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

Lecture notes in networks and systems

A Genetic Algorithm for Quantum Circuit Generation in OpenQASM

Teong Joo Ong, Chye Cheah Tan

Published Aug 2, 2021• 1 citation
Publisher
DOI
10.1007/978-3-030-82196-8_8

Related papers

Genetic algorithm for quantum circuit design

Taro YABUKI, Hitoshi Iba

IEICE technical report. Artificial intelligence and knowledge-based processing•May 19, 2000•3 citations

Automatic Synthesis for Quantum Circuits Using Genetic Algorithms

Cristian Ruican, Mihai Udrescu, Lucian Prodan +1 more

Lecture notes in computer science•Jul 3, 2007•21 citations

Compiling Single Round QCCP-X Quantum Circuits by Genetic Algorithm

Lis Arufe, Riccardo Rasconi, Angelo Oddi +2 more

Lecture notes in computer science•Jan 1, 2022•3 citations

Genetic Algorithm based synthesis of ternary Reversible/Quantum circuit

Rashida Khanoma, Tahseen Kamalb, Mozammel H. A. Khan

Reversible/quantum circuits are believed to be one of the future computer technologies. In this paper, a genetic algorithm (GA) based synthesis of ternary reversible/quantum circuits using Muthukrishnan-Stroud gates is presented. The circuit generated by GA may contain redundant gates. We have used post GA reduction to eliminate these redundant gates. We have experimented with ternary half-adder circuit. The proposed GA converges for many combinations of crossover and mutation.

International Conference on Computer and Information Technology (ICCIT)•Dec 1, 2008•12 citations

Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

Tom Rindell, Berat Yenilen, Niklas Halonen +3 more

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .

arXiv:2210.06411•Oct 12, 2022•1 citation

Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

Tom Rindell, Berat Yenilen, Niklas Halonen +3 more

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga.

Physics Letters A•Apr 24, 2023•11 citations
We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .
We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga.