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arxiv: 2607.00365 · v1 · pith:JE27EOOYnew · submitted 2026-07-01 · 🪐 quant-ph · cs.AI· cs.LG

When AI meets quantum information: A comprehensive review

Min Chen , Yu Gan , Xin Jin , Yuqing Li , Junqi Wang , Zeguan Wu , Yunfei Wang , Bingzhi Zhang
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Priyam Srivastava Tianlong Chen Ankit Kulshrestha Yuan Liu Juan Jos\'e Mendoza-Arenas Kaushik P. Seshadreesan Sarvagya Upadhyay Xueyue Zhang Quntao Zhuang Junyu Liu
This is my paper

Pith reviewed 2026-07-02 12:39 UTC · model grok-4.3

classification 🪐 quant-ph cs.AIcs.LG
keywords AI for quantum informationquantum for AIquantum machine learningquantum controltensor networkshybrid quantum-classical systemsquantum sensingreproducibility
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The pith

AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems while quantum information supplies new models and questions for AI.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review establishes that artificial intelligence and quantum information are co-evolving, with each field supplying concrete tools and theoretical questions to the other. In one direction, AI methods extract information from limited quantum measurements, discover and train quantum algorithms, stabilize noisy hardware, and automate experimental workflows in sensing and networking. In the reverse direction, quantum computation and quantum-inspired structures affect AI through algorithmic speedups, changes in expressivity and trainability, and tensor-network representations. A sympathetic reader would care because the intersection points to hybrid quantum-classical systems that could make both quantum technologies and machine learning more capable. The paper closes by naming reproducibility, scalability, and hardware realism as cross-cutting challenges that require tighter integration of theory and experiment.

Core claim

The paper claims that AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI, with progress depending on tighter integration of theory, experiment, and hybrid quantum-classical systems.

What carries the argument

The bidirectional interface organized around AI-for-QI tasks (information extraction from measurements, algorithm training and discovery, hardware stabilization, workflow automation, sensing and networking) and QI-for-AI effects (algorithmic speedups, expressivity, trainability, generalization, neural-network design, tensor-network representations).

If this is right

  • AI methods will improve extraction of information from limited quantum measurements and verification of quantum systems.
  • Quantum computation will supply speedups and new representational structures that change how learning algorithms are designed and trained.
  • Hybrid quantum-classical systems will be required to address scalability and hardware realism in both directions.
  • Tensor-network representations will alter neural-network design and generalization properties in AI models.
  • Automated workflows will accelerate experimental control and programming of quantum hardware.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The review implies that co-design of quantum hardware and AI algorithms will become a distinct research area rather than two separate tracks.
  • Similar learning-based approaches may extend to quantum networking tasks that combine sensing with communication.
  • Implementation tests could compare whether quantum-inspired tensor networks deliver measurable gains in trainability on specific classical datasets.
  • Reproducibility standards for hybrid systems will likely need new benchmarks that track both quantum fidelity and learning performance.

Load-bearing premise

The review's organization and selection of literature accurately captures the central tasks and cross-cutting challenges without major omissions or outdated framing of the field.

What would settle it

A later survey or set of papers demonstrating major AI-QI methods, tasks, or challenges that fall outside the review's chosen organization would show the framing is incomplete.

Figures

Figures reproduced from arXiv: 2607.00365 by Ankit Kulshrestha, Bingzhi Zhang, Juan Jos\'e Mendoza-Arenas, Junqi Wang, Junyu Liu, Kaushik P. Seshadreesan, Min Chen, Priyam Srivastava, Quntao Zhuang, Sarvagya Upadhyay, Tianlong Chen, Xin Jin, Xueyue Zhang, Yuan Liu, Yu Gan, Yunfei Wang, Yuqing Li, Zeguan Wu.

