Quantum annealing enables a global minimum search for quadratic functions in binary integer variables. It should be applied to more general problems. Therefore, in this research, we investigate the extension of the method to solve global minimum search by the quantum annealing method. There are three issues that need to be addressed: (1) conversion of decimal integer variables to binary integer variables; (2) binary discrete variable approximation of decimal continuous variables; (3) extension to general non-quadratic functions. This paper aims to develop the basic mathematics necessary for expanding the scope of application of the quantum annealing method, which was created as a solution to the minimum problem of quadratic functions of binary integer variables.
Quantum annealing enables a global minimum search for quadratic functions in binary integer variables. It should be applied to more general problems. Therefore, in this research, we investigate the extension of the method to solve global minimum search by the quantum annealing method. There are three issues that need to be addressed: (1) conversion of decimal integer variables to binary integer variables; (2) binary discrete variable approximation of decimal continuous variables; (3) extension to general non-quadratic functions. This paper aims to develop the basic mathematics necessary for expanding the scope of application of the quantum annealing method, which was created as a solution to the minimum problem of quadratic functions of binary integer variables.
With the emergence of D-wave’s quantum annealing machine, the power of quantum computing has become tangible, and the development of quantum computers is accelerating rapidly. Unfortunately, at present, the minimum value search that D-wave’s quantum annealing machine can handle is limited to quadratic functions of binary integer variables. This constraint is due to both hardware and software limitations. If this constraint was to be lifted, it would undoubtedly bring about a revolution in the world. Therefore, in this study, we obtained the basic mathematics of using quantum annealing, which enables hyper-parallel computing using qubits, to find the global minimum of any minimum value problem. Furthermore, we conducted simple simulations using classical computers to demonstrate the effectiveness of the ideas and algorithms. Furthermore, we conducted simple simulations using classical computers to demonstrate the effectiveness of the ideas and algorithms. We obtained also the mathematical method of using quantum annealing in learning neural networks and solving linear equations.
Quantum annealing is a method based on simulated annealing where temperature variations are replaced by quantum fluctuations that cause qubit state transitions. ISING problems and Quadratic Unconstrained Binary Optimization problems (QUBO) can be tackled by quantum annealing-based machines. We can link the interconnected qubits of the machine and the binaries of our model: qubits are binary variables and each pair of them linked by a coupler has a strong impact on the equality or the non-equality of the two binaries of the associated pair. Quantum annealing is based on the fact that any system tends to seek its minimum energy state. Starting from qubits in a state of superposition where all the solutions to the problem are fairly represented, the machine will apply a magnetic field by targeting the qubits and couplers in such a way as to make their value energetically favorable in the direction of optimization (minimization). For the coupled qubits, it is the quadratic products of the binary variables that are considered here and the physical system will make it energetically favorable for them to take (or not) the same values. To work on these machines based on quantum annealing in order to solve optimization problems with constraints, the latter must be relaxed while penalizing the Objective function. After presenting the case of the Max-Cut problem, we will look at such constrained problems.
Quantum annealing is a metaheuristic method aimed at finding the global minimum of a pseudo-Boolean quadratic function. To solve a problem by a quantum annealer, it must be restated as a QUBO (quadratic unconstrained binary optimization) problem, the standard format acceptable to an annealer. However, there are many natural problems that turn out be of cubic or higher order, yet promising solutions can be obtained for many instances using quantum annealers, after converting to the QUBO format, using order reduction criteria. Accordingly, quadratization becomes a crucial preprocessing step in order to make use of present-day quantum annealing hardware that relies on coupling technology, which can only process interactions of at most degree two. A critical challenge is that quadratization methods often require many additional variables, degrading the performance of the annealers. A number of algorithmic and heuristic quadratization techniques have been proposed and used for annealing in the literature, affecting the performance of the annealers differently. We examine a number of such quadratization techniques, focusing on their efficiency, compactness, and applicability. We analyze the approaches motivated by diverse goals and highlight the transition from foundational approaches to more recent compact approaches. An evaluation of the current usage of different quadratization methods in the literature is carried out at the end of the survey. Further, we comment on the limitations on usage and show possible opportunities to enhance the performance of annealers.
With the advent of novel quantum computing technologies and the new possibilities thereby offered, a prime opportunity has presented itself to investigate the practical application of quantum computing. This work investigates the feasibility of using quantum annealing for structural optimization. The target problem is the discrete truss sizing problem—the goal is to select the best size for each truss member so as to minimize a stress-based objective function. To make the problem compatible with quantum annealing devices, the objective function must be translated into a quadratic unconstrained binary optimization (QUBO) form. This work focuses on exploring the feasibility of making this translation. The practicality of using a quantum annealer for such optimization problems is also assessed. A method is eventually established to translate the objective function into a QUBO form and have it solved by a quantum annealer. However, scaling the method to larger problems faces some challenges that would require further research to address.
We propose Quantum Enhanced Simulated Annealing (QESA), a novel hybrid optimization framework that integrates quantum annealing (QA) into simulated annealing (SA) to tackle continuous optimization problems. While QA has shown promise in solving binary problems such as those expressed in Ising or QUBO form, its direct applicability to real-valued domains remains limited. QESA bridges this gap by using QA to select discrete search directions that guide SA through the continuous solution space, enabling the use of quantum resources without requiring full problem discretization. We demonstrate QESA’s effectiveness on box-constrained quadratic programming (QP) problems, a class of non-convex optimization tasks that frequently arise in practice. Experimental results show that QESA consistently outperforms classical baselines in solution quality, particularly on larger and more ill-conditioned problems, while maintaining competitive runtime. As quantum annealing hardware matures, QESA offers a scalable and flexible strategy for leveraging quantum capabilities in continuous optimization.
Recently, several powerful machines dedicated to solving combinatorial optimization problems through the Ising-model formulation have appeared. The trigger for the paradigm shift to a specialized machine for solving optimization problems was the D-Wave machine, which implements quantum annealing. Quantum annealing employs quantum fluctuations to find an optimal solution to an optimization problem with discrete variables. In particular, we input the optimization problem in the form of the Ising Hamiltonian, which is a specialized form of the quadratic unconstrained binary optimization problem. However, when we employ quantum annealing for a practical optimization problem, there are several issues. One typical issue is absence of the detailed form of the cost function, which characterizes the optimization problem. To input problems into specialized machines for solving an optimization problem, it is necessary to determine the unknown parameters within the Ising model. We propose a method to estimate the unknown parameters in the Ising Hamiltonian using compressed sensing. Furthermore, we analyze the theoretical limitations of our proposed method by employing the replica method, which is a sophisticated tool in statistical mechanics.