A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA)
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866908999403700224 |
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| author | Madani, Raneem Lisser, Abdel Toffano, Zeno |
| author_facet | Madani, Raneem Lisser, Abdel Toffano, Zeno |
| contents | Continuous-variable (CV) quantum systems offer a natural framework for continuous optimization through their infinite-dimensional Hilbert spaces. In this paper, we propose the Complex Continuous-Variable Quantum Approximate Optimization Algorithm (CCV-QAOA), a variational framework operating in the complex domain that optimizes over complex decision variables. The method efficiently solves real and complex multivariate optimization problems. To demonstrate its versatility, we apply CCV-QAOA across a broad suite of optimization use cases, including convex quadratic minimization, scaling studies with circuit depth and cutoff dimension, constrained quadratic programs using penalty constructions, and non-convex benchmarks such as the Styblinski-Tang function and complex quartic landscapes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25950 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA) Madani, Raneem Lisser, Abdel Toffano, Zeno Quantum Physics Continuous-variable (CV) quantum systems offer a natural framework for continuous optimization through their infinite-dimensional Hilbert spaces. In this paper, we propose the Complex Continuous-Variable Quantum Approximate Optimization Algorithm (CCV-QAOA), a variational framework operating in the complex domain that optimizes over complex decision variables. The method efficiently solves real and complex multivariate optimization problems. To demonstrate its versatility, we apply CCV-QAOA across a broad suite of optimization use cases, including convex quadratic minimization, scaling studies with circuit depth and cutoff dimension, constrained quadratic programs using penalty constructions, and non-convex benchmarks such as the Styblinski-Tang function and complex quartic landscapes. |
| title | A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA) |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2604.25950 |