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Training the quantum approximate optimization algorithm without access to a quantum processing unit
Open accessAbstract
In this paper, we eliminate the classical outer learning loop of the quantum approximate optimization algorithm (QAOA) and present a strategy to find good parameters for QAOA based on topological arguments of the problem graph and tensor network techniques. Starting from the observation of the concentration of control parameters of QAOA, we find a way to classically infer parameters which scales polynomially in the number of qubits and exponentially with the depth of the circuit. Using this strategy, the quantum processing unit (QPU) is only needed to sample from the final state of QAOA. This method paves the way for a variation-free version of QAOA and makes QAOA more practical for applications on NISQ devices. We investigate the performance of the proposed approach for the initial assumptions and its resilience with respect to situations where they are not fulfilled. Moreover, we investigate the applicability of our method beyond the scope of QAOA, in improving schedules for quantum annealing.
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“Several other approaches have been identified where the ansatz adaptations happen as a secondary effect, e.g., References [36, 48, 82, 131, 153].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [131]
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“As repeated calls to the quantum computer for updating the parameters are considered a major bottleneck for VQAs in general, Streif and Leib [131] suggested a different approach to QAOA by introducing a technique that avoids the outer loop optimization and therefore repeated calls to get optimal parameters.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [131]
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“The proposed method is based on the features of the according problem graph and makes use of tensor network techniques.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [131]
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“An adaptation to QAOA, which does not require exhaustive access to the quantum processing unit [131], has been used to solve the binary paint shop problem [132].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [131]
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Citations
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Comparison of C# and Qiskit Runtimes on K-Means Clustering
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“[7].”
Metadata
- Authors
- Streif, M., & Leib, M.
- Affiliations
- Volkswagen Group (Germany) (DE)
- Corresponding author
- Streif, Michael
- Leib, Martin
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2020-05-12
- Volume
- 5
- Issue
- 3
- Pages
- 034008
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W2969876173
- Cited by
- 125
- References
- 41
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 23 · 2023: 24 · 2022: 26 · 2021: 15 · 2020: 11
- FWCI
- 7.40
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Quantum Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Unions Horizon 2020 (828826)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- quantum approximate optimization algorithm, tensor networks, NISQ devices, variational quantum algorithms, hybrid quantum-classical optimization
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