Quantum Manifold Optimization: A Design Framework for Future Communications Systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917984545538048 |
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| author | Rexhepi, Getuar Rou, Hyeon Seok de Abreu, Giuseppe Thadeu Freitas |
| author_facet | Rexhepi, Getuar Rou, Hyeon Seok de Abreu, Giuseppe Thadeu Freitas |
| contents | Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_09667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Manifold Optimization: A Design Framework for Future Communications Systems Rexhepi, Getuar Rou, Hyeon Seok de Abreu, Giuseppe Thadeu Freitas Signal Processing Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems. |
| title | Quantum Manifold Optimization: A Design Framework for Future Communications Systems |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.09667 |