Recognition: unknown
Recent Advances in Quantum Architecture Search
Pith reviewed 2026-05-07 13:29 UTC · model grok-4.3
The pith
Quantum architecture search automates the discovery of high-performing variational quantum circuit structures.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The paper states that QAS has emerged as a critical area to automate the discovery of high-performing circuit structures for variational quantum algorithms. It reviews core concepts, representative methodologies, and applications while structuring the material for broad accessibility. The review additionally discusses remaining challenges and suggests potential research directions in this field.
What carries the argument
Quantum Architecture Search (QAS), an automated process that explores and selects effective variational quantum circuit architectures using search algorithms.
If this is right
- QAS methods can reduce reliance on manual circuit design for variational quantum algorithms.
- Different search strategies enable adaptation to specific quantum computing tasks.
- Applications in areas such as optimization and simulation become more practical through automated design.
- Addressing scalability and noise issues will expand the range of solvable problems.
Where Pith is reading between the lines
- QAS frameworks could be combined with hardware-aware constraints to tailor circuits directly to specific quantum devices.
- Standardized test problems would allow clearer comparisons across different search approaches.
- The reviewed challenges suggest that hybrid classical-quantum search methods may lower computational overhead.
- Extending QAS beyond variational algorithms could streamline design for other quantum protocols.
Load-bearing premise
The chosen examples of methodologies and applications represent the current state of QAS research without major omissions or selection bias.
What would settle it
Publication of a major QAS methodology or application from the review period that is absent from the survey would demonstrate incompleteness.
read the original abstract
Variational quantum algorithms (VQAs) constitute a prominent framework for exploring the capabilities of near-term quantum computers. As the effectiveness of VQAs depends heavily on the design of variational quantum circuits, Quantum Architecture Search (QAS) has emerged as a critical research area to automate the discovery of high-performing circuit structures. This paper reviews key advancements in current research on QAS, including its core concepts, representative methodologies, and applications. The content is structured to ensure broad accessibility for a diverse audience of researchers while preserving the core principles of complex methodologies. In addition, we discuss remaining challenges and suggest potential research directions to offer perspectives on future exploration in this rapidly evolving field.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript is a review paper on Quantum Architecture Search (QAS) for variational quantum algorithms (VQAs). It claims to cover the emergence of QAS as a means to automate discovery of high-performing variational quantum circuit structures, along with core concepts, representative methodologies, applications, remaining challenges, and suggested future research directions, while aiming for accessibility to a broad audience.
Significance. If the reviewed methodologies and applications are accurately and representatively selected, the paper could provide a useful entry point for researchers working on near-term quantum computing and VQAs by synthesizing advancements in automated circuit design. The review does not introduce new theorems, derivations, or empirical results but instead organizes existing literature; its value therefore hinges entirely on the fidelity and breadth of coverage rather than on novel technical contributions.
major comments (1)
- [Abstract and Introduction] The abstract and introduction assert that the paper reviews 'representative methodologies' and 'key advancements' without describing the literature search protocol, databases queried, keywords, date range, or inclusion/exclusion criteria. This omission is load-bearing for the central claim of providing a reliable overview, as it leaves the selection process opaque and prevents evaluation of completeness or bias.
Simulated Author's Rebuttal
We thank the referee for their constructive review and for recognizing the potential utility of our survey as an accessible entry point into Quantum Architecture Search for near-term quantum computing researchers. We address the single major comment below and commit to revisions that improve transparency without altering the manuscript's core scope or accessibility focus.
read point-by-point responses
-
Referee: [Abstract and Introduction] The abstract and introduction assert that the paper reviews 'representative methodologies' and 'key advancements' without describing the literature search protocol, databases queried, keywords, date range, or inclusion/exclusion criteria. This omission is load-bearing for the central claim of providing a reliable overview, as it leaves the selection process opaque and prevents evaluation of completeness or bias.
