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IEEE Symposium Series on Computational Intelligence (SSCI)

On Comparison of Some Representations for the Evolution of Quantum Operators

Michal Bidlo, Petr Žufan

Published Dec 1, 2020• 2 citations
Publisher

Abstract

This paper presents a comparative study regarding the evolutionary design of quantum operators in the form of unitary matrices. Three different techniques allowing the generation of unitary matrices are investigated whose parameters are tuned by means of evolutionary algorithms. Such problem may be considered as a numerical optimisation task because the unitary matrices are represented by complex numbers with real-valued coefficients. Specifically, Genetic Algorithm and Evolution Strategy are applied, each in four different setups, and evaluated on three case studies: the 2-qubit Controlled-NOT gate, 3-qubit entanglement operator and 4-qubit detector of an element with the maximum amplitude. The evolutionary algorithms in combination with the given representation techniques are evaluated in order to determine the abilities of various experimental setups to solve the given tasks. The main contribution is the utilisation of QR decomposition, a technique for generating unitary matrices from theoretically any sequence of complex numbers, that has been applied for the first time in this paper for the evolution of quantum operators. It will be demonstrated that the QR decomposition exhibits a good performance especially in solving more complex quantum operators in comparison with other representations.

DOI
10.1109/ssci47803.2020.9308423

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This paper presents a comparative study regarding the evolutionary design of quantum operators in the form of unitary matrices. Three different techniques allowing the generation of unitary matrices are investigated whose parameters are tuned by means of evolutionary algorithms. Such problem may be considered as a numerical optimisation task because the unitary matrices are represented by complex numbers with real-valued coefficients. Specifically, Genetic Algorithm and Evolution Strategy are applied, each in four different setups, and evaluated on three case studies: the 2-qubit Controlled-NOT gate, 3-qubit entanglement operator and 4-qubit detector of an element with the maximum amplitude. The evolutionary algorithms in combination with the given representation techniques are evaluated in order to determine the abilities of various experimental setups to solve the given tasks. The main contribution is the utilisation of QR decomposition, a technique for generating unitary matrices from theoretically any sequence of complex numbers, that has been applied for the first time in this paper for the evolution of quantum operators. It will be demonstrated that the QR decomposition exhibits a good performance especially in solving more complex quantum operators in comparison with other representations.
A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices.A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices. Three existing techniques (representations) which allow generating unitary matrices are used in various evolutionary algorithms in order to optimize their coefficients. The objective is to obtain as precise quantum operators (the resulting unitary matrices) as possible for given quantum transformations. Ordinary evolution strategy, self-adaptive evolution strategy and differential evolution are applied with various settings as the optimization algorithms for the quantum operators. These algorithms are evaluated on the tasks of designing quantum operators for the 3-qubit and 4-qubit maximum amplitude detector and a solver of a logic function of three variables in conjunctive normal form. These tasks require unitary matrices of various sizes. It will be demonstrated that the self-adaptive evolution strategy and differential evolution are able to produce remarkably better results than the ordinary evolution strategy. Moreover, the results can be improved by selecting a proper settings for the evolution as presented by a comparative evaluation.
In the literature, there are many Quantum Inspired Evolutionary Algorithms (QIEA) that proved good performance and results when applied to solving a myriad of problems. However, the real quantum representation increases precision for numerical optimisation problems and reduces the memory storage need, alternatively to the binary representation. On the other hand, evolutionary algorithms' performance mainly depends on the crossover and mutation operators, reaching equilibrium between the explorative and exploitative features of evolutionary algorithms. This paper proposes new quantum genetic operators for real quantum representation, instead of the 1/5 rule present in AEIQ-R, to improve the performance of the quantum genetic algorithm with real representation. Three crossover and two mutation operators are proposed and tested to optimise four functions with different dimensions. Friedman's test indicated a significant difference between the combinations, and that combination composed of quantum arithmetic crossover and quantum creep mutation have the best performance.
In this thesis a novel representation of a unitary matrix intended to act as a quantum program generated by evolutionary programming shall be presented. The representation consists of a number of 2times22 \\times 22times2 matrices being evolved and combined such that a unitary matrix capable of carrying out a predetermined function on a pure quantum state is generated. In this thesis the algorithm will demonstrate proficiency at performing tasks such as cloning a quantum state or creating order amidst a quantum state in superposition. Furthermore, in the field of quantum information, quantum channels are implemented in order to either perform quantum operations to a quantum state or for one to transmit a quantum state from a sender to a recipient. When this happens, the physical implementation of a quantum channel will often differ from the intended quantum channel that was to be implemented. In cases such as these, the quantum gate fidelity is calculated in order to determine how decoherent the resulting state is from what it should have been. In this thesis a procedure that uses a genetic algorithm to place an upper bound on minimal quantum gate fidelity will be presented.
A new approach based on Evolution Strategies (ES) is proposed to evolve quantum unitary operators which represent the computational algorithm a quantum computer would perform to solve an arbitrary problem. This approach has superior attributes over previous endeavors by using real valued vectors and not limited to a predefined set of quantum gates. We show this method can be used to solve a variety of key problems from evolving simple quantum gates, quantum oracles, to generalized problem instances. This method is highly extensible and useful for propagating future research of quantum unitary operator development.
As a novel evolutionary algorithm, a quantum-inspired evolutionary algorithm (QIEA) is attracting increasing attention, due to its good global search capability and rapid convergence. A quantum rotation gate (QR-gate) is the key operator in a QIEA. In the literature, there are many versions of QR-gates. How to evaluate and choose a QR-gate is very worth discussing. This paper focuses on a comparison and analysis on six QR-gates. The performances of QIEAs with the six QR-gates are tested on a practical problem, image sparse decomposition. Experimental results show that the QR-gate5 is superior to other five versions of QR-gates in terms of the quality of solutions and computing time.
Quantum computers are seen as the next generation computing technique with the processing power potential they have. However, currently, quantum computers are limited in terms of hardware and algorithmic capabilities. In this study, quantum-inspired methods which are formed by combining quantum computation techniques with classical algorithms are focused on. It has been emphasized in many studies that quantum-inspired methods provide advantages especially for metaheuristic methods. Different from them, in this study, the performance of population-based quantum-inspired methods are compared. The paper focuses on solving the same optimization problem by using quantum-inspired versions of the population-based optimization algorithms such as evolutionary algorithm, genetic algorithm, and differential evolution algorithm. The experimental results show that, while Quantum-inspired Evolutionary Algorithm is better at global search, Quantum-inspired Differential Evolution Algorithm is better at local search and more accurate results.