Pith. sign in

REVIEW 3 major objections 6 minor 54 references

Robust Quantum Machine Learning for Collider Event Selection under Detector Variability

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Parameterised quantum circuits provide a robustness inductive bias for collider-event selection, shifting their scores less than classical neural networks when detector-induced smearing is applied.

desk verdict The empirical robustness result is real for the specific models tested, but the stronger claim of a quantum-specific inductive bias is not yet supported by the unmatched classical baselines. read the letter →

arxiv 2608.11330 v1 pith:R3Z6LBJW submitted 2026-08-11 quant-ph hep-exhep-ph

Christopher Brown , Michael Spannowsky , Simon Williams This is my paper · ORCID
classification quant-phhep-exhep-ph
keywords quantummachinelearningcollidereventselectiondistributionshiftrobustnessautoencoderdatareuploadinganomalydetectiontriggersystemsdetectorsmearing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether the constrained structure of parameterised quantum circuits makes them more stable than classical neural networks when the input distribution shifts after deployment, as happens when collider detectors age or drift in calibration. Using two collider-event benchmarks, it trains quantum autoencoders for unsupervised anomaly detection and data-reuploading quantum classifiers for supervised signal-versus-background selection, then applies controlled feature-level smearing to the inputs with all parameters and preprocessing frozen. Under that shift, the quantum models generally produce smaller changes in their output scores and retain their signal-background discrimination better than the more expressive classical autoencoders and multilayer perceptron, while remaining competitive on clean inputs. The paper concludes that collider-event selection, and trigger systems in particular, is a candidate setting for robust quantum machine learning.

What carries the argument

The objects that carry the argument are two parameterised quantum circuit families. The first is the quantum autoencoder, whose per-event anomaly score is $1 - f_{\mathrm{vac}}(x;\phi)$, the infidelity of the trash register with the vacuum state after a learned unitary compresses background states into a one-qubit latent register; because the score comes from a quantum compression map measured by a swap test, it has a different functional form from classical reconstruction error. The second is the data-reuploading classifier, which alternates trainable angle-encoding layers (affine maps $w x + b$ feeding $R_Y$ and $R_Z$ rotations), a fixed cyclic entangling layer, and trainable single-qubit rotations, then reads out local and nearest-neighbour qubit correlations through a classical sigmoid. Repeated reuploading layers enlarge the accessible frequency spectrum of the decision function without adding qubits, giving a controlled dial for expressivity. The mechanism proposed is that the restricted, structured hypothesis class of these circuits limits how sensitively the learned output can track small input perturbations, so detector drift produces smaller score displacements while discrimination is retained.

What would settle it

Train the classical autoencoder and multilayer perceptron on the same number of events as the quantum models and with matched parameter counts, apply the same smearing with frozen preprocessing, and compare the mean-squared score shifts; if the classical models then shift as little as the quantum models, the claimed quantum-specific robustness inductive bias is refuted.

Watch

Extended reading notes

Core claim

The central claim is that compact parameterised quantum models provide a useful robustness inductive bias for collider-event selection. In the unsupervised study, quantum autoencoders trained on 500 background events with 16 circuit parameters, against a classical autoencoder with 312 parameters trained on $10^6$ events, achieve competitive anomaly detection, especially at the low false-positive rates relevant to triggers, and under smearing their anomaly-score distributions move far less than the classical baselines' while their ROC AUC stays stable. In the supervised study, a four-layer data-reuploading classifier trained on 1000 labelled events reaches a clean-input AUC of 75.5% versus 74.3% for the multilayer perceptron trained on $10^6$ events, yet its output scores shift less under smearing and it retains the largest AUC in the strongly shifted regime, whereas the MLP degrades steadily. The paper reads these results as evidence that the hypothesis class of parameterised quantum circuits, through its encoding, entangling geometry, and fidelity-based compression, resists detector-induced shifts better than highly flexible classical models.

Load-bearing premise

The claim rests on the assumption that the robustness gap comes from the quantum circuit structure and not from the fact that the classical baselines were trained on roughly a thousand times more data and with many more parameters; the paper's appendix checks training-set size only for the unsupervised case, and does not match parameter counts or the supervised training sets.

Editorial extensions

If this is right

  • Compact quantum autoencoders with only local entangling connectivity match the discrimination and robustness of all-to-all connected circuits, pointing toward hardware-efficient trigger implementations.
  • A four-layer data-reuploading classifier reaches clean-input AUC comparable to a multilayer perceptron while degrading less under strong smearing, placing quantum classifiers in a favourable robustness-performance trade-off.
  • Collider trigger models, which cannot be retrained frequently as detector conditions drift, could benefit from models whose event-level scores shift less under calibration changes.
  • Robustness under distribution shift should be reported alongside accuracy when comparing quantum and classical models for high-energy physics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the stability comes from low effective model complexity rather than quantum mechanics itself, then classical models with strong regularisation or early stopping might reproduce the robustness; this is testable and would not diminish the operational relevance.
  • The same smearing protocol should be repeated with a realistic detector-response model, finite measurement shots, and device noise, since the simulations here are noiseless and the robustness ordering could change under those conditions.
  • Because the fidelity-based quantum anomaly score and the reuploading classifier are structurally different, observing robustness in both suggests the effect is tied to quantum hypothesis classes broadly; testing other encodings would sharpen that conclusion.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript reports two empirical studies comparing parameterized quantum models with classical baselines for collider-event selection under detector-induced smearing. In the unsupervised setting, quantum autoencoders (QAEs) trained on 500 embedded background events are compared with classical and variational autoencoders trained on 10^6 events; in the supervised setting, one- to four-layer data-reuploading classifiers trained on 1000 labelled events are compared with linear and multilayer-perceptron classifiers trained on 10^6 events. All models are evaluated on clean test data and on features smeared multiplicatively, with model parameters and preprocessing transformations held fixed. The paper finds competitive clean-data AUC for the quantum models and generally smaller mean-squared score shifts under smearing, and interprets this as evidence for a quantum-specific robustness inductive bias.

Significance. Robustness under distribution shift is practically important for collider triggers, and the paper provides a clearly described benchmark protocol with public datasets, exact circuit simulation, and a useful Appendix A sample-size scan. If the central attribution were supported, the result would be significant for quantum machine learning in high-energy physics. However, the experiments as presented support only the weaker statement that the specific quantum models tested are less sensitive than the specific classical baselines; the causal attribution to the parameterized-quantum-circuit hypothesis class is confounded by unequal training-set sizes, parameter counts, and regularization. The paper is well structured and the measurements are transparently reported, but the central claim needs additional matched classical controls before it can be accepted.

major comments (3)
  1. [Section 2.2, Appendix A] The unsupervised robustness comparison does not control for model capacity or training-data size. Each QAE has 16 trainable circuit parameters and is trained on 500 embedded background events, while the AE has 312 parameters and is trained on 10^6 events (Section 2.2). Appendix A retrains the AE on 500 events, but parameter count and regularization remain unmatched, so the smaller score shifts in Figure 13 remain fully consistent with the explanation that a lower-capacity or smoother model is more stable. A classical AE matched in parameter count and trained on 500 events, or a classical model with equivalent implicit regularization, is needed before the robustness can be attributed to the quantum model family rather than to the training configuration.
  2. [Section 3.2] The statement that the four-layer data-reuploading classifier is 'approximately parameter-matched' to the MLP is inaccurate. The L=4, nq=7 circuit contains 7Lnq = 196 circuit parameters plus 15 readout parameters, for 211 total, while the MLP has 81 parameters, and the quantum model is trained on 1000 labelled events versus 10^6 for the MLP. The linear classifier, with eight parameters, exhibits the smallest score shift but has near-random AUC, demonstrating that low sensitivity can simply reflect weak input dependence. The supervised comparison therefore requires classical baselines matched in parameter count, training-sample size, and regularization before the observed robustness can be attributed to a quantum inductive bias.
  3. [Section 3.3] The smearing protocol in the supervised study applies the multiplicative perturbation to the pT observables but does not recompute the derived kinematic inputs EmissT, MTR, and MTDelta, even though these are kinematically derived from the smeared momenta. The shifted input vectors therefore mix smeared low-level features with reference-derived features, which is not a consistent detector-variability scenario and may artificially reduce the measured score shift. The authors should either recompute the derived variables from the smeared momenta or explicitly characterize the test as a partial-feature perturbation and limit the robustness claim accordingly.
minor comments (6)
  1. [Equation (5)] The product upper limit 'j=1+1' appears to be a typo for 'j=i+1'; please correct this and check the surrounding index notation.
  2. [Throughout] The word 'ansätz' should be 'Ansatz' in the text and figure captions.
  3. [Section 2.3] The text says 'each unmasked input feature' is smeared, but the description immediately before Equation (16) refers specifically to pT features; please clarify precisely which features are perturbed in the unsupervised study.
  4. [Section 3.2] The abbreviations MTR and MTDelta are used without definition; a one-sentence definition of these SUSY kinematic variables would improve accessibility.
  5. [Figures 4, 5, 9, and 11] The shaded bands represent variation across smearing realizations only; the paper does not report variation across independent training seeds or initializations, so it is unclear whether the qualitative ordering of the models is stable. Please state this limitation or add a multiple-seed analysis.
  6. [Data availability] No code or data-availability statement is provided; given the exact-simulation pipeline and public datasets, releasing code would substantially aid reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the robustness result is an empirical measurement on external datasets, not a consequence of the model definitions.

full rationale

No circular step is present. The paper's central claim, that quantum models exhibit smaller score shifts and retain discrimination under smearing, is a direct measurement of quantities defined in Eqs. (17) and (28) on the public ADC2021 and SUSY datasets. The QAE anomaly score is defined through the learned vacuum fidelity (Eq. (12)), and the classifier score is a sigmoid readout of circuit observables (Eq. (26)); neither definition encodes the smeared-input robustness metric. The model parameters and preprocessing constants are held fixed during evaluation, and the smeared samples are never used for training, so the robustness comparison is not forced by construction. Self-citations appear only as architecture or background references (e.g., Refs. [10, 11, 17]) and do not supply the load-bearing evidence for the empirical robustness observation. The asymmetric training-set sizes and parameter counts between quantum and classical models raise a legitimate control concern, but that is a question of experimental fairness and attribution, not of circularity. The claimed result is therefore self-contained with respect to its empirical inputs.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The ledger is dominated by design choices and domain assumptions rather than invented physics entities. The central claim depends on the untested attribution of robustness to quantum structure, on the representativeness of the simple pT smearing model, and on the validity of exact-simulation results.

free parameters (5)
  • Embedding dimension = 8
    The 57-dimensional ADC2021 input is reduced to a fixed 8-dimensional learned embedding (Eq. 13); this choice bounds the quantum circuit size and shapes all downstream comparisons.
  • QAE trash-qubit count nt = 3
    With nq=4, nt=3 leaves a one-qubit latent subsystem; no scan over latent size is reported, so the anomaly score depends on this hand-chosen compression ratio.
  • Quantum training sample size = 500 background events (QAE); 500 signal plus 500 background (classifier)
    Training-set sizes are orders of magnitude smaller than the classical baselines; this asymmetry is a confound for the robustness comparison and is only partially addressed in Appendix A.
  • Classical training sample size = 10^6 events
    The classical baselines are trained on the full available data, while quantum models are not; the robustness comparison therefore mixes model family with data regime.
  • Data-reuploading depth L = 1, 2, 3, 4
    Depth is scanned as the expressivity control in Section 3.1; the main conclusions quote L=1 and L=4.
assumptions (5)
  • standard math The swap-test vacuum fidelity (Eq. 10) is a valid anomaly score for QAE training and inference.
    Sections 2.1 and 2.2 use f_vac as the training loss and 1 minus f_vac as the score; this is a standard construction from the quantum autoencoder literature.
  • domain assumption Multiplicative Gaussian smearing on pT features with clipping and zero-masking (Eq. 16) represents detector-induced distribution shift.
    Section 2.3 introduces the smearing model; the authors explicitly state it does not reproduce a specific detector effect in full detail.
  • domain assumption Exact noiseless circuit simulation captures the relevant behavior of parameterised quantum models for robustness.
    All PennyLane simulations use exact expectation values without finite-shot sampling or device noise (Sections 2.2 and 3.2); the conclusions are explicitly limited to this idealization.
  • ad hoc to paper The robustness difference between quantum and classical models is attributable to the quantum model family rather than to model capacity or training data size.
    The paper does not match parameter counts or training-sample sizes across model families; this attribution is required for the central claim of a quantum robustness inductive bias.
  • domain assumption Holding derived kinematic variables fixed while smearing their inputs still provides a meaningful shift test.
    Section 3.3 states that E_T^miss, M_T^R, and M_T^Delta are not recalculated after pT smearing, so the shifted feature vectors are not kinematically consistent.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Robust Quantum Machine Learning for Collider Event Selection under Detector Variability." pith.science (2026). https://pith.science/paper/R3Z6LBJW