Figure 2
Figure 2. Figure 2: FIG. 2. Schematic loss trajectories in two QNTK regimes. [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Schematic overview of tensor notation and basic multilinear operations. Top left: scalars, vectors, matrices, and higher [PITH_FULL_IMAGE:figures/full_fig_p037_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Two core ideas behind tensor-network representations. Left: an order- [PITH_FULL_IMAGE:figures/full_fig_p038_4.png] view at source ↗
read the original abstract

Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. This manuscript is a comprehensive review surveying the bidirectional interface between artificial intelligence (AI) and quantum information (QI). In the AI-for-QI direction it organizes recent work around five central tasks: extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning methods to sensing and networking. In the QI-for-AI direction it examines algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. The review closes by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress requires tighter integration of theory, experiment, and hybrid quantum-classical systems.

Significance. If the literature selection and framing are representative, the review supplies a useful high-level synthesis that maps the main tasks and open problems at the AI-QI boundary. Its organizational structure around concrete tasks and cross-cutting challenges can help newcomers and specialists alike locate relevant work and identify opportunities for hybrid approaches. The absence of new theorems or empirical results is appropriate for a survey; the contribution lies in the synthesis itself.

minor comments (2)
  1. The abstract states that the review examines 'expressivity, trainability, generalization' in the QI-for-AI section; a short table or bullet list in the introduction that maps these topics to the subsections that treat them would improve navigability.
  2. Several sentences in the closing paragraph on cross-cutting challenges refer to 'hardware realism' without citing the specific hardware platforms or noise models discussed earlier; adding one or two forward references would tighten the connection.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and accurate summary of the manuscript, as well as for the recommendation to accept. The report correctly captures the bidirectional organization, the five central tasks in the AI-for-QI direction, the six themes in the QI-for-AI direction, and the cross-cutting challenges we highlight.

Circularity Check

0 steps flagged

No significant circularity: review paper with no internal derivations

full rationale

This is a survey paper whose content consists entirely of summaries and organization of external literature on AI-QI interactions. No original theorems, fitted parameters, predictions, or derivations are advanced within the manuscript itself. All claims reference prior external work, satisfying the condition for a self-contained review with no load-bearing internal steps that could reduce to self-definition or self-citation chains. The selection and framing of topics is definitional to any review and does not constitute circularity under the specified patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review the central claim rests on the authors' selection criteria and interpretation of cited works rather than new axioms or parameters.

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Reference graph

Works this paper leans on

300 extracted references · 76 canonical work pages · 17 internal anchors

  1. [1]

    Vendeiro, J

    Z. Vendeiro, J. Ramette, A. Rudelis, M. Chong, J. Sin- clair, L. Stewart, A. Urvoy, and V. Vuleti´ c, Machine- learning-accelerated bose-einstein condensation, Physi- cal Review Research4, 043216 (2022)

    2022

  2. [2]

    Milson, A

    N. Milson, A. Tashchilina, T. Ooi, A. Czarnecka, Z. F. Ahmad, and L. J. LeBlanc, High-dimensional reinforce- ment learning for optimization and control of ultracold quantum gases, Machine Learning: Science and Tech- nology4, 045057 (2023)

    2023

  3. [3]

    Reinschmidt, J

    M. Reinschmidt, J. Fort´ agh, A. G¨ unther, and V. V. Volchkov, Reinforcement learning in cold atom exper- iments, Nature Communications15, 10.1038/s41467- 024-52775-8 (2024)

    work page doi:10.1038/s41467- 2024

  4. [4]

    Blatz, J

    T. Blatz, J. Kwan, J. L´ eonard, and A. Bohrdt, Bayesian optimization for robust state preparation in quantum many-body systems, Quantum8, 1388 (2024)

    2024

  5. [5]

    J. P. Zwolak and J. M. Taylor, Colloquium: Advances in automation of quantum dot devices control, Reviews of Modern Physics95, 011006 (2023)

    2023

  6. [6]

    S. S. Kalantre, J. P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M. D. Stewart, and J. M. Taylor, Ma- chine learning techniques for state recognition and auto- tuning in quantum dots, npj Quantum Information5, 10.1038/s41534-018-0118-7 (2019)

    work page doi:10.1038/s41534-018-0118-7 2019

  7. [7]

