Computationally expensive applications, including machine learning, chemical simulations,
and financial modeling, are promising candidates for noisy intermediate scale quantum …

Optimizing quantum circuits is critical for enhancing computational speed and mitigating
errors caused by quantum noise. Effective optimization must be achieved without …

In the realm of classical software engineering, model-driven optimization has been widely
used for different problems such as (re) modularization of software systems. In this paper, we …

Processing quantum information poses novel challenges regarding the debugging of faulty
quantum programs. Notably, the lack of accessible information on intermediate states during …

We have defined a new method for automatic construction of reversible logic circuits by
using the genetic programming approach. The choice of the gate library is 100% dynamic …

Quantum computing is a field of study aimed at develo** technologies based on the
principles of quantum theory. In this research area, enormous theoretical progress has been …

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 …