Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Bulletin of the American Physical Society

Improved variational algorithms for optimization problems in a quantum computer

Panagiotis Kl. Barkoutsos, Anton Robert, Giacomo Nannicini, Ivano Tavernelli, Stefan Woerner

Published Mar 5, 2019• 0 citations
OpenAlex

Related papers

Course Selection Optimization using pVSQA on Quantum Computers

Takeru Ota, Tatsuhiko Shirai, Nozomu Togawa

Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm capable of solving combinatorial optimization problems on gate-based quantum computers. Postprocessing Variationally Scheduled Quantum Algorithm (pVSQA) is an enhanced approach that combines a quantum variational algorithm like QAOA with a postprocessing step that transforms infeasible solutions into feasible ones. Compared to QAOA, pVSQA can more efficiently solve constrained combinatorial optimization problems. In this paper, we define the personalized course selection problem (PCSP), which involves finding the optimal combination of courses for a student, and then propose a method that integrates a postprocessing algorithm into pVSQA to effectively solve PCSP. Experimental results show that, compared to conventional QAOA, pVSQA significantly improves both the probability to obtain feasible solutions and the energy expectation among feasible solutions.

IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)•Sep 6, 2025•0 citations

Variational quantum algorithms: fundamental concepts, applications and challenges

Han Qi, Sihui Xiao, Zhuo Liu +2 more

Quantum Information Processing•Jun 3, 2024•37 citations

Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

Ling Qiu, Mahabubul Alam, Abdullah Ash Saki +1 more

Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.

arXiv:2004.12447•International Symposium on Low Power Electronics and Design (ISLPED)•Aug 7, 2020•6 citations

Variational quantum algorithms

Marco Cerezo, Andrew Arrasmith, Ryan Babbush +8 more

arXiv:2012.09265•Nature Reviews Physics•Aug 12, 2021•3,385 citations

Quantum-Inspired Optimization with Variational Apprenticeship

Jincheng Zhang

Quantum-Inspired Optimization with Variational Apprenticeship presents a novel approach to optimization that leverages the principles of quantum computing while incorporating the strengths of variational methods. This technique focuses on developing a surrogate function, guided by a variational algorithm, to efficiently explore the solution space and improve the performance of optimization algorithms. The core mechanism involves utilizing a quantum-inspired surrogate to approximate the objective function, while concurrently optimizing the solution itself through a variational approach. This combination offers the potential for enhanced exploration, faster convergence, and improved robustness compared to traditional optimization methods. The study demonstrates the efficacy of this method through simulations and theoretical analysis, showcasing its potential for tackling complex optimization problems across diverse domains.

•Sep 1, 2026•0 citations

Quantum Algorithms for Optimization Problems

Dilnoz Muhamediyeva, Rustam Raximov, Shavkat Urokov

Lecture notes in computer science•Jul 29, 2025•0 citations
Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm capable of solving combinatorial optimization problems on gate-based quantum computers. Postprocessing Variationally Scheduled Quantum Algorithm (pVSQA) is an enhanced approach that combines a quantum variational algorithm like QAOA with a postprocessing step that transforms infeasible solutions into feasible ones. Compared to QAOA, pVSQA can more efficiently solve constrained combinatorial optimization problems. In this paper, we define the personalized course selection problem (PCSP), which involves finding the optimal combination of courses for a student, and then propose a method that integrates a postprocessing algorithm into pVSQA to effectively solve PCSP. Experimental results show that, compared to conventional QAOA, pVSQA significantly improves both the probability to obtain feasible solutions and the energy expectation among feasible solutions.
Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.
Quantum-Inspired Optimization with Variational Apprenticeship presents a novel approach to optimization that leverages the principles of quantum computing while incorporating the strengths of variational methods. This technique focuses on developing a surrogate function, guided by a variational algorithm, to efficiently explore the solution space and improve the performance of optimization algorithms. The core mechanism involves utilizing a quantum-inspired surrogate to approximate the objective function, while concurrently optimizing the solution itself through a variational approach. This combination offers the potential for enhanced exploration, faster convergence, and improved robustness compared to traditional optimization methods. The study demonstrates the efficacy of this method through simulations and theoretical analysis, showcasing its potential for tackling complex optimization problems across diverse domains.