    J. P. Zwolak, T. McJunkin, S. S. Kalantre, J. P. Dod- son, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, M. A. Eriksson, and J. M. Taylor, Autotuning of double-dot devices in situ with machine learning, Physical Review Applied13, 034075 (2020)

    2020

  8. [8]

    H. Moon, D. T. Lennon, J. Kirkpatrick, N. M. van Es- broeck, L. C. Camenzind, L. Yu, F. Vigneau, D. M. Zumb¨ uhl, G. A. D. Briggs, M. A. Osborne, D. Sejdi- novic, E. A. Laird, and N. Ares, Machine learning en- ables completely automatic tuning of a quantum device faster than human experts, Nature Communications11, 10.1038/s41467-020-17835-9 (2020)

    work page doi:10.1038/s41467-020-17835-9 2020

  9. [9]

    Schuff, M

    J. Schuff, M. J. Carballido, M. Kotzagiannidis, J. C. Calvo, M. Caselli, J. Rawling, D. L. Craig, B. van Straaten, B. Severin, F. Fedele, S. Svab, P. Cheva- lier Kwon, R. S. Eggli, T. Patlatiuk, N. Korda, D. M. Zumb¨ uhl, and N. Ares, Fully autonomous tuning of a spin qubit, Nature Electronics9, 304 (2026)

    2026

  10. [10]

    Nguyen, S

    V. Nguyen, S. B. Orbell, D. T. Lennon, H. Moon, F. Vigneau, L. C. Camenzind, L. Yu, D. M. Zumb¨ uhl, G. A. D. Briggs, M. A. Osborne, D. Sejdinovic, and N. Ares, Deep reinforcement learning for efficient mea- surement of quantum devices, npj Quantum Informa- tion7, 10.1038/s41534-021-00434-x (2021)

    work page doi:10.1038/s41534-021-00434-x 2021

  11. [11]

    J. H. B´ ejanin, C. T. Earnest, Y. R. Sanders, and M. Mariantoni, Resonant coupling parameter estima- tion with superconducting qubits, PRX Quantum2, 040343 (2021)

    2021

  12. [12]

    Genois, J

    ´E. Genois, J. A. Gross, A. Di Paolo, N. J. Steven- son, G. Koolstra, A. Hashim, I. Siddiqi, and A. Blais, Quantum-tailored machine-learning characterization of a superconducting qubit, PRX Quantum2, 040355 (2021)

    2021

  13. [13]

    Genois, N

    ´E. Genois, N. J. Stevenson, N. Goss, I. Siddiqi, and A. Blais, Quantum optimal control of superconduct- ing qubits based on machine-learning characterization, Physical Review Applied24, 10.1103/d9yg-d3qr (2025)

    work page doi:10.1103/d9yg-d3qr 2025

  14. [14]

    P. Qian, S. Qamar, X. Xiao, Y. Gu, X. Chai, Z. Zhao, N. Forcellini, and D. E. Liu, Fast quantum calibra- tion using bayesian optimization with state parame- ter estimator for non-markovian environment (2022), arXiv:2205.12929 [quant-ph]

    work page arXiv 2022

  15. [15]

    C. N. Barrett, A. H. Karamlou, S. E. Muschinske, I. T. Rosen, J. Braum¨ uller, R. Das, D. K. Kim, B. M. Niedzielski, M. Schuldt, K. Serniak, M. E. Schwartz, J. L. Yoder, T. P. Orlando, S. Gustavsson, J. A. Grover, and W. D. Oliver, Learning-based calibration of flux crosstalk in transmon qubit arrays, Physical Review Ap- plied20, 024070 (2023)

    2023

  16. [16]

