired computing, which emulates quantum mechanical principles on classical hardware, emerges as a promising paradigm to address these challenges. This paper delves into two notable approaches: coherent Ising machines (CIM) and graphics processing unit (GPU)-accelerated simulated annealing. In essence, both methods offer innovative strategies to navigate the solution landscape, potentially bypassing the pitfalls of local optima and ensuring more efficient convergence to solutions. By harnessing the strengths of these quantum-inspired techniques, we can pave the way for enhanced computational capabilities in tackling complex optimization problems, even without a fault-tolerant quantum computer.

DOI: 10.1109/mnano.2024.3378485

Related papers

Quantum-Inspired Optimization for Enterprises

R. Thilagavathy, Malavika Krishnamoorthy Gayathri, G. K. Sandhia +1 more

Optimization involves finding the best solution from a set of possible options. Traditional computers employ classical algorithms like gradient descent and simulated annealing to solve these problems. However, as the complexity of problems increases, classical computers encounter limitations in terms of time and resources. Quantum optimization stands as a revolutionary paradigm that harnesses the unique properties of quantum mechanics to tackle complex optimization problems that are beyond the capabilities of classical computers. Quantum computers utilize qubits, the fundamental units of quantum information, which can exist in superpositions of states, enabling them to process a vast number of possibilities simultaneously. Additionally, the phenomenon of entanglement enables qubits to be correlated in ways that classical bits cannot, further enhancing their computational power. In this chapter, the authors will delve into the principles, applications, and current developments in the realm of quantum optimization.

Advances in computer and electrical engineering book seriesJan 31, 20243 citations

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 Heuristics for Complex Optimization

Jincheng Zhang

This paper explores the application of quantum-inspired heuristics to tackle complex optimization problems. The core idea stems from leveraging quantum mechanical principles, specifically superposition and interference, to design novel heuristic algorithms. Instead of relying on traditional approaches like simulated annealing or genetic algorithms, we propose algorithms that mimic the evolution of wave functions within complex search spaces. These algorithms incorporate principles analogous to quantum annealing, allowing them to effectively navigate local minima and accelerate convergence rates. The novelty of this work lies in its direct application of quantum concepts to classical optimization, offering a potentially more efficient solution method without the need for specialized quantum computing hardware. The methodology outlined herein provides a framework for developing a new generation of optimization algorithms inspired by the underlying principles of quantum mechanics. The performance of these algorithms is evaluated through theoretical analysis and conceptual demonstrations, highlighting their potential advantages over conventional heuristic techniques.

Aug 28, 20260 citations

Quantum Computing: Algorithms and Applications in Optimization Problems

Vikram Sehrawat

Quantum computing has emerged as a transformative paradigm with the potential to revolutionize computational tasks previously deemed intractable for classical computers. This paper explores the application of quantum computing algorithms in solving optimization problems, a cornerstone of numerous fields including logistics, finance, machine learning, and operations research. Quantum computers harness quantum mechanical phenomena such as superposition and entanglement to perform computations in ways fundamentally different from classical computers. This enables quantum algorithms to explore vast solution spaces simultaneously, offering potential exponential speedups over classical algorithms for certain optimization tasks. The abstract focuses on reviewing prominent quantum algorithms tailored for optimization, such as Grover's algorithm and quantum annealing approaches like those developed by D-Wave Systems. These algorithms target diverse optimization challenges including combinatorial optimization, portfolio optimization, and scheduling problems. We discuss their theoretical underpinnings, computational complexities, and practical implementations on current and near-term quantum hardware.

Journal of Quantum Science and Technology.Jul 2, 20243 citations

Physics-Inspired Optimization in the QUBO Framework: Key Concepts and Approaches

Lien-Po Yu, Chin-Fu Nien

Quantum computing promises to have a tremendous advantage over its classical counterpart for solving computationally hard problems, yet remains in a relatively early stage for practical applications owing to the limited capabilities of today’s quantum computers. The approach to the special purposes of quantum computers by exploiting the special-purpose physics-inspired or quantum-inspired computers is emerging as a novel alternative to its quantum counterpart in tackling hard problems in high-performance computing. Inspired by physics, the Ising machine — a type of special-purpose computer that implements or emulates physics or quantum effects of the Ising model to speed up finding solutions to optimization problems — has recently become an active research area in the field of combinatorial optimization. This paper is to address the key enabling software and hardware technology underlying physics-inspired optimization using Ising machines in the unified quadratic unconstrained binary optimization (QUBO) framework for modeling and solving computationally hard combinatorial optimization problems, and with an aim to shed some light on the challenges and opportunities associated with the ever-growing landscape of this novel high-performance computing.

SPINAug 30, 20231 citation

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