Computer-inspired quantum experiments

Dipankar Bhattacharyya, Jyotirmoy Guha

Tian Huang, Jun Xu, Tao Luo +3 more

Quantum(-inspired) annealers show promise in solving combinatorial optimisation problems in practice. There has been extensive researches demonstrating the utility of D-Wave quantum annealer and quantum-inspired annealer, i.e., Fujitsu Digital Annealer on various applications, but few works are comparing these platforms. In this paper, we benchmark quantum(-inspired) annealers with three combinatorial optimisation problems ranging from generic scientific problems to complex problems in practical use. In the case where the problem size goes beyond the capacity of a quantum(-inspired) computer, we evaluate them in the context of decomposition. Experiments suggest that both annealers are effective on problems with small size and simple settings, but lose their utility when facing problems in practical size and settings. Decomposition methods extend the scalability of annealers, but they are still far away from practical use. Based on the experiments and comparison, we discuss the advantages and limitations of quantum(-inspired) annealers, as well as the research directions that may improve the utility and scalability of the these emerging computing technologies.

Takahiro Imabeppu, Shigeru Nakayama, Satoshi Ono

Quantum-Inspired Evolutionary Algorithm (QEA), a type of stochastic algorithm for solving combinatorial optimization problems, is evolutionary computation using quantum bits and superposition states in quantum computation. Although coarse-grained parallel, QEA has many parameters that must be adjusted manually. The simpler algorithm, Quantum-inspired Evolutionary Computation with Pair Swap operator (QEAPS), the authors propose involves just one population and a simple genetic operation exchanging best solution information between two individuals chosen randomly, instead of the migration operation used in QEA, and thereby fewer parameters to be adjusted. The authors found in experiments that QEAPS finds highly qualified solutions, is more robust against constraint handling, and has a higher search performance of thanks to diversified best solution information.

Henry O. Everitt

Hanns‐Christoph Nägerl, Dietrich G. Leibfried, Ferdinand Schmidt‐Kaler +9 more

Abdesslem Layeb

This paper presents a new inspired algorithm called quantum inspired cuckoo search algorithm (QICSA). This one is a new framework relying on quantum computing principles and cuckoo search algorithm. The contribution consists in defining an appropriate representation scheme in the cuckoo search algorithm that allows applying successfully on combinatorial optimisation problems some quantum computing principles like qubit representation, superposition of states, measurement, and interference. This hybridisation between quantum inspired computing and bioinspired computing has led to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. Experiments on knapsack problems show the effectiveness of the proposed framework and its ability to achieve good quality solutions.