Quantum optimization

Bhagvan Kommadi

This chapter covers an overview of quantum optimization algorithms. You will see how quantum optimization is used in real life. Code examples are presented for the quantum optimization applications.

Jesse Van Griensven Thé, Roydon Andrew Fraser, José R. Rosas-Bustos

Jincheng Zhang

Quantum Optimization Algorithms (QOA) represent a paradigm shift in optimization techniques, leveraging the unique properties of quantum mechanics to potentially achieve exponential speedups compared to classical algorithms. This paper introduces a novel QOA framework based on the manipulation of quantum entanglement to facilitate efficient and robust optimization. We outline the core mechanism, detailing the algorithmic steps and their mathematical foundations. The research explores the potential of QOA to address limitations in traditional optimization methods, particularly concerning the complexity of certain optimization problems. This work serves as a foundation for further investigation and development into practical quantum optimization solutions.

Van-Quang-Huy Nguyen, Hoang-Quan Nguyen, Samee U. Khan +2 more

Quantum computing has demonstrated its potential to solve various optimization problems, including drone scheduling, which is important not only for drone delivery but also for logistics in general. However, one of the main obstacles is that practical drone scheduling settings typically require quantum resources that current hardware cannot provide. Therefore, in this work, we introduce a new Quantum Optimization via Coordinate Descent (QUACOD) approach to address this problem under the constraint of a limited number of available qubits. By leveraging coordinate descent, QUACOD decomposes the original high-complexity problem into multiple subproblems, which are then solved using quantum optimization. In our experiments, QUACOD outperforms the state-of-the-art (SOTA) quantum-based drone scheduling method not only in optimized drone completion times but also in scalability, handling up to 5 times more drones and 35 times more routes. In addition, QUACOD demonstrates that hardware-efficient circuits are effective for optimization problems. Together, these contributions advance quantum computing toward practical applications in the noisy intermediate-scale quantum (NISQ) era.

Ojas Parekh

Tomoyuki Yamakami