Authors: We agree that explicitly documenting the literature search methodology strengthens the credibility of any review claiming representative coverage. Although our selection was guided by a systematic process (focusing on high-impact works from 2018 onward via arXiv, Google Scholar, and IEEE Xplore using keywords such as 'Quantum Architecture Search', 'QAS', 'variational quantum circuit design', and 'automated quantum circuit optimization', with inclusion limited to peer-reviewed or preprint works directly addressing QAS for VQAs), we acknowledge that omitting these details reduces transparency. In the revised version we will insert a dedicated subsection (likely in the Introduction) that fully specifies the databases, keywords, date range, and inclusion/exclusion criteria. This addition will be concise yet sufficient for readers to evaluate coverage and potential bias. revision: yes
Circularity Check
Review paper presents no derivations, predictions, or fitted quantities; no circularity present.
full rationale
This manuscript is a high-level survey of external literature on Quantum Architecture Search. It contains no original equations, no parameter fitting, no predictions derived from internal models, and no derivation chain that could reduce to its own inputs by construction. The central content consists of summaries of prior work by other authors, with discussion of challenges and directions. No self-citation functions as a load-bearing uniqueness theorem or ansatz that the paper then treats as independently derived. The absence of any internal mathematical structure means none of the enumerated circularity patterns (self-definitional, fitted-input-called-prediction, etc.) can apply. The review's representativeness is a separate methodological concern but does not constitute circularity under the defined criteria.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
-
[1]
Altares-L´ opez, J
S. Altares-L´ opez, J. J. Garc´ ıa-Ripoll, and A. Ribeiro. AutoQML: Automatic generation and training of ro- bust quantum-inspired classifiers by using evolutionary algorithms on grayscale images.Expert Syst. Appl., 244:122984, 2024
2024
-
[2]
Altares-L´ opez, A
S. Altares-L´ opez, A. Ribeiro, and J. J. Garc´ ıa-Ripoll. Automatic design of quantum feature maps.Quantum Sci. Technol., 6(4):045015, 2021
2021
-
[3]
Anagolum, N
S. Anagolum, N. Alavisamani, P. Das, M. Qureshi, and Y. Shi. ´Eliv´ agar: Efficient quantum circuit search for classification. InProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), volume 2, pages 336–353, 2024
2024
-
[4]
Bi, Y.-M
X.-Y. Bi, Y.-M. Yu, Y.-H. Chen, and Z.-R. Zhong. General-purpose quantum architecture search based on deep reinforcement learning.Phys. Rev. A, 112(5):052409, 2025
2025
-
[5]
Bilkis, M
M. Bilkis, M. Cerezo, G. Verdon, P. J. Coles, and L. Cincio. A semi-agnostic ansatz with variable structure for variational quantum algorithms.Quantum Mach. Intell., 5(2):43, 2023
2023
-
[6]
Bravo-Prieto, R
C. Bravo-Prieto, R. LaRose, M. Cerezo, Y. Subasi, L. Cincio, and P. J. Coles. Variational quantum linear solver.Quantum, 7:1188, 2023
2023
-
[7]
Caleffi, M
M. Caleffi, M. Amoretti, D. Ferrari, J. Illiano, A. Manzalini, and A. S. Cacciapuoti. Distributed quantum computing: A survey.Comput. Netw., 254:110672, 2024
2024
-
[8]
Cerezo, A
M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, and P. J. Coles. Variational quantum algorithms.Nat. Rev. Phys., 3(9):625–644, 2021
