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Approximate Quantum Adders With Genetic Algorithms: An IBM Quantum Experience

Rui Li, Álvarez Rodríguez, Unai, Lamata Manuel, Lucas, Solano Villanueva, Enrique Leónidas

Published Jan 1, 2017• 34 citations
OpenAlex

Abstract

It has been proven that quantum adders are\n\t\t\t\t forbidden by the laws of quantum mechanics. We analyze\n\t\t\t\t theoretical proposals for the implementation of approximate\n\t\t\t\t quantum adders and optimize them by means\n\t\t\t\t of genetic algorithms, improving previous protocols in\n\t\t\t\t terms of e ciency and delity. Furthermore, we experimentally\n\t\t\t\t realize a suitable approximate quantum adder\n\t\t\t\t with the cloud quantum computing facilities provided by\n\t\t\t\t IBM Quantum Experience. The development of approximate\n\t\t\t\t quantum adders enhances the toolbox of quantum\n\t\t\t\t information protocols, paving the way for novel applications\n\t\t\t\t in quantum technologies.

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International Symposium on Information and Communication Technology•Dec 6, 2023•0 citations
It has been proven that quantum adders are\n\t\t\t\t forbidden by the laws of quantum mechanics. We analyze\n\t\t\t\t theoretical proposals for the implementation of approximate\n\t\t\t\t quantum adders and optimize them by means\n\t\t\t\t of genetic algorithms, improving previous protocols in\n\t\t\t\t terms of e ciency and delity. Furthermore, we experimentally\n\t\t\t\t realize a suitable approximate quantum adder\n\t\t\t\t with the cloud quantum computing facilities provided by\n\t\t\t\t IBM Quantum Experience. The development of approximate\n\t\t\t\t quantum adders enhances the toolbox of quantum\n\t\t\t\t information protocols, paving the way for novel applications\n\t\t\t\t in quantum technologies.
Abstract It has been proven that quantum adders are forbidden by the laws of quantum mechanics. We analyze theoretical proposals for the implementation of approximate quantum adders and optimize them by means of genetic algorithms, improving previous protocols in terms of efficiency and fidelity. Furthermore, we experimentally realize a suitable approximate quantum adder with the cloud quantum computing facilities provided by IBM Quantum Experience. The development of approximate quantum adders enhances the toolbox of quantum information protocols, paving the way for novel applications in quantum technologies.
Abstract The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between quantum autoencoders and quantum adders, which approximately add two unknown quantum states supported in different quantum systems. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms. (c) 2018 IOP Publishing Ltd.
Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.
Typical methods of quantum/reversible synthesis are based on using the binary character of quantum computing.However, multi-valued logic is a promising choice for future computer technologies, given a set of advantages when comparing to binary circuits.In this work, we have developed a genetic algorithm-based synthesis of ternary reversible circuits using Muthukrishnan-Stroud gates.The method for chromosomes coding that we present, as well as a judicious choice of algorithm parameters, allowed obtaining circuits for half-adder and full adder which are better than other published methods in terms of cost, delay times and amount of input ancillary bits.A structure of the circuits is analyzed in details, based on their decomposition.
The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum optimization technique used for solving combinatorial optimization problems. However, in constrained binary optimization, QAOA’s reliance on equal initial probabilities for all solutions can lead to suboptimal outcomes. To enhance the performance of QAOA in this context, we propose a novel approach that combines QAOA with genetic algorithms. In this hybrid approach, the results obtained from QAOA serve as the initial population for a genetic algorithm. We apply this methodology to address the k-heaviest subgraph problem, a critical challenge in quantum computing research. Our experiments, conducted on benchmark datasets, demonstrate a significant improvement in solution quality.