    Berritta, J

    F. Berritta, J. Benestad, L. Pahl, M. Mathews, J. A. Krzywda, R. Assouly, Y. Sung, D. K. Kim, B. M. Niedzielski, K. Serniak, M. E. Schwartz, J. L. Yoder, A. Chatterjee, J. A. Grover, J. Danon, W. D. Oliver, and F. Kuemmeth, Efficient qubit calibration by binary- search hamiltonian tracking, PRX Quantum6, 030335 (2025)

    2025

  17. [17]

    Berritta, J

    F. Berritta, J. Benestad, J. A. Krzywda, O. Krause, M. A. Marciniak, S. Krøjer, C. W. Warren, E. Hogedal, A. Nylander, I. Ahmad, A. Osman, J. Bizn´ arov´ a, M. Rommel, A. F. Roudsari, J. Bylander, G. Tancredi, J. Danon, J. Hastrup, F. Kuemmeth, and M. Kjaer- gaard, Real-time adaptive tracking of fluctuating relax- ation rates in superconducting qubits, Ph...

    2026

  18. [18]

    Kung, C.-Y

    H.-H. Kung, C.-Y. Liu, Q.-R. Lee, C.-Y. Hu, Y.-C. Chang, C.-Y. Chen, D.-W. Wang, and Y.-H. Lin, Au- tomatic characterization of fluxonium superconducting 49 qubits parameters with deep transfer learning (2025), arXiv:2503.12099 [quant-ph]

    work page arXiv 2025

  19. [19]

    A. A. Gentile, B. Flynn, S. Knauer, N. Wiebe, S. Pae- sani, C. E. Granade, J. G. Rarity, R. Santagati, and A. Laing, Learning models of quantum systems from experiments, Nature Physics17, 837 (2021)

    2021

  20. [20]

    Flynn, A

    B. Flynn, A. A. Gentile, N. Wiebe, R. Santagati, and A. Laing, Quantum model learning agent: characteri- sation of quantum systems through machine learning, New Journal of Physics24, 053034 (2022)

    2022

  21. [21]

    Youssry, Y

    A. Youssry, Y. Yang, R. J. Chapman, B. Haylock, F. Lenzini, M. Lobino, and A. Peruzzo, Experimental graybox quantum system identification and control, npj Quantum Information10, 10.1038/s41534-023-00795-5 (2024)

    work page doi:10.1038/s41534-023-00795-5 2024

  22. [22]

    V. V. Sivak, A. Eickbusch, H. Liu, B. Royer, I. Tsiout- sios, and M. H. Devoret, Model-free quantum control with reinforcement learning, Physical Review X12, 011059 (2022)

    2022

  23. [23]

    Porotti, A

    R. Porotti, A. Essig, B. Huard, and F. Marquardt, Deep reinforcement learning for quantum state preparation with weak nonlinear measurements, Quantum6, 747 (2022)

    2022

  24. [24]

    S. Li, Y. Fan, X. Li, X. Ruan, Q. Zhao, Z. Peng, R.-B. Wu, J. Zhang, and P. Song, Robust quantum control using reinforcement learning from demonstration, npj Quantum Information11, 10.1038/s41534-025-01065-2 (2025)

    work page doi:10.1038/s41534-025-01065-2 2025

  25. [25]

    Daraeizadeh, S

    S. Daraeizadeh, S. P. Premaratne, N. Khammassi, X. Song, M. Perkowski, and A. Y. Matsuura, Machine- learning-based three-qubit gate design for the toffoli gate and parity check in transmon systems, Physical Review A102, 012601 (2020)

    2020

  26. [26]

    Y. Baum, M. Amico, S. Howell, M. Hush, M. Liuzzi, P. Mundada, T. Merkh, A. R. R. Carvalho, and M. J. Biercuk, Experimental deep reinforcement learning for error-robust gate-set design on a superconducting quan- tum computer, PRX Quantum2, 040324 (2021)

    2021

  27. [27]

    Wright and R

    E. Wright and R. De Sousa, Fast quantum gate design with deep reinforcement learning using real-time feed- back on readout signals, in2023 IEEE International Conference on Quantum Computing and Engineering (QCE)(IEEE, 2023) pp. 1295–1303

    2023

  28. [28]

    H. N. Nguyen, F. Motzoi, M. Metcalf, K. B. Wha- ley, M. Bukov, and M. Schmitt, Reinforcement learn- ing pulses for transmon qubit entangling gates, Machine Learning: Science and Technology5, 025066 (2024)

    2024

  29. [29]

    Sarma and M

    B. Sarma and M. J. Hartmann, Designing fast quan- tum gates using optimal control with a reinforcement- learning ansatz, Physical Review Applied23, 014015 (2025)

    2025

  30. [30]

    M. N. Bhat, M. Russo, L. P. Carloni, G. Di Guglielmo, F. Fahim, A. C. Y. Li, and G. N. Perdue, Ma- chine learning for arbitrary single-qubit rotations on an embedded device, Quantum Machine Intelligence7, 10.1007/s42484-024-00214-8 (2025)

    work page doi:10.1007/s42484-024-00214-8 2025

  31. [31]

    Liu, Superconducting quantum computing optimiza- tion based on multi-objective deep reinforcement learn- ing, Scientific Reports15, 10.1038/s41598-024-73456-y (2025)

    Y. Liu, Superconducting quantum computing optimiza- tion based on multi-objective deep reinforcement learn- ing, Scientific Reports15, 10.1038/s41598-024-73456-y (2025)

    work page doi:10.1038/s41598-024-73456-y 2025

  32. [32]

    Magesan, J

    E. Magesan, J. M. Gambetta, A. D. C´ orcoles, and J. M. Chow, Machine learning for discriminating quantum measurement trajectories and improving readout, Phys- ical Review Letters114, 200501 (2015)

    2015

  33. [33]

    A. Seif, K. A. Landsman, N. M. Linke, C. Figgatt, C. Monroe, and M. Hafezi, Machine learning assisted readout of trapped-ion qubits, Journal of Physics B: Atomic, Molecular and Optical Physics51, 174006 (2018)

    2018

  34. [34]

    L. A. Martinez, Y. J. Rosen, and J. L. DuBois, Improv- ing qubit readout with hidden markov models, Physical Review A102, 062426 (2020)

    2020

  35. [35]

    Flurin, L

    E. Flurin, L. S. Martin, S. Hacohen-Gourgy, and I. Sid- diqi, Using a recurrent neural network to reconstruct quantum dynamics of a superconducting qubit from physical observations, Physical Review X10, 011006 (2020)

    2020

  36. [36]

    Koolstra, N

    G. Koolstra, N. Stevenson, S. Barzili, L. Burns, K. Siva, S. Greenfield, W. Livingston, A. Hashim, R. K. Naik, J. M. Kreikebaum, K. P. O’Brien, D. I. Santiago, J. Dressel, and I. Siddiqi, Monitoring fast superconduct- ing qubit dynamics using a neural network, Physical Re- view X12, 031017 (2022)

    2022

  37. [37]

    Lienhard, A

    B. Lienhard, A. Veps¨ al¨ ainen, L. C. G. Govia, C. R. Hof- fer, J. Y. Qiu, D. Rist` e, M. Ware, D. Kim, R. Winik, A. Melville, B. Niedzielski, J. Yoder, G. J. Ribeill, T. A. Ohki, H. K. Krovi, T. P. Orlando, S. Gustavsson, and W. D. Oliver, Deep-neural-network discrimination of multiplexed superconducting-qubit states, Physical Re- view Applied17, 014024 (2022)

    2022

  38. [38]

    Azad and H

    U. Azad and H. Zhang, Machine learning based dis- crimination for excited state promoted readout (2022), arXiv:2210.08574 [quant-ph]

    work page arXiv 2022

  39. [39]

    Maurya, C

    S. Maurya, C. N. Mude, W. D. Oliver, B. Lienhard, and S. Tannu, Scaling qubit readout with hardware ef- ficient machine learning architectures, inProceedings of the 50th Annual International Symposium on Computer Architecture (ISCA ’23)(ACM, 2023) pp. 1–13