@misc{pith2026260811330,
  author       = {Pith},
  title        = {Pith review of: Robust Quantum Machine Learning for Collider Event Selection under Detector Variability},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R3Z6LBJW}},
  note         = {Machine review of arXiv:2608.11330}
}
read the original abstract

Robust machine-learning methods are becoming increasingly important for high-energy physics data analysis as experiments enter the era of higher luminosity and future higher-energy colliders. Detector degradation, changing running conditions and calibration drift can shift data distributions, causing models trained on clean reference samples to degrade after deployment. We investigate whether parameterised quantum models provide a useful inductive bias for robust collider-event selection in two complementary settings. In the unsupervised study, quantum autoencoders trained on background events are compared with classical and variational autoencoders for anomaly detection. In the supervised study, quantum classifiers with data reuploading are trained to distinguish a supersymmetric signal from background and are compared with linear and multilayer-perceptron classifiers. All models are trained under reference conditions and subsequently evaluated under controlled feature-level smearing while their parameters and preprocessing transformations are held fixed. On clean inputs, the quantum autoencoders achieve competitive anomaly-detection performance, including in the low-false-positive-rate regime relevant for triggering, while the deeper data-reuploading classifier attains discrimination comparable to the non-linear classical baseline. Under smearing, the quantum models generally exhibit smaller shifts in their output scores and retain their discrimination more effectively than the expressive classical baselines. These results suggest that parameterised quantum models can provide a useful robustness inductive bias for collider-event selection and motivate further studies with realistic detector systematics, finite-shot statistics and quantum-device noise.

Figures

Figures reproduced from arXiv: 2608.11330 by the authors.

Figure 1
Figure 1. Quantum autoencoder architecture and entangling ansätze. The circuit (top) consists of angle [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Anomaly-score distributions obtained under reference detector conditions using the [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Anomaly-detection performance in the absence of detector-induced distribution shift [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Mean-squared deviation of the anomaly score from its reference value as a function of [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: ROC AUC as a function of the detector-smearing strength for the [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Supervised quantum classifier architecture and entangling ansätz. The data-reuploading layer [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Classifier-score distributions obtained under reference detector conditions for the [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: ROC curves obtained under reference detector conditions for the linear classifier, [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Mean-squared deviation of the classifier score from its reference value as a function of [PITH_FULL_IMAGE:figures/full_fig_p021_9.png]
Figure 10
Figure 10. Figure 10: Dependence of the quantum-classifier response on the data-reuploading depth. The [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: ROC AUC as a function of the detector-smearing strength for the SUSY signal sample. [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: Dependence of the anomaly-detection performance on the number [PITH_FULL_IMAGE:figures/full_fig_p025_12.png]
Figure 13
Figure 13. Figure 13: Mean-squared deviation of the anomaly score from its reference value as a function of [PITH_FULL_IMAGE:figures/full_fig_p026_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 11 canonical work pages

  1. [1]

    Learning to Pivot with Adversarial Networks,

    G. Louppe, M. Kagan, and K. Cranmer, “Learning to Pivot with Adversarial Networks,” [arXiv:1611.01046 [stat.ML]]

    arXiv

  2. [2]

    Machine Learning Uncertainties with Adversarial Neural Networks,

    C. Englert, P. Galler, P. Harris, and M. Spannowsky, “Machine Learning Uncertainties with Adversarial Neural Networks,” Eur. Phys. J. C79no. 1, (2019) 4, [arXiv:1807.08763 [hep-ph]]

    arXiv 2019

  3. [3]

