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 ansatz adaptations happen as a secondary effect.
“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]
Section 2.3 (p. 7) introduces tree-QAOA to infer QAOA parameters without repeated QPU execution, and Section 3.1 (p. 12) describes optimizing those parameters on a tree subgraph. This supports an indirect change to parameter selection, but our copy does not describe adapting the final QAOA ansatz as a secondary effect. (LLM, conf=0.78)
Experimental — accuracy not yet measuredStreif and Leib suggested a different approach to QAOA that avoids the outer loop optimization and therefore repeated calls to get optimal parameters.
“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]
The proposed method by Streif and Leib is based on the features of the according problem graph and makes use of tensor network techniques.
“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]
An adaptation to QAOA, which does not require exhaustive access to the quantum processing unit, has been used to solve the binary paint shop problem.
“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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BackgroundLayer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
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“The special structure of the QAOA ansatz enables finding high-quality parameters γ , β purely classically in many settings [4], [37], [38] or using very few iterations of the outer-loop optimizer [25], [39], [40].”
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“Thus, exploring new avenues to improve the performance of QAOA has become an exciting research domain [8]–[13].”
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“Thus, QAOA is a hybrid quantum-classical optimization algorithm in which proper Hamiltonian design and the discovery of good parameters in a classical optimization loop are key [20,23,24].”
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“The cost of finding parameters can be significantly reduced by leveraging the apparent problem-instance independence of the optimal QAOA parameters [26, 27].”
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“Due to these limitations, a variation-free version of the algorithm (VF-QAOA) has been pursued [2], [7], [9] where the optimal parameters are either found analytically [2], [7] or they are reused among a similar set of problems [9].”
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“Our analytical formula gives us the ability to analytically optimize the QAOA parameters—this enables one to compute the optimal QAOA parameters classically, and then only run the QAOA on a quantum processor with the optimal set of angles [9].”
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“Here we briefly review the mechanism for doing so; for more details and an in-depth discussion, the reader is referred to [13], [22].”
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“Streif and Leib [67] developed a method of determining optimal parameters for the QAO algorithm that aimed to reduce or even remove the need for a classical optimisation loop.”
MethodAn expressive ansatz for low-depth quantum approximate optimisation
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“Ideas that are similar to AQA have been investigated before [17, 21, 22, 46].”
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“B and C depend on the problem of interest and the angles that maximize them can be found using classical preprocessing [12, 21, 22].”
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“Recent results, however, suggest that angles may be precomputed on a classical device [16] or transferred from other similar graphs [17].”
BackgroundPolicy Gradient based Quantum Approximate Optimization AlgorithmPreprint
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Evaluation of QAOA based on the approximation ratio of individual samples
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“Along the same lines of avoiding the training phase, recent works propose the use of machine learning techniques [62, 63] or a mix of analytical and numerical results at N → ∞ [64] to identify good parameters.”
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Quantum algorithms with local particle-number conservation: Noise effects and error correction
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“[18] Michael Streif and Martin Leib.”
BackgroundSelf-citationSimilarity-based parameter transferability in the quantum approximate optimization algorithm
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Analytical framework for quantum alternating operator ansätze
Hadfield, S., Hogg, T., & Rieffel, E. G. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/aca3ce · Cited by 28 · see citing works
“This reduced number of necessary qubits and operators for computing expectation values is often called the (reverse) causal cone or lightcone in the literature [42, 53, 68–70], and relates to Lieb-Robinson bounds concerning the speed of propagation of information [71].”
Provable bounds for noise-free expectation values computed from noisy samples
Barron, S. V., Egger, D. J., Pelofske, E., Bärtschi, A., Eidenbenz, S., Lehmkuehler, M., & Woerner, S. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00709-1 · Cited by 25 · see citing works
“In certain cases it might be feasible to determine θ * classically 39–41 and only use the quantum computer to sample good solutions, since evaluating (local) expectation values might be easier than sampling from the full circuit.”
An investigation of IBM quantum computing device performance on combinatorial optimisation problems
Khumalo, M. T., Chieza, H. A., Prag, K., & Woolway, M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07438-4 · Cited by 27 · see citing works
“On the other hand, QAOA using quantum computers to prepare parameters means it uses more quantum functions and exposes the results to more error [34, 58].”
MethodSecure and Reliable Big-Data-Based Decision Making Using Quantum Approach in IIoT Systems
Azzaoui, A. E., Salim, M. M., & Park, J. H. (2023) · Sensors · doi:10.3390/s23104852 · Cited by 26 · see citing works
“…their clients and personalize unique products and commercials for them, which is regarded as the most essential information that almost every smart factory relies on in the era of social platforms. discovery, material research, finance analytics, precision medicine therapies, drug structure [21–23].”
Quantum approximate multi-objective optimization
Kotil, A., Pelofske, E., Riedmüller, S., Egger, D. J., Eidenbenz, S., Koch, T., & Woerner, S. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00873-y · Cited by 27 · see citing works
“For example, as proposed in [26] the expectation values for training QAOA parameters can be evaluated classically.”
Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation
Kim, J., Kwak, Y., Jung, S., & Kim, J. (2021) · IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS) · doi:10.1109/apwcs50173.2021.9548770 · Cited by 24 · see citing works
“…can be shown that the QAOA-based approximation is one of widely known hybrid quantum-classical optimization algorithms where the efficient Hamiltonian design (for quantum approach) and the approximation of efficient hyper-parameters (for conventional optimization approach) are correlated [29], [30].”
BackgroundQuantum-Assisted Solution Paths for the Capacitated Vehicle Routing Problem
Palackal, L., Poggel, B., Wulff, M., Ehm, H., Lorenz, J. M., & Mendl, C. B. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00080 · Cited by 24 · see citing works
“Instead of preparing a uniform superposition state | + ⊗ n , a classical solver obtains a solution to the relaxed continuous problem, ˜ x ∈ [0 , 1] n that, once encoded, serves as initial state where the angles are defined by the solution ˜ x by θ i = 2 arcsin √ ˜ x i .”
BackgroundScaling whole-chip QAOA for higher-order ising spin glass models on heavy-hex graphs
Pelofske, E., Bärtschi, A., Cincio, Ł., Golden, J. K., & Eidenbenz, S. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00906-w · Cited by 24 · see citing works
“This technique, often referred to as parameter transfer or parameter concentration , has been shown to be effective, both analytically and numerically, for a number of different problem types [12, 22, 25–33 , 38 ].”
MethodMahroo, R., & Kargarian, A. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3320872 · Cited by 22 · see citing works
“Various approaches are proposed for optimizing variational parameters, including gradient-based [28] and gradient-free meth-ods [29], [30].”
BackgroundMethodFalla, J., Langfitt, Q., Alexeev, Y., & Safro, I. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00178-9 · Cited by 22 · see citing works
“OptimalQAOAparameterconcentrationeffectshavebeen reported for several types of graphs on which the MaxCut is formulated, mainly those involving random 3-regular graphs (Brandao et al. 2018; Streif and Leib 2020; Akshay et al. 2021).”
Multilevel Leapfrogging Initialization Strategy for Quantum Approximate Optimization Algorithm
Ni, X., Cai, B., Liu, H., Qin, S., Gao, F., & Wen, Q. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202300419 · Cited by 23 · see citing works
Circuit depth scaling for quantum approximate optimization
Akshay, V., Philathong, H., Campos, E., Rabinovich, D., Zacharov, I., Zhang, X., & Biamonte, J. (2022) · Physical Review A · doi:10.1103/physreva.106.042438 · Cited by 22 · see citing works
Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22 · see citing works
“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].”
