Abstract The quantum annealing framework provides a general technique for the solution of a wide variety of optimization problems. Recent technological developments have made it possible to build quantum annealing devices with thousands of qubits, bringing them within range to tackle real-world problems in the near future. In this paper, I review this framework, and present a method for its use in the minimization of functions of continuous variables. Then, I discuss how this method can be employed to solve optimization problems with applications in high-energy physics, including training neural networks for classification, solving partial differential equations, and fitting effective field theories to experimental data.
Abstract The quantum annealing framework provides a general technique for the solution of a wide variety of optimization problems. Recent technological developments have made it possible to build quantum annealing devices with thousands of qubits, bringing them within range to tackle real-world problems in the near future. In this paper, I review this framework, and present a method for its use in the minimization of functions of continuous variables. Then, I discuss how this method can be employed to solve optimization problems with applications in high-energy physics, including training neural networks for classification, solving partial differential equations, and fitting effective field theories to experimental data.
This chapter investigates the application of quantum computing to optimization, a field traditionally dominated by classical methods but increasingly limited by computational demands in tackling complex, large-scale problems. The discussion focuses on quantum optimization techniques, particularly variational quantum algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and the variational quantum eigensolver (VQE), along with quantum annealing (QA), a process inspired by physical annealing that leverages quantum tunnelling and superposition. The chapter also explores the integration of quantum approaches within machine learning frameworks, where quantum algorithms can potentially accelerate model training and enhance capabilities. Additionally, it presents practical applications and challenges associated with these quantum algorithms, covering their effectiveness on noisy intermediate-scale quantum (NISQ) devices and the evolving landscape of quantum hardware. This chapter concludes with insights into quantum optimization's future potential to address optimization and machine learning challenges that are currently intractable for classical methods, marking a significant shift in computational science.
The quantum annealing technique is such a paradigm that is next generation that can be used in solving optimization problem across different scientific domains like finance and material science and is capable of breaking through past obstacles to generate solutions for problems waiting to be solved. The purpose of this paper is the detailed depiction of the application of the quantum annealing in the optimization that includes the principles, applications, methodologies and outcomes and future trends all of them. Through a multidisciplinary approach, we examine the use of quantum annealing, as the method that exploits the specific features of the quantum annealing through the quantum mechanics, to explore the multitude of solution spaces faster and better than the classical methods, leading to the superior solution quality and the faster computing times. Next, we give examples for its implementation in Portfolio optimization, Machine learning, chemical optimization, Traffic management and so on depicting its role in delivering tangible benefits such as higher financial returns, precision improvement, and time savings contrast to the classical methods. Therefore, we also emphasize the current impediments, such as the scaling down the size and the ethical aspect, through which we give the way forward for the subsequent scientific researches and studies. In this abstract, we aim to depict the helix-like nature of quantum annealing in the revolution of optimization, bringing in innovations, and thus, shaping the future in which it is the quantum computing technologies that hold the central stage in addressing the complex interconnecting real-world issues.
Most existing quantum annealing approaches are formulated for qubit-based architectures. Consequently, applying them to continuous-variable optimization problems requires discretizing the variables, which can incur substantial qubit overhead. Continuous-variable quantum annealing based on bosonic systems has recently been proposed as an alternative framework, in which each optimization variable is directly encoded in a bosonic mode, such as a cavity mode. In this work, we develop a continuous-variable quantum annealing formulation for solving linear differential equations. By recasting the determination of the solution as a continuous-variable optimization problem, the differential equation can be mapped onto an objective function compatible with bosonic quantum annealing. Numerical simulations of second-order linear differential equations demonstrate that, under the conditions considered, the proposed formulation reproduces the corresponding analytical solutions. These results establish a potential route toward solving differential equations without the discretization overhead inherent in qubit-based implementations.
In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.
Abstract Quantum annealing is a method developed to solve combinatorial optimization problems by utilizing quantum bits. Solving such problems corresponds to minimizing a cost function defined over binary variables. However, in many practical cases, the cost function may also involve continuous variables. Representing continuous variables using quantum bits requires binary encoding, which demands a large number of qubits. To overcome this limitation, an approach using quantum resonators has been proposed, enabling the direct handling of continuous variables within the quantum annealing framework. On the other hand, certain optimization problems involve both binary and continuous variables simultaneously, and a quantum annealing method capable of efficiently solving such hybrid problems has not been established. Here, we propose a quantum annealing method based on a hybrid system composed of qubits and resonators, aiming to minimize cost functions that contain both binary and continuous variables. We present a general framework for hybrid quantum annealing using such systems, and investigate its feasibility and effectiveness through numerical simulations.
In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.