Miguel Luna
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Miguel Luna has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Gamzepelin Aksoy
Department of Computer Engineering Isparta University of Applied Sciences Isparta Türkiye
1 shared publication Grégoire Cattan
1 shared publication- Grégoire Cattan
IBM Software, Data & AI Kraków Poland
1 shared publication - Vedant Patare
Fr. Conceicao Rodrigues Institute of Technology Electrical Engineering Mumbai India
1 shared publication
Latest Publications
- Open Access
Implementation of a Quantum Approximate Optimization Algorithm for Continuous Variables with Qiskit
In this paper, we propose a quantum approximate optimization algorithm (QAOA) for solving continuous-variable optimization problems on gate-based universal quantum computers. Our approach exploits the power of quantum computing to explore the solution space efficiently and find approximate optima. We demonstrate the effectiveness of our algorithm by solving a quadratic program, testing different combinations of mixer operators, numbers of repetitions and optimizers. The optimizer and the number of repetitions had the greatest effect size. The best performance was achieved using the SPSA optimizer, three repetitions, and the binary X mixer.
Open AccessImplementation of a Quantum Approximate Optimization Algorithm for Continuous Variables with Qiskit
In this paper, we propose a quantum approximate optimization algorithm (QAOA) for solving continuous-variable optimization problems on gate-based universal quantum computers. Our approach exploits the power of quantum computing to explore the solution space efficiently and find approximate optima. We demonstrate the effectiveness of our algorithm by solving a quadratic program, testing different combinations of mixer operators, numbers of repetitions and optimizers. The optimizer and the number of repetitions had the greatest effect size. The best performance was achieved using the SPSA optimizer, three repetitions, and the binary X mixer.