    Parameterized quantum circuits as machine learning models,

    M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, “Parameterized quantum circuits as machine learning models,” Quantum Science and Technology4no. 4, (Nov., 2019) 043001. http://dx.doi.org/10.1088/2058-9565/ab4eb5

    doi:10.1088/2058-9565/ab4eb5 2019

  4. [4]

    Supervised learning with quantum-enhanced feature spaces,

    V. Havlicek, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature567 (2019) 209–212, [arXiv:1804.11326 [quant-ph]]

    arXiv 2019

  5. [5]

    Supervised quantum machine learning models are kernel methods,

    M. Schuld, “Supervised quantum machine learning models are kernel methods,” 1, 2021

    work page 2021

  6. [6]

    Unsupervised event classification with graphs on classical and photonic quantum computers,

    A. Blance and M. Spannowsky, “Unsupervised event classification with graphs on classical and photonic quantum computers,” JHEP21(2020) 170, [arXiv:2103.03897 [hep-ph]]

    arXiv 2020

  7. [7]

    Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier,

    A. Blance and M. Spannowsky, “Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier,” JHEP02(2021) 212, [arXiv:2010.07335 [hep-ph]]

    arXiv 2021

  8. [8]

    Quantum machine learning in high energy physics,

    W. Guan, G. Perdue, A. Pesah, M. Schuld, K. Terashi, S. Vallecorsa, and J.-R. Vlimant, “Quantum machine learning in high energy physics,” Machine Learning: Science and Technology2no. 1, (Mar, 2021) 011003.https://doi.org/10.1088/2632-2153/abc17d

    doi:10.1088/2632-2153/abc17d 2021

Show all 54 references
  1. [9]

    Quantum computing for data analysis in high energy physics,

    A. Delgado, K. E. Hamilton, P. Date, J.-R. Vlimant, D. Magano, Y. Omar, P. Bargassa, A. Francis, A. Gianelle, L. Sestini,et al., “Quantum computing for data analysis in high energy physics,” 2022.https://arxiv.org/abs/2203.08805

    2022 arXiv

  2. [10]

    Event classification with quantum machine learning in high-energy physics,

    K. Terashi, M. Kaneda, T. Kishimoto, M. Saito, R. Sawada, and J. Tanaka, “Event classification with quantum machine learning in high-energy physics,” Computing and Software for Big Science5no. 1, (2021) 2

    2021

  3. [11]

    Anomaly detection in high-energy physics using a quantum autoencoder,

    V. S. Ngairangbam, M. Spannowsky, and M. Takeuchi, “Anomaly detection in high-energy physics using a quantum autoencoder,” Phys. Rev. D105no. 9, (2022) 095004, [arXiv:2112.04958 [hep-ph]]

    2022 arXiv

  4. [12]

    One particle - one qubit: Particle physics data encoding for quantum machine learning,

    A. Bal, M. Klute, B. Maier, M. Oughton, E. Pezone, and M. Spannowsky, “One particle - one qubit: Particle physics data encoding for quantum machine learning,” Phys. Rev. D 112(Oct, 2025) 076004.https://link.aps.org/doi/10.1103/l8y2-87vq

    2025 doi

  5. [13]

    Continuous-variable photonic quantum extreme learning machines for fast collider-data selection,

    B. Maier, M. Spannowsky, and S. Williams, “Continuous-variable photonic quantum extreme learning machines for fast collider-data selection,” EPJ Quant. Technol.13no. 1, (2026) 70, [arXiv:2510.13994 [quant-ph]]

    2026 arXiv

  6. [14]

    Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP),

    J. Gonskiet al., “Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP),” 2, 2026. 27

    2026

  7. [15]

    Vulnerability of quantum classification to adversarial perturbations,

    N. Liu and P. Wittek, “Vulnerability of quantum classification to adversarial perturbations,” Phys. Rev. A101no. 6, (2020) 062331, [arXiv:1905.04286 [quant-ph]]

    2020 arXiv

  8. [16]

    Adversarial robustness guarantees for quantum classifiers,

    N. Dowling, M. T. West, A. Southwell, A. C. Nakhl, M. Sevior, M. Usman, and K. Modi, “Adversarial robustness guarantees for quantum classifiers,” npj Quantum Inf.12no. 1, (2026) 16, [arXiv:2405.10360 [quant-ph]]

    2026

  9. [17]

