Zobrazeno 1 - 10
of 200
pro vyhledávání: '"EIDENBENZ, STEPHAN"'
The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm designed to solve combinatorial optimization problems. However, a key limitation of QAOA is that it is a "local algorithm," meaning it can only optimize over loc
Externí odkaz:
http://arxiv.org/abs/2411.05216
We study the challenge of predicting the time at which a competitor product, such as a novel high-capacity EV battery or a new car model, will be available to customers; as new information is obtained, this time-to-market estimate is revised. Our sce
Externí odkaz:
http://arxiv.org/abs/2411.04266
Maximin fairness is the ideal that the worst-off group (or individual) should be treated as well as possible. Literature on maximin fairness in various decision-making settings has grown in recent years, but theoretical results are sparse. In this pa
Externí odkaz:
http://arxiv.org/abs/2410.02589
Autor:
Tate, Reuben, Eidenbenz, Stephan
In order to boost the performance of the Quantum Approximate Optimization Algorithm (QAOA) to solve problems in combinatorial optimization, researchers have leveraged the solutions returned from classical algorithms in order to create a warm-started
Externí odkaz:
http://arxiv.org/abs/2410.00027
This paper presents a numerical simulation investigation of the Warm-Start Quantum Approximate Optimization Algorithm (QAOA) as proposed by Tate et al. [1], focusing on its application to 3-regular Max-Cut problems. Our study demonstrates that Warm-S
Externí odkaz:
http://arxiv.org/abs/2409.09012
Quantum computing is an emerging field on the multidisciplinary interface between physics, engineering, and computer science with the potential to make a large impact on computational intelligence (CI). The aim of this paper is to introduce quantum a
Externí odkaz:
http://arxiv.org/abs/2407.07202
Autor:
Bärtschi, Andreas, Caravelli, Francesco, Coffrin, Carleton, Colina, Jonhas, Eidenbenz, Stephan, Jayakumar, Abhijith, Lawrence, Scott, Lee, Minseong, Lokhov, Andrey Y., Mishra, Avanish, Misra, Sidhant, Morrell, Zachary, Mughal, Zain, Neill, Duff, Piryatinski, Andrei, Scheie, Allen, Vuffray, Marc, Zhang, Yu
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural to presume that such computing technologies would be valuable to large
Externí odkaz:
http://arxiv.org/abs/2406.06625
Autor:
Aktar, Shamminuj, Bärtschi, Andreas, Oyen, Diane, Eidenbenz, Stephan, Badawy, Abdel-Hameed A.
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full pot
Externí odkaz:
http://arxiv.org/abs/2405.08100
Theoretical Approximation Ratios for Warm-Started QAOA on 3-Regular Max-Cut Instances at Depth $p=1$
Autor:
Tate, Reuben, Eidenbenz, Stephan
We generalize Farhi et al.'s 0.6924-approximation result technique of the Max-Cut Quantum Approximate Optimization Algorithm (QAOA) on 3-regular graphs to obtain provable lower bounds on the approximation ratio for warm-started QAOA. Given an initial
Externí odkaz:
http://arxiv.org/abs/2402.12631
Publikováno v:
21st ACM International Conference on Computing Frontiers CF'24, pages 199-206, May 2024
The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifying input parameters that yield high-quality solutions. However, underst
Externí odkaz:
http://arxiv.org/abs/2402.10188