Quantum Optimization Techniques

Euimin Lee, Shiho Kim

Pooja P. Kadam, Sachin Kadam

Wim van Dam, Karim Eldefrawy, Nicholas Genise +1 more

This paper introduces two techniques that make the standard Quantum Approximate Optimization Algorithm (QAOA) more suitable for constrained optimization problems. The first technique describes how to use the outcome of a prior greedy classical algorithm to define an initial quantum state and mixing operation to adjust the quantum optimization algorithm to explore the possible answers around this initial greedy solution. The second technique is used to nudge the quantum exploration to avoid the local minima around the greedy solutions. To analyze the benefits of these two techniques we run the quantum algorithm on known hard instances of the Knapsack Problem using unit depth quantum circuits. The results show that the adjusted quantum optimization heuristics typically perform better than various classical heuristics.

Pradeep Kumar Pandey, Ruchi Chaturvedi, Suraj Bhan Dangi

The twenty-first century has been the era of data. Algorithms are crucial in tasks ranging from simple document searches to complex batching and scheduling jobs. Optimization techniques are often applied to enhance algorithms and achieve better results. As Moore rightly predicted, the exponential increase in transistors has led to a point where classical computers can no longer solve specific problems within a human timeframe. This paved the way for the development of quantum computers, which utilize quantum phenomena to solve problems. Quantum optimization techniques and algorithms have been designed to leverage the quantum advantage for improved optimization. This research compares and presents the results of quantum optimization techniques applied to classic combinatorial algorithms.

Tad Hogg, Dmitriy Portnov

Sunita S. Biswal, Dipak Ranjan Swain, Pravat Kumar Rout +2 more

This article comprehensively reviews the recently developed quantum mechanics-based optimization techniques. The quantum theory has been employed to speed up the evolutionary method and enhance the possibility of finding the optimal solution of conventional optimization techniques. Current developments in quantum computing have demonstrated that quantum theory can offer significant benefits over conventional theory for certain optimization techniques. The future prospective and benefits and drawbacks of this fresh category of quantum optimization techniques have been presented in this review. It also enables new researchers and algorithm developers to use these simple but extremely effective algorithms for problem-solving. This paper aims to give readers an overview of the fundamental elements and recent advances in optimization techniques so that they can develop and implement these for various applications. Finally, a few findings are reached, and future study on quantum-based optimization techniques is discussed.