    Quantum autoencoders for efficient compression of quantum data,

    J. Romero, J. P. Olson, and A. Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,” Quantum Science and Technology2no. 4, (Aug., 2017) 045001.http://dx.doi.org/10.1088/2058-9565/aa8072

    2017 doi

  10. [18]

    Data re-uploading for a universal quantum classifier,

    A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster, and J. I. Latorre, “Data re-uploading for a universal quantum classifier,” Quantum4(Feb., 2020) 226. https://doi.org/10.22331/q-2020-02-06-226

    2020 doi

  11. [19]

    Effect of data encoding on the expressive power of variational quantum-machine-learning models,

    M. Schuld, R. Sweke, and J. J. Meyer, “Effect of data encoding on the expressive power of variational quantum-machine-learning models,” Phys. Rev. A103no. 3, (2021) 032430, [arXiv:2008.08605 [quant-ph]]

    2021 arXiv

  12. [20]

    Quantum algorithms for quantum field theories,

    S. P. Jordan, K. S. M. Lee, and J. Preskill, “Quantum algorithms for quantum field theories,” Science336no. 6085, (2012) 1130–1133. http://dx.doi.org/10.1126/science.1217069

    2012 doi

  13. [21]

    Digitization of scalar fields for quantum computing,

    N. Klco and M. J. Savage, “Digitization of scalar fields for quantum computing,” Phys. Rev. A99no. 5, (2019) 052335, [arXiv:1808.10378 [quant-ph]]

    2019 arXiv

  14. [22]

    Simulating quantum field theory with a quantum computer,

    J. Preskill, “Simulating quantum field theory with a quantum computer,” PoS LA TTICE2018(2018) 024, [arXiv:1811.10085 [hep-lat]]

    2018 arXiv

  15. [23]

    Simulating Lattice Gauge Theories within Quantum Technologies,

    M. C. Bañulset al., “Simulating Lattice Gauge Theories within Quantum Technologies,” Eur. Phys. J. D74no. 8, (2020) 165, [arXiv:1911.00003 [quant-ph]]

    2020 arXiv

  16. [24]

    Quantum simulation for high-energy physics,

    C. W. Bauer, Z. Davoudi, A. B. Balantekin, T. Bhattacharya, M. Carena, W. A. de Jong, P. Draper, A. El-Khadra, N. Gemelke, M. Hanada,et al., “Quantum simulation for high-energy physics,” PRX Quantum4(May, 2023) 027001. https://link.aps.org/doi/10.1103/PRXQuantum.4.027001

    2023 doi

  17. [25]

    Enhancing quantum field theory simulations on NISQ devices with Hamiltonian truncation,

    J. Ingoldby, M. Spannowsky, T. Sypchenko, and S. Williams, “Enhancing quantum field theory simulations on NISQ devices with Hamiltonian truncation,” Phys. Rev. D110no. 9, (2024) 096016, [arXiv:2407.19022 [quant-ph]]

    2024 arXiv

  18. [26]

    Simulating quantum field theories on continuous-variable quantum computers,

    S. Abel, M. Spannowsky, and S. Williams, “Simulating quantum field theories on continuous-variable quantum computers,” Phys. Rev. A110no. 1, (2024) 012607, [arXiv:2403.10619 [quant-ph]]

    2024 arXiv

  19. [27]

    Quantum computing for high-energy physics: State of the art and challenges,

    A. Di Meglio, K. Jansen, I. Tavernelli, C. Alexandrou, S. Arunachalam, C. W. Bauer, K. Borras, S. Carrazza, A. Crippa, V. Croft,et al., “Quantum computing for high-energy physics: State of the art and challenges,” PRX Quantum5(Aug, 2024) 037001. https://link.aps.org/doi/10.110...