2021
-
[9]
S. Y.-C. Chen, C.-H. H. Yang, J. Qi, P.-Y. Chen, X. Ma, and H.-S. Goan. Variational quantum circuits for deep reinforcement learning.IEEE Access, 8:141007–141024, 2020
2020
-
[10]
Cincio, Y
L. Cincio, Y. Suba¸ sı, A. T. Sornborger, and P. J. Coles. Learning the quantum algorithm for state overlap. New J. Phys., 20(11):113022, 2018
2018
-
[11]
Y. Du, T. Huang, S. You, M.-H. Hsieh, and D. Tao. Quantum circuit architecture search for variational quantum algorithms.npj Quantum Inf., 8:62, 2022
2022
-
[12]
Duong, S
T. Duong, S. Truong, M. Tam, B. Bach, J.-Y. Ryu, and J.-K. K. Rhee. Quantum neural architecture search with quantum circuits metric and Bayesian optimization. InICML 2022 2nd AI4Science Workshop, 2022
2022
-
[13]
A Quantum Approximate Optimization Algorithm
E. Farhi, J. Goldstone, and S. Gutmann. A quantum approximate optimization algorithm.arXiv preprint arXiv:1411.4028, 2014
work page internal anchor Pith review arXiv 2014
-
[14]
H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall. An adaptive variational algorithm for exact molecular simulations on a quantum computer.Nat. Commun., 10:3007, 2019
2019
-
[15]
Havl´ ıˇ cek, A
V. Havl´ ıˇ cek, A. D. C´ orcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta. Supervised learning with quantum-enhanced feature spaces.Nature, 567(7747):209, 2019
2019
-
[16]
Z. He, C. Chen, L. Li, S. Zheng, and H. Situ. Quantum architecture search with meta-learning.Adv. Quantum Technol., 5(8):2100134, 2022
2022
-
[17]
Z. He, H. Chen, Y. Zhou, H. Situ, Y. Li, and L. Li. Self-supervised representation learning for Bayesian quantum architecture search.Phys. Rev. A, 111(3):032403, 2025
2025
-
[18]
Z. He, M. Deng, S. Zheng, L. Li, and H. Situ. GSQAS: Graph self-supervised quantum architecture search. Phys. A, 630:129286, 2023
2023
-
[19]
Z. He, M. Deng, S. Zheng, L. Li, and H. Situ. Training-free quantum architecture search.Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 38(11):12430–12438, 2024
2024
-
[20]
Z. He, L. Li, S. Zheng, Y. Li, and H. Situ. Variational quantum compiling with double Q-learning.New J. Phys., 23(3):033002, 2021. 14
2021
-
[21]
Z. He, Z. Li, H. Situ, Q. Li, J. Shi, and L. Li. Adaptive fusion of training-free proxies for quantum architecture search.Phys. Rev. Applied, 24(2):024074, 2025
2025
-
[22]
Z. He, J. Wei, C. Chen, Z. Huang, H. Situ, and L. Li. Gradient-based optimization for quantum architecture search.Neural Networks, 179:106508, 2024
2024
-
[23]
Holmes, K
Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles. Connecting ansatz expressibility to gradient magnitudes and barren plateaus.PRX Quantum, 3(1):010313, 2022
2022
-
[24]
Huang, S
Y. Huang, S. Jin, B. Zeng, and Q. Shao. Adaptive diversity-based quantum circuit architecture search.Phys. Rev. Res., 6(3):033033, 2024
2024
-
[25]
Huang, Q
Y. Huang, Q. Li, X. Hou, R. Wu, M.-H. Yung, A. Bayat, and X. Wang. Robust resource-efficient quantum variational ansatz through an evolutionary algorithm.Phys. Rev. A, 105(5):052414, 2022
2022
-
[26]
Khatri, R
S. Khatri, R. LaRose, A. Poremba, L. Cincio, A. T. Sornborger, and P. J. Coles. Quantum-assisted quantum compiling.Quantum, 3:140, 2019
2019
-
[27]
Kulshrestha, X
A. Kulshrestha, X. Liu, H. Ushijima-Mwesigwa, and I. Safro. Neural architecture search algorithms for quantum autoencoders.IEEE Trans. Quantum Eng., 6:2101117, 2025