BackgroundMethodInfluentialEnergy-Efficient Cluster Head Selection via Quantum Approximate Optimization
Choi, J., Oh, S., & Kim, J. (2020) · Electronics · doi:10.3390/electronics9101669 · Cited by 20 · see citing works
Quantum dropout: On and over the hardness of quantum approximate optimization algorithm
Wang, Z., Zheng, P.-L., Wu, B., & Zhang, Y. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023171 · Cited by 20 · see citing works
Entanglement production and convergence properties of the variational quantum eigensolver
Woitzik, A. J. C., Barkoutsos, P. K., Wudarski, F., Buchleitner, A., & Tavernelli, I. (2020) · Physical Review A · doi:10.1103/physreva.102.042402 · Cited by 20 · see citing works
Alibaba Cloud Quantum Development Platform: Applications to Quantum Algorithm DesignPreprint
Huang, C., Szegedy, M., Zhang, F., Gao, X., Chen, J., & Shi, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.02559 · Cited by 20 · see citing works
“In fact, it is proposed in [16] that the angle sequences for small-cycle-free instances be used directly to completely get rid of the optimization phase, which could be chanllenging on noisy quantum devices.”
BackgroundModular Parity Quantum Approximate Optimization
Ender, K., Messinger, A., Fellner, M., Dlaska, C., & Lechner, W. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.030304 · Cited by 18 · see citing works
Parity Quantum Optimization: Benchmarks
FELLNER, M. J., Ender, K., ter Hoeven, R., & Lechner, W. (2023) · Quantum · doi:10.22331/q-2023-03-17-952 · Cited by 23 · see citing works
“Recent research also indicates parameter concentration in the QAOA protocol which means that good QAOA parameters found for (small) problem instances are also adequate for other, potentially larger instances [7, 8, 24, 25].”
BackgroundChallenges and Opportunities in Quantum Software Architecture
Yue, T., Mauerer, W., Ali, S., & Taibi, D. (2023) · Springer eBooks · doi:10.1007/978-3-031-36847-9_1 · Cited by 17 · see citing works
Miki, T., Okita, R., Shimada, M., Tsukayama, D., & Shirakashi, J. (2022) · AIP Advances · doi:10.1063/5.0077706 · Cited by 16 · see citing works
Quantum k-community detection: algorithm proposals and cross-architectural evaluation
Gemeinhardt, F., Wille, R., & Wimmer, M. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03239-1 · Cited by 15 · see citing works
Recommending Solution Paths for Solving Optimization Problems with Quantum Computing
Poggel, B., Quetschlich, N., Burgholzer, L., Wille, R., & Lorenz, J. M. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00017 · Cited by 15 · see citing works
“As a first recommendation, we choose the QAOA depth such that all qubits can be entangled by the problem Hamiltonian evolution according to a reverse causal cone argument [6], [27].”
BackgroundParameter-setting heuristic for the quantum alternating operator ansatz
Sud, J., Hadfield, S., Rieffel, E. G., Tubman, N. M., & Hogg, T. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023171 · Cited by 17 · see citing works
“The first of these include initializing parameters to be close to parameters informed by previous information or intuition [22–24].”
Multilevel Combinatorial Optimization Across Quantum Architectures
Ushijima‐Mwesigwa, H., Shaydulin, R., Negre, C. F. A., Mniszewski, S. M., Alexeev, Y., & Safro, I. (2019) · TigerPrints (Clemson University) · Cited by 15 · see citing works
Sawaya, N. P. D., Schmitz, A. T., & Hadfield, S. (2023) · Quantum · doi:10.22331/q-2023-09-14-1111 · Cited by 16 · see citing works
“The algorithm parameters may be predetermined by analytic, empirical, or other means [51, 90, 57, 91, 92, 93, 94, 95, 96, 97, 98], or determined variationally using a hybrid quantum-classical search procedure [27].”
MethodProblem-specific classical optimization of Hamiltonian simulation
Mansuroglu, R., Fischer, F., & Hartmann, M. J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043035 · Cited by 13 · see citing works
Grover-QAOA for 3-SAT: quadratic speedup, fair-sampling, and parameter clustering
Zhang, Z., Paredes, R., Sundar, B., Quiroga, D., Kyrillidis, A., Dueñas‐Osorio, L., Pagano, G., & Hazzard, K. R. A. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad895c · Cited by 17 · see citing works
“While parameter clustering has previously been seen in different QAOA variants, these focused on only few-round results [23, 24, 77].”