    2024 doi

  20. [28]

    False vacuum decay in a two-dimensional quantum spin system,

    L. Pavešić, I. G. Moss, and S. Montangero, “False vacuum decay in a two-dimensional quantum spin system,” [arXiv:2607.01994 [quant-ph]]. 28

    arXiv

  21. [29]

    Real-time dynamics of lattice gauge theories with a few-qubit quantum computer,

    E. A. Martinezet al., “Real-time dynamics of lattice gauge theories with a few-qubit quantum computer,” Nature534(2016) 516–519, [arXiv:1605.04570 [quant-ph]]

    2016 arXiv

  22. [30]

    Quantum computation of scattering in scalar quantum field theories,

    S. P. Jordan, K. S. M. Lee, and J. Preskill, “Quantum computation of scattering in scalar quantum field theories,” 2019.https://arxiv.org/abs/1112.4833

    2019 arXiv

  23. [31]

    Real-time scattering in Ising field theory using matrix product states,

    R. G. Jha, A. Milsted, D. Neuenfeld, J. Preskill, and P. Vieira, “Real-time scattering in Ising field theory using matrix product states,” Phys. Rev. Res.7no. 2, (2025) 023266, [arXiv:2411.13645 [hep-th]]

    2025 arXiv

  24. [32]

    Real-time scattering processes with continuous-variable quantum computers,

    S. Abel, M. Spannowsky, and S. Williams, “Real-time scattering processes with continuous-variable quantum computers,” Phys. Rev. A112no. 1, (2025) 012614, [arXiv:2502.01767 [quant-ph]]

    2025 arXiv

  25. [33]

    Scalable quantum simulations of scattering in scalar field theory on 120 qubits,

    N. A. Zemlevskiy, “Scalable quantum simulations of scattering in scalar field theory on 120 qubits,” Phys. Rev. D112(Aug, 2025) 034502. https://link.aps.org/doi/10.1103/qr72-51v1

    2025 doi

  26. [34]

    Real-Time Scattering on Quantum Computers via Hamiltonian Truncation,

    J. Ingoldby, M. Spannowsky, T. Sypchenko, S. Williams, and M. Wingate, “Real-Time Scattering on Quantum Computers via Hamiltonian Truncation,” [arXiv:2505.03878 [quant-ph]]

    arXiv

  27. [35]

    Quantum algorithm for Feynman loop integrals,

    S. Ramírez-Uribe, A. E. Rentería-Olivo, G. Rodrigo, G. F. R. Sborlini, and L. Vale Silva, “Quantum algorithm for Feynman loop integrals,” JHEP05(2022) 100, [arXiv:2105.08703 [hep-ph]]

    2022 arXiv

  28. [36]

    Loop Feynman integration on a quantum computer,

    J. J. M. de Lejarza, L. Cieri, M. Grossi, S. Vallecorsa, and G. Rodrigo, “Loop Feynman integration on a quantum computer,” Phys. Rev. D110no. 7, (2024) 074031, [arXiv:2401.03023 [hep-ph]]

    2024 arXiv

  29. [37]

    A general approach to quantum integration of cross sections in high-energy physics,

    I. Williams and M. Pellen, “A general approach to quantum integration of cross sections in high-energy physics,” Quantum Sci. Technol.10no. 4, (2025) 045017, [arXiv:2502.14647 [quant-ph]]

    2025 arXiv

  30. [38]

    Quantum Algorithm for High Energy Physics Simulations,

    C. W. Bauer, W. A. de Jong, B. Nachman, and D. Provasoli, “Quantum Algorithm for High Energy Physics Simulations,” Phys. Rev. Lett.126no. 6, (2021) 062001, [arXiv:1904.03196 [hep-ph]]

    2021 arXiv

  31. [39]

    Towards a quantum computing algorithm for helicity amplitudes and parton showers,

    K. Bepari, S. Malik, M. Spannowsky, and S. Williams, “Towards a quantum computing algorithm for helicity amplitudes and parton showers,” Phys. Rev. D103no. 7, (2021) 076020, [arXiv:2010.00046 [hep-ph]]

    2021 arXiv

  32. [40]

    Quantum walk approach to simulating parton showers,

    K. Bepari, S. Malik, M. Spannowsky, and S. Williams, “Quantum walk approach to simulating parton showers,” Phys. Rev. D106no. 5, (2022) 056002, [arXiv:2109.13975 [hep-ph]]

    2022 arXiv

  33. [41]

    Collider events on a quantum computer,

    G. Gustafson, S. Prestel, M. Spannowsky, and S. Williams, “Collider events on a quantum computer,” JHEP11(2022) 035, [arXiv:2207.10694 [hep-ph]]

    2022 arXiv

  34. [42]

