☆ 3.8 Proceedings Paper

Quantum Mating Operator: A New Approach to Evolve Chromosomes in Genetic Algorithms

2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2022)

Implementing evolutionary optimization on actual quantum processors

Giovanni Acampora et al.

Summary: This study introduces a new evolutionary algorithm utilizing an actual quantum processor, which employs quantum phenomena to achieve significant speed-up in computation. By implementing quantum concepts such as quantum chromosome and entangled crossover, the proposed algorithm efficiently executes genetic evolution on quantum devices to converge towards proper sub-optimal solutions of a given optimization problem. The experimental results show that the synergy between quantum and evolutionary computation leads to a promising bio-inspired optimization strategy.

INFORMATION SCIENCES (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Genetic Algorithms for Error Mitigation in Quantum Measurement

Giovanni Acampora et al.

Summary: Noisy Intermediate Scale Quantum (NISQ) devices have the potential to demonstrate quantum computing capabilities, but quantum noise remains an obstacle. Quantum error correction approaches are costly, leading to the emergence of quantum error mitigation methods. Measurement errors are a major issue, with a new genetic algorithm-based approach proposed to mitigate these errors effectively.

2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) (2021)

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Review Computer Science, Artificial Intelligence

An introduction to quantum machine learning: from quantum logic to quantum deep learning

Leonardo Alchieri et al.

Summary: The aim of this work is to introduce the field of quantum machine learning to non-practical readers, covering quantum logic basics, specific elements and algorithms, and the latest quantum machine learning techniques including quantum neural networks.

QUANTUM MACHINE INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence