Cross-Platform Benchmarking of Near-Term Quantum Optimisation Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908909928710144 |
|---|---|
| author | McDowall, Kieran Kapourniotis, Theodoros Oliver, Christopher Lolur, Phalgun Georgopoulos, Konstantinos |
| author_facet | McDowall, Kieran Kapourniotis, Theodoros Oliver, Christopher Lolur, Phalgun Georgopoulos, Konstantinos |
| contents | Quantum computers show potential for achieving computational advantage over classical computers, with many candidate applications in combinatorial optimisation. We present an application level benchmarking framework for near-term quantum optimisation algorithms using a dense Quadratic Unconstrained Binary Optimisation (QUBO) materials science problem as a representative test-case. To solve this problem, we implement two methods, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA), on commercially-available gate-based and quantum annealing devices that are accessible via Quantum-Computing-as-a-Service (QCaaS) models. To analyse the performance of these algorithms, we use a toolbox of relevant metrics and compare performance against three classical algorithms. We employ quantum methods to solve fully-connected QUBOs of up to $72$ variables, and find that algorithm performance beyond this is restricted by device connectivity, noise and classical computation time overheads. The applicability of our approach goes beyond the selected configurational analysis test-case, and we anticipate that our approach will be of use for optimisation problems in general. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06885 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Platform Benchmarking of Near-Term Quantum Optimisation Algorithms McDowall, Kieran Kapourniotis, Theodoros Oliver, Christopher Lolur, Phalgun Georgopoulos, Konstantinos Quantum Physics Quantum computers show potential for achieving computational advantage over classical computers, with many candidate applications in combinatorial optimisation. We present an application level benchmarking framework for near-term quantum optimisation algorithms using a dense Quadratic Unconstrained Binary Optimisation (QUBO) materials science problem as a representative test-case. To solve this problem, we implement two methods, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA), on commercially-available gate-based and quantum annealing devices that are accessible via Quantum-Computing-as-a-Service (QCaaS) models. To analyse the performance of these algorithms, we use a toolbox of relevant metrics and compare performance against three classical algorithms. We employ quantum methods to solve fully-connected QUBOs of up to $72$ variables, and find that algorithm performance beyond this is restricted by device connectivity, noise and classical computation time overheads. The applicability of our approach goes beyond the selected configurational analysis test-case, and we anticipate that our approach will be of use for optimisation problems in general. |
| title | Cross-Platform Benchmarking of Near-Term Quantum Optimisation Algorithms |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.06885 |