    Quantum simulation of colour in perturbative quantum chromodynamics,

    H. A. Chawdhry and M. Pellen, “Quantum simulation of colour in perturbative quantum chromodynamics,” SciPost Phys.15no. 5, (2023) 205, [arXiv:2303.04818 [hep-ph]]. 29

    2023 arXiv

  35. [43]

    Quantum simulation of scattering amplitudes and interferences in perturbative QCD,

    H. A. Chawdhry, M. Pellen, and S. Williams, “Quantum simulation of scattering amplitudes and interferences in perturbative QCD,” The European Physical Journal C86 no. 8, (Aug., 2026) 961.https://doi.org/10.1140/epjc/s10052-026-16121-0

    2026 doi

  36. [44]

    Solving a higgs optimization problem with quantum annealing for machine learning,

    A. Mott, J. Job, J.-R. Vlimant, D. Lidar, and M. Spiropulu, “Solving a higgs optimization problem with quantum annealing for machine learning,” Nature550no. 7676, (2017) 375–379

    2017

  37. [45]

    Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider,

    E. Govorkovaet al., “Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider,” Nature Mach. Intell.4(2022) 154–161, [arXiv:2108.03986 [physics.ins-det]]

    2022 arXiv

  38. [46]

    Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments,

    I. Ge, S. Addepalli, A. Dave, and J. Gonski, “Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments,” [arXiv:2607.20302 [cs.LG]]

    arXiv

  39. [47]

    Lhc physics dataset for unsupervised new physics detection at 40 mhz,

    E. Govorkova, E. Puljak, T. Aarrestad, M. Pierini, K. A. Woźniak, and J. Ngadiuba, “Lhc physics dataset for unsupervised new physics detection at 40 mhz,” Scientific Data9no. 1, (2022) 118.https://doi.org/10.1038/s41597-022-01187-8

    2022 doi

  40. [48]

    Whiteson, “SUSY.” Uci machine learning repository, 2014

    D. Whiteson, “SUSY.” Uci machine learning repository, 2014. DOI: https://doi.org/10.24432/C54606

    2014 doi

  41. [49]

    Unsupervised beyond-standard-model event discovery at the lhc with a novel quantum autoencoder,

    C. Duffy, M. Hassanshah, M. Jastrzebski, and S. Malik, “Unsupervised beyond-standard-model event discovery at the lhc with a novel quantum autoencoder,” 2024.https://arxiv.org/abs/2407.07961

    2024 arXiv

  42. [50]

    Anomaly-preserving contrastive neural embeddings for end-to-end model-independent searches at the lhc,

    K. Metzger, L. Xu, M. Sodini, T. K. Årrestad, K. Govorkova, G. Grosso, and P. Harris, “Anomaly-preserving contrastive neural embeddings for end-to-end model-independent searches at the lhc,” Phys. Rev. D112(Oct, 2025) 072011. https://link.aps.org/doi/10.1103/5n77-ynsp

    2025 doi

  43. [51]

    Anomaly detection with AXOL1TL at the CMS Level-1 Trigger in 2024 and 2025,

    CMSCollaboration, CMS Collaboration, “Anomaly detection with AXOL1TL at the CMS Level-1 Trigger in 2024 and 2025,”.https://cds.cern.ch/record/2942560

    2024

  44. [52]

    Pennylane: Automatic differentiation of hybrid quantum-classical computations,

    V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, S. Ahmed, V. Ajith, M. S. Alam, G. Alonso-Linaje, B. AkashNarayanan, A. Asadi,et al., “Pennylane: Automatic differentiation of hybrid quantum-classical computations,” 2022. https://arxiv.org/abs/1811.04968

    2022 arXiv

  45. [53]

    Variational Autoencoders for New Physics Mining at the Large Hadron Collider,

    O. Cerri, T. Q. Nguyen, M. Pierini, M. Spiropulu, and J.-R. Vlimant, “Variational Autoencoders for New Physics Mining at the Large Hadron Collider,” JHEP05(2019) 036, [arXiv:1811.10276 [hep-ex]]

    2019 arXiv

  46. [54]

    Scikit-learn: Machine learning in Python,

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg,et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research12(2011) 2825–2830. 30

    2011

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.