Quick Navigation
Topics
Quantum Optimization
Eclipse Qrisp QAOA: description and preliminary comparison with Qiskit counterparts
arXiv
Authors: Eneko Osaba, Matic Petrič, Izaskun Oregi, Raphael Seidel, Alejandra Ruiz, Sebastian Bock, Michail-Alexandros Kourtis
Year
2024
Paper ID
67104
Status
Preprint
Abstract Read
~2 min
Abstract Words
102
Citations
N/A
Abstract
This paper focuses on the presentation and evaluation of the high-level quantum programming language Eclipse Qrisp. The presented framework, used for developing and compiling quantum algorithms, is measured in terms of efficiency for its implementation of the Quantum Approximation Optimization Algorithm (QAOA) Module. We measure this efficiency and compare it against two alternative QAOA algorithm implementations using IBM's Qiskit toolkit. The evaluation process has been carried out over a benchmark composed of 15 instances of the well-known Maximum Cut Problem. Through this preliminary experimentation, Eclipse Qrisp demonstrated promising results, outperforming both versions of its counterparts in terms of results quality and circuit complexity.
Why This Paper Matters
- This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- This paper focuses on the presentation and evaluation of the high-level quantum programming language Eclipse Qrisp.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.