Rapidly trainable and shallow-compiled quantum approximate optimization algorithm for maximum likelihood detection
In multiple-input and multiple-output (MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becomes increasingly complex with more transmitting antennas and symbols. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy inter...
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| Vydáno v: | Physics letters. A Ročník 548; s. 130541 |
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| Jazyk: | angličtina |
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Elsevier B.V
15.07.2025
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| ISSN: | 0375-9601 |
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| Abstract | In multiple-input and multiple-output (MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becomes increasingly complex with more transmitting antennas and symbols. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy intermediate-scale quantum (NISQ) devices, can show quantum advantage for approximately solving combinatorial optimization problems. In this paper, we propose an improved QAOA based maximum likelihood detection. In the proposed scheme, we use ZX-calculus to prove the parameter symmetry in QAOA circuits, which can be used to reduce the search space and accelerate the training process. Moreover, to run QAOA on quantum devices, an improved qubit mapping method with simultaneous gate absorption is proposed, which can compile the quantum circuit of the QAOA to satisfy the connectivity constraints of real quantum devices with fewer CNOT counts. In numerical experiments, our scheme accelerates parameter training by an average of 29.8% and uses fewer CNOT gates and shallower circuit depth during compilation. This demonstrates that our scheme has significant advantages over the traditional scheme.
•Use ZX-calculus to demonstrate the symmetry of parameters, which can be leveraged to accelerate parameter training.•Propose a circuit optimization enhanced qubit mapping method to reduce the number of CNOT gates during circuit compilation.•Simulations show the scheme boosts training speed and reduces CNOT count and circuit depth during compilation. |
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| AbstractList | In multiple-input and multiple-output (MIMO) systems, the maximum likelihood (ML) detection problem is NP-hard and becomes increasingly complex with more transmitting antennas and symbols. The quantum approximate optimization algorithm (QAOA), a leading candidate algorithm running in the noisy intermediate-scale quantum (NISQ) devices, can show quantum advantage for approximately solving combinatorial optimization problems. In this paper, we propose an improved QAOA based maximum likelihood detection. In the proposed scheme, we use ZX-calculus to prove the parameter symmetry in QAOA circuits, which can be used to reduce the search space and accelerate the training process. Moreover, to run QAOA on quantum devices, an improved qubit mapping method with simultaneous gate absorption is proposed, which can compile the quantum circuit of the QAOA to satisfy the connectivity constraints of real quantum devices with fewer CNOT counts. In numerical experiments, our scheme accelerates parameter training by an average of 29.8% and uses fewer CNOT gates and shallower circuit depth during compilation. This demonstrates that our scheme has significant advantages over the traditional scheme.
•Use ZX-calculus to demonstrate the symmetry of parameters, which can be leveraged to accelerate parameter training.•Propose a circuit optimization enhanced qubit mapping method to reduce the number of CNOT gates during circuit compilation.•Simulations show the scheme boosts training speed and reduces CNOT count and circuit depth during compilation. |
| ArticleNumber | 130541 |
| Author | Liu, Yuxiang Zhang, Zaichen Yu, Xutao Wang, Lu Qian, Yinuo |
| Author_xml | – sequence: 1 givenname: Yuxiang orcidid: 0000-0003-3016-8114 surname: Liu fullname: Liu, Yuxiang organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China – sequence: 2 givenname: Yinuo surname: Qian fullname: Qian, Yinuo organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China – sequence: 3 givenname: Lu surname: Wang fullname: Wang, Lu organization: State Key Laboratory of Millimeter Waves, Southeast University, 210096, Nanjing, China – sequence: 4 givenname: Zaichen surname: Zhang fullname: Zhang, Zaichen email: zczhang@seu.edu.cn organization: National Mobile Communications Research Laboratory, Southeast University, Nanjing, 210096, China – sequence: 5 givenname: Xutao orcidid: 0000-0002-4903-4573 surname: Yu fullname: Yu, Xutao email: yuxutao@seu.edu.cn organization: State Key Laboratory of Millimeter Waves, Southeast University, 210096, Nanjing, China |
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| Cites_doi | 10.1109/TCOMM.2022.3185287 10.1103/PhysRevA.96.012335 10.1103/PhysRevA.101.012334 10.1007/s11128-020-02768-5 10.1109/TWC.2024.3383101 10.1007/s11128-023-04252-2 10.1103/RevModPhys.68.733 10.1088/2058-9565/ac44dd 10.1109/ACCESS.2019.2921522 10.22331/q-2022-12-07-870 10.21468/SciPostPhysCore.4.4.031 10.1109/TWC.2022.3189604 10.1109/MSP.2011.2178495 10.1103/PhysRevA.99.032311 10.1088/1367-2630/18/7/073011 10.1109/JSAC.2020.3007211 10.1140/epjqt/s40507-022-00131-4 10.1103/PhysRevA.104.012403 10.22331/q-2022-07-07-759 10.1088/2058-9565/ad152e |
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| Keywords | Quantum approximate optimization algorithm (QAOA) Parameter symmetry Maximum likelihood (ML) detection Compilation |
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