2025
- [28]
-
[29]
C. Lei, Y. Du, P. Mi, J. Yu, and T. Liu. Neural auto-designer for enhanced quantum kernels. InThe Twelfth International Conference on Learning Representations (ICLR), 2024
2024
-
[30]
Lipardi, D
V. Lipardi, D. Dibenedetto, G. Stamoulis, and M. H. M. Winands. Quantum circuit design using a progressive widening enhanced Monte Carlo tree search.Adv. Quantum Technol., 8(10):e2500093, 2025
2025
-
[31]
Y. Liu, F. Meng, L. Wang, Y. Hu, S. Li, X. Yu, and Z. Zhang. HaQGNN: Hardware-aware quantum kernel design based on graph neural networks.arXiv preprint arXiv:2506.21161, 2025
work page internal anchor Pith review Pith/arXiv arXiv 2025
-
[32]
Lu, P.-X
Z. Lu, P.-X. Shen, and D.-L. Deng. Markovian quantum neuroevolution for machine learning.Phys. Rev. Applied, 16(4):044039, 2021
2021
-
[33]
Meng, Z.-T
F.-X. Meng, Z.-T. Li, X.-T. Yu, and Z.-C. Zhang. Quantum circuit architecture optimization for variational quantum eigensolver via Monto Carlo tree search.IEEE Trans. Quantum Eng., 2:3103910, 2021
2021
-
[34]
Mitarai, M
K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. Quantum circuit learning.Phys. Rev. A, 98:032309, 2018
2018
-
[35]
Montanaro
A. Montanaro. Quantum algorithms: an overview.npj Quantum Inf., 2:15023, 2016
2016
-
[36]
Moretti, A
R. Moretti, A. Giachero, V. Radescu, and M. Grossi. Enhanced feature encoding and classification on dis- tributed quantum hardware.Mach. Learn.: Sci. Technol., 6(1):015056, 2025
2025
-
[37]
Ostaszewski, L
M. Ostaszewski, L. M. Trenkwalder, W. Masarczyk, E. Scerri, and V. Dunjko. Reinforcement learning for optimization of variational quantum circuit architectures. InProceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS), NIPS ’21, Red Hook, NY, USA, 2021. Curran Associates Inc
2021
-
[38]
Y. J. Patel, A. Kundu, M. Ostaszewski, X. Bonet-Monroig, V. Dunjko, and O. Danaci. Curriculum reinforce- ment learning for quantum architecture search under hardware errors. InThe Twelfth International Conference on Learning Representations (ICLR), 2024
2024
-
[39]
P´ erez-Salinas, H
A. P´ erez-Salinas, H. Wang, and X. Bonet-Monroig. Analyzing variational quantum landscapes with information content.npj Quantum Inf., 10:27, 2024
2024
-
[40]
Peruzzo, J
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’Brien. A variational eigenvalue solver on a photonic quantum processor.Nat. Commun., 5:4213, 2014
2014
-
[41]
Romero, J
J. Romero, J. P. Olson, and A. Aspuru-Guzik. Quantum autoencoders for efficient compression of quantum data.Quantum Sci. Technol., 2(4):045001, 2017. 15
2017
-
[42]
P. R¨ oseler, D. Willsch, and K. Michielsen. How to find expressible and trainable parameterized quantum circuits?arXiv preprint arXiv:2603.14451, 2026
-
[43]
Rosenhahn and T
B. Rosenhahn and T. J. Osborne. Monte Carlo graph search for quantum circuit optimization.Phys. Rev. A, 108(6):062615, 2023
2023
-
[44]
Schuld and N
M. Schuld and N. Killoran. Quantum machine learning in feature Hilbert spaces.Phys. Rev. Lett., 122(4):040504, 2019
2019
-
[45]
S. Sim, P. D. Johnson, and A. Aspuru-Guzik. Expressibility and entangling capability of parameterized quan- tum circuits for hybrid quantum-classical algorithms.Adv. Quantum Technol., 2(12):1900070, 2019
2019
-
[46]
H. Situ, Z. He, Y. Wang, L. Li, and S. Zheng. Quantum generative adversarial network for generating discrete distribution.Inform. Sci., 538:193, 2020
