This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.
This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.
In this work, the Particle Swarm Optimization (PSO) algorithm has been used to train various Variational Quantum Circuits (VQCs). This approach is motivated by the fact that commonly used gradient-based optimization methods can suffer from the barren plateaus problem. PSO is a stochastic optimization technique inspired by the collective behavior of a swarm of birds. The dimension of the swarm, the number of iterations of the algorithm, and the number of trainable parameters can be set. In this study, PSO has been used to train the entire structure of VQCs, allowing it to select which quantum gates to apply, the target qubits, and the rotation angle, in case a rotation is chosen. The algorithm is restricted to choosing from four types of gates: Rx, Ry, Rz, and cNOT. The proposed optimization approach has been tested on various datasets of the MedMNIST, which is a collection of biomedical image datasets designed for image classification tasks. Performance has been compared with the results achieved by classical stochastic gradient descent applied to a predefined VQC. The results show that the PSO can achieve comparable or even better classification accuracy across multiple datasets, despite the PSO using a lower number of quantum gates than the VQC used with gradient descent optimization.
The article explored and delved into the advanced computational strategies of Particle Swarm Optimization (PSO) by contrasting classical and quantum computing paradigms. The advantages of quantum computing lie in its potential to solve computationally complex problems exponentially faster than classical computers. One of the advantages of Particle Swarm Optimization is its ability to find optimal solutions in complex search spaces. The research centers around the performance of PSO algorithms, as a part of the biological swarm optimization algorithms, when applied to a set of single-objective optimization functions, namely the Sphere, Rosenbrock, Booth, and Himmelblau functions. Utilizing a controlled setup of 100 particles, iterating 100 times across various dimensions tailored to each function, our study reveals that quantum Particle Swarm Optimization, implemented via Q# programming language and tested in Azure Quantum Workspace, consistently surpasses classical PSO in precision and convergence to global minima, despite the increased computational demands and error sensitivity inherent to quantum computations. The classical approach facilitated through Python programming language and leveraging deterministic pseudorandom number generators demonstrates robustness and lower computational costs but does not achieve the quantum's level of accuracy. The paper highlights the potential of quantum PSO to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces, paving the way for future applications where quantum advantages can be fully realized. The analysis goes further to discuss the implications of these findings for the future of optimization in various industries, including logistics, engineering, and finance, where optimization plays a critical role. The potential of quantum Particle Swarm Optimization to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces is particularly notable. It suggests that quantum computing could soon transform the landscape of computational optimization, providing solutions that are not only quicker but also more accurate.
Based on quantum evolutionary algorithm and particle swarm optimization, a quantum particle swarm evolutionary algorithm is proposed. In this algorithm, quantum angle is used to represent the qubit, a new method learning from the idea of particle swarm algorithm is presented to determine rotation angle, He gate is taken to prevent from premature convergence. Applying this algorithm to identify system parameter, and comparing with conventional genetic algorithm and quantum evolutionary algorithm, the experimental results illustrate that the proposed algorithm has better performance than that of others. Meanwhile, it can also keep high identification ability to the system with the existence of noise.
The ability of theoretical and monadic quantum models against number comparison to conventional Quantum Genetic Algorithm (QGA), quantitative particle swarm optimization, ant colony groups with simulated annealing types is analyzed in the context of electrical engineering. Several experiments were done, involving a collection of multiple data sets standard for the proposed circuit layout within power distribution and signal processing applications. In regard to comparative analysis, each algorithm presents its particular strengths for QGA-competitive convergence speed; ͟QPSO – quick conversion of individuals into a global optimum during evolution’s course and robust solutions quality in an unstable environment without tuning. The related works of the proposed algorithms include evaluating it against metaheuristics for power systems, nature-inspired hybrid heuristic approaches as well as physics motivated optimization schemes. The paper emphasizes the superiority of quantum algorithms over their classical counterparts, which is a major innovation in optimization space. This detailed analysis contributes to further comprehending the potentials of quantum computing in overcoming tough optimization challenges faced by electrical engineers.
A quantum evolutionary computation (QEC) algorithm with particle swarm optimization (PSO) and two-crossovers is proposed to overcome identified limitations. PSO is adopted to update the Q-bit automatically, and two-crossovers are applied to improve the convergence quality in the basic QEC model. This hybrid strategy can effectively employ both the ability to jump out of the local minima and the capacity of searching the global optimum. The performance of the proposed approach is compared with basic QEC on the standard unconstrained scalable benchmark problem that numerous hard combinatorial optimization problems can be formulated. The experimental results show that the proposed method outperforms the basic QEC quite significantly.
This study explores the potential of employing algorithms like iSOMA, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimization, and Ant Colony Optimization for the design of quantum computing circuits. Utilizing the Qiskit environment, the research involved simulating a straightforward quantum circuit with variable parameters. To substantiate the effectiveness of these algorithms, three distinct experimental setups were conducted under varying conditions and degrees of freedom. The findings reveal that these algorithms are not only suitable for simulations but also excel in identifying solutions that conserve qubits. A comparative analysis of the methods was performed using the Friedman test, followed by the Nemenyi post-hoc test to evaluate their relative performance.