The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
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
Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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A variational eigenvalue solver on a photonic quantum processor
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“There exist many versions tailored for different fields of applications, such as the variational quantum eigensolver (VQE) [8] for finding the minimal energy state in quantum chemistry applications or quantum neural networks (QNNs) [9, 10] for quantum machine learning applications.”
BackgroundMethodIsing formulations of many NP problems
Lucas, A. (2014) · Frontiers in Physics · doi:10.3389/fphy.2014.00005 · Cited by 2,512
Orús, R. (2014) · Annals of Physics · doi:10.1016/j.aop.2014.06.013 · Cited by 2,255
QuTiP 2: A Python framework for the dynamics of open quantum systems
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A Quantum Approximate Optimization Algorithm
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Cited by
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A review on Quantum Approximate Optimization Algorithm and its variants
Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.03.002 · Cited by 563
Layerwise learning for quantum neural networks
Skolik, A., McClean, J. R., Mohseni, M., van der Smagt, P., & Leib, M. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00036-4 · Cited by 324
Self-citationDiagnosing Barren Plateaus with Tools from Quantum Optimal Control
Larocca, M., Czarnik, P., Sharma, K., Muraleedharan, G., Coles, P. J., & Cerezo, M. (2022) · Quantum · doi:10.22331/q-2022-09-29-824 · Cited by 272
Shaydulin, R., Li, C., Chakrabarti, S., DeCross, M., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adm6761 · Cited by 120
Counterdiabaticity and the quantum approximate optimization algorithm
Wurtz, J., & Love, P. J. (2022) · Quantum · doi:10.22331/q-2022-01-27-635 · Cited by 124
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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