    2023

  40. [40]

    Luchi, P

    P. Luchi, P. E. Trevisanutto, A. Roggero, J. L. DuBois, Y. J. Rosen, F. Turro, V. Amitrano, and F. Pederiva, Enhancing qubit readout with autoencoders, Physical Review Applied20, 014045 (2023)

    2023

  41. [41]

    Cosco and N

    F. Cosco and N. Lo Gullo, Enhancing qubit read- out with bayesian learning, Physical Review A108, L060402 (2023)

    2023

  42. [42]

    Z. You, J. Duan, W. Huang, L. Zhang, S. Liu, Y. Zhong, and H. Ian, Neural network based time- resolved state tomography of superconducting qubits (2023), arXiv:2312.07958 [quant-ph]

    work page arXiv 2023

  43. [43]

    S. Cao, Z. Shao, J.-Q. Zheng, M. Alghadeer, S. D. Fas- ciati, M. Piscitelli, P. A. Spring, S. Wang, S. Tamate, N. Vora, Y. Xu, G. Huang, K. Nowrouzi, Y. Nakamura, I. Siddiqi, P. Leek, T. Lyons, and M. Bakr, Super- conducting qubit readout enhanced by path signature (2024), arXiv:2402.09532 [quant-ph]

    work page arXiv 2024

  44. [44]

    N. R. Vora, Y. Xu, A. Hashim, N. Fruitwala, H. N. Nguyen, H. Liao, J. Balewski, A. Rajagopala, K. Nowrouzi, Q. Ji, K. B. Whaley, I. Siddiqi, P. Nguyen, and G. Huang, Ml-powered fpga-based real-time quan- tum state discrimination enabling mid-circuit measure- ments (2024), arXiv:2406.18807 [quant-ph]

    work page arXiv 2024

  45. [45]

    P. K. Gautam, S. Kalipatnapu, Shankaranarayanan H, U. Singhal, B. Lienhard, V. Singh, and C. S. Thakur, Low-latency machine learning fpga accel- erator for multi-qubit-state discrimination (2024), arXiv:2407.03852 [quant-ph]

    work page arXiv 2024

  46. [46]

    Chatterjee, J

    A. Chatterjee, J. Schwinger, and Y. Y. Gao, Enhanced qubit readout via reinforcement learning, Physical Re- 50 view Applied23, 054057 (2025)

    2025

  47. [47]

    Di Guglielmo, B

    G. Di Guglielmo, B. Du, J. Campos, A. Boltasseva, A. Dixit, F. Fahim, Z. Kudyshev, S. Lopez, R. Ma, G. N. Perdue, N. Tran, O. Yesilyurt, and D. Bowring, End-to- end workflow for machine-learning-based qubit readout with qick and hls4ml, IEEE Transactions on Quantum Engineering6, 1 (2025)

    2025

  48. [48]

    X. Guo, T. Bunarjyan, D. Liu, B. Lienhard, and M. Schulz, KLiNQ: Knowledge distillation-assisted lightweight neural network for qubit readout on fpga, in2025 62nd ACM/IEEE Design Automation Confer- ence (DAC)(IEEE, 2025) pp. 1–7

    2025

  49. [49]

    C. N. Mude, S. Maurya, B. Lienhard, and S. Tannu, Effi- cient and scalable architectures for multi-level supercon- ducting qubit readout, in2025 62nd ACM/IEEE Design Automation Conference (DAC)(IEEE, 2025) pp. 1–7

    2025

  50. [50]

    Cosco, F

    F. Cosco, F. Plastina, and N. Lo Gullo, Bayesian mitiga- tion of measurement errors in multiqubit experiments, Physical Review A112, 042621 (2025)

    2025

  51. [51]

