Results 71 to 80 of about 3,959 (191)

Fast Simulation of High-Depth QAOA Circuits

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, 2023
Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA).
Danylo Lykov, Ruslan Shaydulin, Yue Sun, Yuri Alexeev, Marco Pistoia   +4 more
openaire   +2 more sources

Enhancing Distributed State Estimation of Power Grid With a Simplified Quantum Algorithm

IET Generation, Transmission &Distribution, Volume 20, Issue 1, January/December 2026.
This paper presents the application of the Harrow‐Hassidim‐Lloyd (HHL) algorithm and its simplified quantum circuit to distributed state estimation in power grids. The results show that the proposed approach successfully tackles distributed state estimation in a power grid, underscoring its potential as a practical quantum computing solution for this ...
Shyh‐Jier Huang, Po‐Wei Yang, Shen‐Kun Yen, Yong‐Tai Li   +3 more
wiley   +1 more source

Sampling frequency thresholds for the quantum advantage of the quantum approximate optimization algorithm

npj Quantum Information, 2023
We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p
Danylo Lykov, Jonathan Wurtz, Cody Poole, Mark Saffman, Tom Noel, Yuri Alexeev   +5 more
doaj   +1 more source

Proactively incremental-learning QAOA

, 2023
Solving optimization problems with high performance is the target of existing works of Quantum Approximate Optimization Algorithm (QAOA). With this intention, we propose an advanced QAOA based on incremental learning, where the training trajectory is proactively segmented into incremental phases.
Li, Lingxiao, Li, Jing, Song, Yanqi, Qin, Sujuan, Wen, Qiaoyan, Gao, Fei   +5 more
openaire   +2 more sources

A Structured Lifecycle Model for Quantum Software Engineering: Bridging Technical Challenges and Future Directions

IET Software, Volume 2026, Issue 1, 2026.
Quantum computing is one of the research areas progressing rapidly toward practical deployment, yet the engineering of scalable and reliable quantum software remains underdeveloped. Current quantum software engineering (QSE) practices are largely tools‐driven and ad hoc that providing limited support for managing probabilistic execution, hybrid quantum–
Hessa Alfraihi, Islam Zada, Mahmoud Ahmed, Sara Shahzad, Siwar Rekik, Abdullah Alshahrani, Nguyen Phi, Zahid Akhtar   +7 more
wiley   +1 more source

Quantum approximate optimization algorithms for maximum cut on low-girth graphs

Physical Review Research
Maximum cut (MaxCut) on graphs is a classic NP-hard problem. In quantum computing, Farhi, Gutmann, and Goldstone proposed the quantum approximate optimization algorithm (QAOA) for solving the MaxCut problem.
Tongyang Li, Yuexin Su, Ziyi Yang, Shengyu Zhang   +3 more
doaj   +1 more source

Recursive QAOA outperforms the original QAOA for the MAX-CUT problem on complete graphs

Quantum Information Processing
8 pages, 1 ...
Eunok Bae, Soojoon Lee
openaire   +2 more sources

Missing Puzzle Pieces in the Performance Landscape of the Quantum Approximate Optimization Algorithm [PDF]

Quantum
We consider the maximum cut and maximum independent set problems on random regular graphs in the infinite-size limit, and calculate the energy densities achieved by QAOA for high degrees up to $d=100$.
Elisabeth Wybo, Martin Leib
doaj   +1 more source

Enhancing Quantum Approximate Optimization Algorithm Through Manifold Optimization

Quantum Engineering, Volume 2026, Issue 1, 2026.
We propose the models of Riemannian manifold optimization techniques to enhance the performance of the quantum approximate optimization algorithm (QAOA) for combinatorial optimization problems on near‐term quantum devices. The approach leverages the intrinsic geometric structure of the problem domain, addressing the nonconvexity of the QAOA objective ...
Qingqing Yu, Yinhui Yu, Rong Jin, Srijoni Sengupta   +3 more
wiley   +1 more source

Dynamic programming in economics on a quantum annealer

Quantitative Economics, Volume 17, Issue 1, Page 1-37, January 2026.
We introduce novel algorithms for solving dynamic programming problems in economics on a quantum annealer, a specialized quantum computer used for combinatorial optimization. Quantum annealers begin in a superposition of all states and generate candidate global solutions in milliseconds, regardless of problem size.
Jesús Fernández‐Villaverde, Isaiah Hull   +1 more
wiley   +1 more source