Lyngfelt, I., & García-Álvarez, L. (2025) · Physical Review A · doi:10.1103/physreva.111.022418 · Cited by 12 · see citing works
Convergence of digitized-counterdiabatic QAOA: circuit depth versus free parameters
Vizzuso, M., Passarelli, G., Cantele, G., & Lucignano, P. (2023) · New Journal of Physics · doi:10.1088/1367-2630/ad1536 · Cited by 12 · see citing works
Solving Quadratic Unconstrained Binary Optimization with divide-and-conquer and quantum algorithmsPreprint
Guerreschi, G. G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.07813 · Cited by 17 · see citing works
“The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm designed to find approximate solutions of combinatorial optimzation problems by variationally improving quantum circuits [16–21].”
MethodCapturing Symmetries of Quantum Optimization Algorithms Using Graph Neural Networks
Deshpande, A., & Melnikov, A. A. (2022) · Symmetry · doi:10.3390/sym14122593 · Cited by 11 · see citing works
Approximating under the Influence of Quantum Noise and Compute Power
Thelen, S., Safi, H., & Mauerer, W. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10291 · Cited by 10 · see citing works
“[4, 11, 16–20]) have been proposed.”
Problem-size-independent angles for a Grover-driven quantum approximate optimization algorithm
Headley, D., & Wilhelm, F. K. (2023) · Physical Review A · doi:10.1103/physreva.107.012412 · Cited by 10 · see citing works
Cadavid, A. G., Montalban, I., Dalal, A., Solano, E., & Hegade, N. N. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.054037 · Cited by 9 · see citing works
“There is extensive literature on how PQCs can be trained and modified in order to circumvent those issues [12–14].”
BackgroundGenuine multipartite entanglement in quantum optimization
Santra, G. C., Roy, S. S., Egger, D. J., & Hauke, P. (2025) · Physical Review A · doi:10.1103/physreva.111.022434 · Cited by 8 · see citing works
“In particular, tensor networks may help to obtain good parameters [64].”
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum AlgorithmsPreprint
Rudolph, M. S., Sim, S., Raza, A., Stęchły, M., McClean, J. R., Anschuetz, E. R., Serrano, L., & Perdomo‐Ortiz, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.04695 · Cited by 14 · see citing works
Beyond quantum annealing: optimal control solutions to maxcut problems
Pecci, G., Wang, R., Torta, P., Mbeng, G. B., & Santoro, G. E. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad60f2 · Cited by 8 · see citing works
Quantifying the impact of precision errors on quantum approximate optimization algorithms
Quiroz, G., Titum, P., Lotshaw, P. C., Lougovski, P., Schultz, K., Dumitrescu, E., & Hen, I. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023240 · Cited by 10 · see citing works
Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective
Carbonelli, C., Felderer, M., Jung, M., Lobe, E., Lochau, M., Luber, S. M., Mauerer, W., Ramler, R., Schaefer, I., & Schroth, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-64136-7_12 · Cited by 7 · see citing works
Diagrammatic Analysis for Parameterized Quantum Circuits
Stollenwerk, T., & Hadfield, S. (2023) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.394.15 · Cited by 7 · see citing works
“This phenomena is known, generally, as the lightcone or causal cone rule [20, 21, 27, 45], and is clearly exhibited with the ZX-calculus.”
BackgroundStreif, M., & Leib, M. (2020) · Physical Review A · doi:10.1103/physreva.102.042416 · Cited by 7 · see citing works
“[16] Michael Streif and Martin Leib.”
MethodSelf-citationQuantum Approximate Optimization Algorithm with Sparsified Phase Operator
Liu, X., Shaydulin, R., & Safro, I. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00032 · Cited by 6 · see citing works
Measurement-Based Control for Minimizing Energy Functions in Quantum Systems
Clausen, H. G., Rahman, S. A., Karabacak, Ö., & Wiśniewski, R. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.111 · Cited by 6 · see citing works
“, Streif and Leib (2020) for results suggesting such relations).”