    R. Kent, B. Lienhard, G. Lafyatis, and D. J. Gauthier, Superconducting-qubit readout using next-generation reservoir computing, Physical Review Applied25, 044009 (2026)

    2026

  52. [52]

    Reuer, J

    K. Reuer, J. Landgraf, T. F¨ osel, J. O’Sullivan, L. Beltr´ an, A. Akin, G. J. Norris, A. Remm, M. Ker- schbaum, J.-C. Besse, F. Marquardt, A. Wallraff, and C. Eichler, Realizing a deep reinforcement learning agent for real-time quantum feedback, Nature Commu- nications14, 10.1038/s41467-023-42901-3 (2023)

    work page doi:10.1038/s41467-023-42901-3 2023

  53. [53]

    S. Cao, Z. Zhang, M. Alghadeer, S. D. Fasciati, M. Piscitelli, M. Bakr, P. Leek, and A. Aspuru-Guzik, Automating quantum computing laboratory experi- ments with an agent-based ai framework, Patterns6, 101372 (2025)

    2025

  54. [54]

    S. Li, J. M. Miller, P. J. Lee, G. Andersson, C. R. Con- ner, Y. J. Joshi, B. Karimi, A. M. King, H. L. Malc, H. Mishra, H. Qiao, M. Ryu, X. Wu, S. Xing, H. Yan, J. Shi, and A. N. Cleland, Large language model- assisted superconducting qubit experiments (2026), arXiv:2603.08801 [quant-ph]

    work page arXiv 2026

  55. [55]

    Sultanow, M

    E. Sultanow, M. Tehrani, S. Dutta, W. J. Buchanan, and M. S. Khan, Quantum agents, arXiv preprint arXiv:2506.01536 (2025)

    work page arXiv 2025

  56. [56]

    Ax-Prover: A Deep Reasoning Agentic Framework for Theorem Proving in Mathematics and Quantum Physics

    B. Breen, M. Del Tredici, J. McCarran, J. A. Mijares, W. W. Yin, K. Sulimany, J. M. Taylor, F. H. Kop- pens, and D. Englund, Ax-prover: A deep reasoning agentic framework for theorem proving in mathematics and quantum physics, arXiv preprint arXiv:2510.12787 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  57. [57]

    Y. Ren, J. Li, and Y. Qi, Merlean: An agentic frame- work for autoformalization in quantum computation, arXiv preprint arXiv:2602.16554 (2026)

    work page arXiv 2026

  58. [58]

    End-to-End Formalization of Quantum Error Correction

    M. Ehatamm, Y. Lee, X. Wu, and R. Tao, End-to- end formalization of quantum error correction, arXiv preprint arXiv:2605.16523 (2026)

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  59. [59]

    Karosas, Agentic ai meets quantum computing: Frontiers of digital futures, Available at SSRN 5386268 (2025)

    K. Karosas, Agentic ai meets quantum computing: Frontiers of digital futures, Available at SSRN 5386268 (2025)

    2025

  60. [60]

    Guevara, A

    A. Guevara, A. Lupsasca, D. Skinner, A. Strominger, and K. Weil, Single-minus gluon tree amplitudes are nonzero, arXiv preprint arXiv:2602.12176 (2026)

    work page arXiv 2026

  61. [61]

    S. Cao, Z. Zhang, M. Alghadeer, S. D. Fasciati, M. Piscitelli, M. Bakr, P. Leek, and A. Aspuru-Guzik, Agents for self-driving laboratories applied to quantum computing, arXiv preprint arXiv:2412.07978 (2024)

    work page arXiv 2024

  62. [62]

    Zhuang and Z

    Q. Zhuang and Z. Zhang, Physical-layer supervised learning assisted by an entangled sensor network, Phys- ical Review X9, 041023 (2019)

    2019

  63. [63]

    Y. Xia, W. Li, Q. Zhuang, and Z. Zhang, Quantum- enhanced data classification with a variational entan- gled sensor network, Physical Review X11, 021047 (2021)

    2021

  64. [64]