2020
-
[47]
H. Situ, Z. He, S. Zheng, and L. Li. Distributed quantum architecture search.Phys. Rev. A, 110(2):022403, 2024
2024
-
[48]
H. Situ, G. Li, Z. Li, Z. He, Y. Li, and L. Li. Data-efficient predictor-based quantum architecture search with semi-supervised learning.Phys. Rev. A, 113(1):012402, 2026
2026
-
[49]
H. Situ, Z. Li, Z. He, Q. Li, and J. Shi. Automl-driven optimization of variational quantum circuit.Inform. Sci., 717:122272, 2025
2025
-
[50]
V. P. Soloviev, V. Dunjko, C. Bielza, P. Larra˜ naga, and H. Wang. Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search.EPJ Quantum Technol., 11(1):69, 2024
2024
-
[51]
J. Su, J. Fan, S. Wu, G. Li, S.-J. Qin, and F. Gao. Topology-driven quantum architecture search framework. Sci. China Inf. Sci., 68(8):180507, 2025
2025
-
[52]
Y. Sun, Y. Ma, and V. Tresp. Differentiable quantum architecture search for quantum reinforcement learning. In2023 IEEE International Conference on Quantum Computing and Engineering (QCE), volume 02, pages 15–19, 2023
2023
-
[53]
Y. Sun, Z. Wu, V. Tresp, and Y. Ma. Quantum architecture search with unsupervised representation learning. Quantum, 10:1994, 2026
1994
-
[54]
S. S. Tannu and M. K. Qureshi. Not all qubits are created equal: A case for variability-aware policies for NISQ-era quantum computers. InProceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), ASPLOS ’19, page 987–999, New York, NY, USA, 2019. Association for Computing Machinery
2019
-
[55]
H. Wang, Y. Ding, J. Gu, Y. Lin, D. Z. Pan, F. T. Chong, and S. Han. QuantumNAS: Noise-adaptive search for robust quantum circuits. In2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pages 692–708. IEEE, 2022
2022
-
[56]
P. Wang, M. Usman, U. Parampalli, L. C. L. Hollenberg, and C. R. Myers. Automated quantum circuit design with nested Monte Carlo tree search.IEEE Trans. Quantum Eng., 4:3100620, 2023
2023
-
[57]
Z. Wang, J. Huang, R. Ye, Q. Li, Q.-M. Ding, Y. Huang, T. Zhang, Y. Zeng, J. Gao, X. Yuan, and Y. Yao. A review of variational quantum algorithms: Insights into fault-tolerant quantum computing.arXiv preprint arXiv:2604.07909, 2026
work page internal anchor Pith review Pith/arXiv arXiv 2026
-
[58]
W. Wu, G. Yan, X. Lu, K. Pan, and J. Yan. QuantumDARTS: Differentiable quantum architecture search for variational quantum algorithms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, editors,Proceedings of the 40th International Conference on Machine Learning (ICML), volume 202 ofProceedings of Machine Learning Research, pag...
2023
-
[59]
Zhang and L
S. Zhang and L. Li. A brief introduction to quantum algorithms.CCF Trans. HPC, 4(1):53–62, 2022
2022
-
[60]
Zhang, C.-Y
S.-X. Zhang, C.-Y. Hsieh, S. Zhang, and H. Yao. Neural predictor based quantum architecture search.Mach. Learn.: Sci. Technol., 2(4):045027, 2021. 16
2021
-
[61]
Zhang, C.-Y
S.-X. Zhang, C.-Y. Hsieh, S. Zhang, and H. Yao. Differentiable quantum architecture search.Quantum Sci. Technol., 7(4):045023, 2022
2022
-
[62]
Zhang, P.-L
Y.-H. Zhang, P.-L. Zheng, Y. Zhang, and D.-L. Deng. Topological quantum compiling with reinforcement learning.Phys. Rev. Lett., 125(17):170501, 2020
2020
-
[63]
T. Zhao, B. Chen, G. Wu, and L. Zeng. Hierarchical quantum architecture search for variational quantum algorithms.IEEE Trans. Quantum Eng., 5:3103410, 2024
2024
- [64]
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.