Quantum Algorithms for solving Hard Constrained Optimisation Problems

TL;DR

This paper explores quantum algorithms for complex constrained optimization problems, demonstrating their application in real-world scenarios like scheduling and robotics, and introduces new methods to enhance algorithm efficiency in the NISQ era.

Contribution

It introduces quantum algorithms for constrained optimization, proposes EVA to accelerate variational algorithms, and demonstrates integration with mobile robotics and AI paradigms.

Findings

Quantum algorithms can solve real-world constrained problems.

EVA significantly speeds up variational quantum algorithms.

Successful integration with quantum hardware and robotics.

Abstract

The thesis deals with Quantum Algorithms for solving Hard Constrained Optimization Problems. It shows how quantum computers can solve difficult everyday problems such as finding the best schedule for social workers or the path of a robot picking and batching in a warehouse. The path to the solution has led to the definition of a new artificial intelligence paradigm with quantum computing, quantum Case-Based Reasoning (qCBR) and to a proof of concept to integrate the capacity of quantum computing within mobile robotics using a Raspberry Pi 4 as a processor (qRobot), capable of operating with leading technology players such as IBMQ, Amazon Braket (D-Wave) and Pennylane. To improve the execution time of variational algorithms in this NISQ era and the next, we have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE, and that is, to date, the flagship of the quantum computation. To improve the execution time of variational algorithms in this NISQ era and the next, we have proposed EVA: a quantum Exponential Value Approximation algorithm that speeds up the VQE, and that is, to date, the flagship of the quantum computation.