    P. Liao, B. Zhang, and Q. Zhuang, Quantum-enhanced learning with a controllable bosonic variational sensor network, Quantum Science and Technology9, 045040 (2024)

    2024

  65. [65]

    S. A. Khan, S. Prabhu, L. G. Wright, and P. L. McMa- hon, Quantum computational-sensing advantage, arXiv preprint arXiv:2507.16918 (2025), arXiv:2507.16918 [quant-ph]

    work page arXiv 2025

  66. [66]

    Quantum computational displacement sensing

    S. Prabhu, S. A. Khan, X. Song, M. Ouellet, R. Yanag- imoto, S. Roy, A. Senanian, L. G. Wright, V. Fatemi, and P. L. McMahon, Quantum computational displace- ment sensing, arXiv preprint arXiv:2604.13177 (2026), arXiv:2604.13177 [quant-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  67. [67]

    I. Cong, S. Choi, and M. D. Lukin, Quantum convolu- tional neural networks, Nature Physics15, 1273 (2019)

    2019

  68. [68]

    H. Chen, L. Wossnig, S. Severini, H. Neven, and M. Mohseni, Universal discriminative quantum neural networks, Quantum Machine Intelligence3, 1 (2021)

    2021

  69. [69]

    Patterson, H

    A. Patterson, H. Chen, L. Wossnig, S. Severini, D. Browne, and I. Rungger, Quantum state discrimi- nation using noisy quantum neural networks, Physical Review Research3, 013063 (2021)

    2021

  70. [70]

    Zhang and Q

    B. Zhang and Q. Zhuang, Fast decay of classification error in variational quantum circuits, Quantum Science and Technology7, 035017 (2022)

    2022

  71. [71]

    R. S. Kennedy,A Near-Optimum Receiver for the Binary Coherent State Quantum Channel, Quarterly Progress Report 108 (Research Laboratory of Electron- ics, Massachusetts Institute of Technology, 1973)

    1973

  72. [72]

    S. J. Dolinar,An Optimum Receiver for the Binary Coherent State Quantum Channel, Quarterly Progress Report 111 (Research Laboratory of Electronics, Mas- sachusetts Institute of Technology, 1973)

    1973

  73. [73]

    R. L. Cook, P. J. Martin, and J. M. Geremia, Optical coherent state discrimination using a closed-loop quan- tum measurement, Nature446, 774 (2007)

    2007

  74. [74]

    J. Chen, J. L. Habif, Z. Dutton, R. Lazarus, and S. Guha, Optical codeword demodulation with error rates below the standard quantum limit using a condi- tional nulling receiver, Nature Photonics6, 374 (2012)

    2012

  75. [75]

    F. E. Becerra, J. Fan, G. Baumgartner, J. Goldhar, J. T. Kosloski, and A. Migdall, Experimental demon- stration of a receiver beating the standard quantum limit for multiple nonorthogonal state discrimination, Nature Photonics7, 147 (2013)

    2013

  76. [76]

    F. E. Becerra, J. Fan, and A. Migdall, Photon num- ber resolution enables quantum receiver for realistic co- herent optical communications, Nature Photonics9, 48 (2015)

    2015

  77. [77]

    A. R. Ferdinand, M. T. DiMario, and F. E. Becerra, Multi-state discrimination below the quantum noise limit at the single-photon level, npj Quantum Informa- tion3, 43 (2017)

    2017

  78. [78]

    M. T. DiMario and F. E. Becerra, Robust measurement for the discrimination of binary coherent states, Physical Review Letters121, 023603 (2018). 51

    2018

  79. [79]

    Hentschel and B

    A. Hentschel and B. C. Sanders, Machine learning for precise quantum measurement, Physical Review Letters 104, 063603 (2010)

    2010

  80. [80]

    L. J. Fiderer, J. Schuff, and D. Braun, Neural-network heuristics for adaptive bayesian quantum estimation, PRX Quantum2, 020303 (2021)

    2021

Showing first 80 references.