Quantum-inspired evolutionary algorithms on ibm quantum experience

Quantum computing has been proposed as a possible accelerator for a myriad of complex computational problems. From this, quantum-inspired methodologies have emerged as methods that take the principles and restrictions of quantum theory to solve classical problems on classical computers. Quantum-inspired methodologies have proven advantageous in solving optimization problems and in learning over traditional nature-inspired methods. Since these algorithms operate on classical computers, the next unanswered question is important and valid: Can quantum-inspired evolutionary algorithms take advantage of quantum computers? The present work attempts to shed some light on this question, implementing quantum-inspired evolutionary algorithms for numerical optimization on quantum hardware. We present statistical metrics of their performance on the IBM Q quantum computer and compared them to their execution on a GPU-based quantum simulator, and the IBM quantum simulator.