An implementation of compact genetic algorithm on a quantum computer

Yingchareonthawornchai, Sorrachai; Aporntewan, Chatchawit; Chongstitvatana, Prabhas

doi:10.1109/jcsse.2012.6261939

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…A good example of this option is QCL (Quantum Computing Language) [21]-a programming language ( Figure 5) for quantum computers developed by Bernhard Ömer. QCL has been applied in a variety of problems, e.g., solving systems of nonlinear equations [22], the implementation of Bernstein-Vazirani algorithm [23], parallelization of quantum gates [24], simulation of Dijkstra's algorithm [25] and programming quantum genetic algorithms [26]. In the example shown in Figure 5, two qubits are declared with qureg command: a first qubit in a register labeled as u and the second qubit in a register v. Consequently, we have implemented a quantum memory , uv  …”

Section: Q-programming Languagesmentioning

confidence: 99%

Quantum Genetic Algorithms for Computer Scientists

Lahoz-Beltrá

2016

Computers

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Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as "Quantum Genetic Algorithms" (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs "avoiding" the possible difficulties of quantum-mechanical phenomena.

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Section: Q-programming Languagesmentioning

confidence: 99%

Quantum Genetic Algorithms for Computer Scientists

Lahoz-Beltrá

2016

Computers

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“…During the past decades, the merge of GAs and quantum computation has been a source of new heuristic optimization methods [6][7][8]. Induced by the non-linear behavior of genetic operators, most of the effort has been focused on quantum inspired GAs, which integrate some concepts of quantum mechanics to engineer new varieties of classical evolutionary algorithms [7,[9][10][11][12][13][14][15]. On the other hand, fully quantum approaches potentially achieving quantum speed-up have only attained partial success in the inclusion of the aforementioned characteristic elements [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Quantum Genetic Algorithm with Individuals in Multiple Registers

Ibarrondo¹,

Gatti²,

Sanz³

2022

Preprint

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Genetic algorithms are heuristic optimization techniques inspired by Darwinian evolution, which are characterized by successfully finding robust solutions for optimization problems. Here, we propose a subroutine-based quantum genetic algorithm with individuals codified in independent registers. This distinctive codification allows our proposal to depict all the fundamental elements characterizing genetic algorithms, i.e. population-based search with selection of many individuals, crossover, and mutation. Our subroutine-based construction permits us to consider several variants of the algorithm. For instance, we firstly analyze the performance of two different quantum cloning machines, a key component of the crossover subroutine. Indeed, we study two paradigmatic examples, namely, the biomimetic cloning of quantum observables and the Bužek-Hillery universal quantum cloning machine, observing a faster average convergence of the former, but better final populations of the latter. Additionally, we analyzed the effect of introducing a mutation subroutine, concluding a minor impact on the average performance. Furthermore, we introduce a quantum channel analysis to prove the exponential convergence of our algorithm and even predict its convergence-ratio. This tool could be extended to formally prove results on the convergence of general non-unitary iteration-based algorithms.

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“…The author is with Graduate School of Media and Governance, Keio University SFC, Fujisawa, Kanagawa 252-0882, Japan, E-mail: whit3z@sfc.wide.ad.jp of quantum phenomena such as interference and superposition and translate them into classical analogues. Another approach, quantum-assisted genetic algorithms [24]- [27] and quantum-assisted compact genetic algorithm [28], delegates some of the complex tasks to quantum computers, such as the mutation operator or probabilistic elements, while still performing crossover and population updates on the classical side. The last approach attempts to redefine the GA in the context of quantum computation [29]- [31].…”

Section: Introductionmentioning

confidence: 99%

Compact Genetic Algorithm with Quantum-Assisted Feasibility Enforcement

Suksen

Benchasattabuse

Chongstitvatana

2022

ECTI-CIT Transactions

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The local optima problem is one of the biggest obstacles in compact genetic algorithms since each bit in the problem encoding string is independent of the others. We propose a quantum-assisted compact genetic algorithm that uses a quantum amplitude amplification technique in the selection process to circumvent the said problem. In addition to using elitism mechanics where a single best candidate solution is kept to drive the probability vector, the amplitude amplification subroutine also acts as a mutation operator which, with high probability, enforces the constraint that the newly generated candidate is a feasible solution. We demonstrate this idea by applying the algorithm to the traveling salesman problem of size 3 and 4 cities on IBM Qiskit simulator to show how one would construct the quantum circuit and how to encode the optimization problem into quantum states via Ising spin model encoding. We then show space and time complexity analysis based on the quantum circuit model. Finally, we discuss the number of qubits required for encoding, gate counts in the circuit model, and the practicality of applying this to a small-scale devices in the future.

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An implementation of compact genetic algorithm on a quantum computer

Cited by 8 publications

References 6 publications

Quantum Genetic Algorithms for Computer Scientists

Quantum Genetic Algorithms for Computer Scientists

Quantum Genetic Algorithm with Individuals in Multiple Registers

Compact Genetic Algorithm with Quantum-Assisted Feasibility Enforcement

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