BackgroundLarkin, J. M., Jonsson, M., Justice, D., & Guerreschi, G. G. (2020) · arXiv (Cornell University) · Cited by 6 · see citing works
Engineering a Cost Function for Real-world Implementation of a Variational Quantum Algorithm
Premaratne, S., & Matsuura, A. Y. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00042 · Cited by 5 · see citing works
“Concentration of optimal parameters has been observed in training with QAOA, which lends credence to the reuse of previously found optimal parameters in subsequent evaluations [2], [19].”
Parallel circuit implementation of variational quantum algorithms
Cattelan, M., Yarkoni, S., & Lechner, W. (2025) · npj Quantum Information · doi:10.1038/s41534-025-00982-6 · Cited by 8 · see citing works
Constant Depth Code Deformations in the Parity Architecture
Messinger, A., Fellner, M., & Lechner, W. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00022 · Cited by 5 · see citing works
Miki, T., Tsukayama, D., Okita, R., Shimada, M., & Shirakashi, J. (2022) · IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO) · doi:10.1109/3m-nano56083.2022.9941666 · Cited by 3 · see citing works
Restricted global optimization for QAOA
Gleißner, P., Kruse, G., & Roßkopf, A. (2024) · APL Quantum · doi:10.1063/5.0189374 · Cited by 3 · see citing works
“This includes the concepts from [10, 17, 27] where this strategy is successfully demonstrated.”
BackgroundMethodInfluentialChoco-Q: Commute Hamiltonian-based QAOA for Constrained Binary Optimization
Xiang, D., Jiang, Q., Lu, L., Tan, S., & Yin, J. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00031 · Cited by 3 · see citing works
“Since QAOA can be deployed on current noisy quantum devices, it has been studied and optimized from different aspects, such as compilation [3], parameter initialization [45], parameter updating [42], and distributed version [4].”
Krüger, T., & Mauerer, W. (2025) · Quantum · doi:10.22331/q-2025-11-06-1903 · Cited by 3 · see citing works
“In 2020, Streif and Leib [84] observed a clustering of optimal parameters when training qaoa circuits for random Max-Cut instances, which inspired them to propose training qaoa without quantum hardware access.”
InfluentialAn Efficient QAOA via a Polynomial QPU-Needless Approach
Chicano, F., Dahi, Z. A., & Luque, G. (2023) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3583133.3596409 · Cited by 3 · see citing works
Measurement-Based Quantum Approximate Optimization
Stollenwerk, T., & Hadfield, S. (2024) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw63119.2024.00183 · Cited by 3 · see citing works
“…algorithm performance will depend on the finding high-quality circuit parameters to begin with, and we emphasize that in different cases parameters may be found through analytic [40], numeric [4], or average-case [41] techniques, among others, though parameter setting remains challenging generally.”
BackgroundA Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
Agirre, A., van Nieuwenburg, E., & Wauters, M. M. (2025) · New Journal of Physics · doi:10.1088/1367-2630/adc765 · Cited by 3 · see citing works
“The crucial modification to MCTS aims at exploiting the parameter concentration and regularity inherent in many optimal QAOA schedules [27, 48–50].”
Missing Puzzle Pieces in the Performance Landscape of the Quantum Approximate Optimization Algorithm
Wybo, E., & Leib, M. (2025) · Quantum · doi:10.22331/q-2025-10-22-1892 · Cited by 5 · see citing works
Self-citationPredict and Conquer: Navigating Algorithm Trade-Offs with Quantum Design Automation
Thelen, S., & Mauerer, W. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00071 · Cited by 2 · see citing works
“[6], [7], [30]–[34]).”
Quantifying the advantages of applying quantum approximate algorithms to portfolio optimisation
Yuan, H., Long, C. K., Lepage, H. V., & Barnes, C. H. W. (2026) · Quantum Science and Technology · doi:10.1088/2058-9565/ae4a48 · Cited by 2 · see citing works
Kasi, S., Sud, J., Jamieson, K., & Ravi, G. S. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00051 · Cited by 2 · see citing works
“The performance of QAOA relies on the choice of parameters ( ⃗γ, ⃗β ) , and techniques for parameter optimization are in many cases highly non-trivial [45]–[54].”
- 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 · 2019: 2
- 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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