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Michael Spannowsky

Publications and source records attributed to Michael Spannowsky.

At least 19 recordsLinked to original sources
arXiv2026

Higher Order Component Attribution via Learned Surrogates in High Energy Physics

Experimental High Energy Physics (HEP) has adopted the transformer architecture for identification of jets, particularly the state-of-the-art Particle Transformer (ParT) which injects explicit information about particle correlations into the attention mechanism. While able to achieve state-of-the-art results in accuracy, interpretability to its effectiveness has been limited. In this paper we propose component attribution methods using Kolmogorov-Arnold Networks (KANs) that directly tie back to the physical descriptors of jets, to probe whether mechanisms such as Multi-Head Attention (MHA), or the ParT architecture, are able to learn these as discriminating features.

cs.LG↗
arXiv2026

Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach

We investigate whether a multiscale tensor-network architecture provides a useful inductive bias for reconstruction-based anomaly detection in collider jets. Jets are produced by a branching cascade, so their internal structure is naturally organised across angular and momentum scales. This motivates an autoencoder that compresses information hierarchically and can reorganise short-range correlations before coarse-graining. Guided by this picture, we formulate a Multiscale Entanglement Renormalization Ansatz (MERA)-inspired autoencoder acting on ordered jet constituents. To our knowledge, a MERA-inspired autoencoder has not previously been proposed, and this architecture has not been explored in collider anomaly detection. We compare this architecture to a dense autoencoder, the corresponding tree-tensor-network limit, and classical baselines within a background-only reconstruction framework. The paper is organised around two main questions: whether locality-aware hierarchical compression is genuinely supported by the data, and whether the disentangling layers of MERA contribute beyond a simpler tree hierarchy. To address these questions, we combine benchmark comparisons with a training-free local-compressibility diagnostic and a direct identity-disentangler ablation. In the nominal benchmark, the MERA-inspired model attains a higher mean AUC than the dense autoencoder at all three latent dimensions while using roughly one-third of its trainable parameters. The locality-preserving multiscale structure is well matched to jet data, and, at the strongest compression bottleneck, the trained disentanglers move substantially away from the identity and are accompanied by a positive mean performance shift. Overall, the study supports locality-aware hierarchical compression as a useful and parameter-efficient inductive bias for reconstruction-based jet anomaly detection in this benchmark setting.

hep-ph↗
arXiv2026

Fermion Scattering on Quantum Computers

Resonant scattering links particle interactions to the formation and decay of unstable excitations. We develop a hybrid quantum-computing framework to study these dynamics through fermion--antifermion collisions. Our formulation of $(1+1)$-dimensional Yukawa theory encodes fermions in qubits and the scalar field in qumodes. We identify the operations needed for a photonic implementation and benchmark the dynamics with tensor-network simulations. Near resonance, we resolve the build-up of a localised scalar excitation and the subsequent partial return of energy to the fermions. Enhanced entanglement accompanies this exchange. Time-dependent correlator measurements reproduce the analytically predicted decay rate to a few percent precision, with energy scans exhibiting a resonance peak consistent with packet-averaged Breit--Wigner behaviour. These results establish a concrete scattering benchmark for hybrid quantum computers and demonstrate how local real-time observables can probe particle-resonance dynamics.

quant-ph↗
arXiv2026

QGrAT: Quantum Grade-Aware Transformer for Jet Tagging

Lorentz symmetry and the three-prong structure of hadronic top decays provide physical guidance for designing compact quantum jet taggers. We introduce QGrAT (Quantum Grade-Aware Transformer), a quantum transformer that incorporates these structures into its interactions between constituent qubits. Geometric features constructed from the constituent four-momenta are organised by the grades of the spacetime Clifford algebra $C\ell(1,3)$, and their grade-specific inner products determine the pairwise attention couplings. For fixed constituents and co-transforming reference vectors, these couplings are Lorentz invariant, although the component-wise input encoding makes the complete classifier frame dependent. An auxiliary qubit introduces an explicit three-particle interaction through products of pairwise Minkowski inner products, while a trainable in-circuit readout converts the resulting quantum state into a classification score. We evaluate the architecture in state-vector simulation on the Top Tagging benchmarks. With ten constituents per jet, the configuration using grades $0\text{-}3$ and the three-particle ancilla requires twelve qubits and $144$ trainable parameters, reaching AUC $0.944 \pm 0.002$ and background rejection $1/\varepsilon_B = 35.7 \pm 2.4\ (96.8 \pm 7.7)$ at signal efficiency $\varepsilon_S = 0.50\ (0.30)$. This performance is competitive with the L-GATr-Slim baseline at matched constituent and parameter budgets. Adding the three-particle ancilla raises background rejection at $50\%$ signal efficiency from $19.9$ to $35.7$, whereas extending the representation to the tested grade-$4$ feature gives no consistent improvement. The study establishes a framework for incorporating spacetime geometry into quantum attention and identifies explicit three-particle correlations as the principal source of improved background rejection among the architectural variations tested.

hep-ph↗
arXiv2026

Weak boson probes of Higgs unitarity restoration at 10 TeV parton colliders

Higgs coupling deviations, at levels accessible to the high-luminosity LHC, can imply a phenomenological no-lose theorem for the next generation of collider facilities. Correlating Higgs coupling deviations from the SM expectation in the gauge boson sector with high-scale unitarity requirements, we estimate and compare the sensitivity that can be expected at a future hadron collider (operating at 100 TeV centre-of-mass energy) and a 10 TeV muon collider. Both muon and hadron colliders offer discovery potential for mass scales up to ${\cal{O}}(6~\text{TeV})$ where unitarity violation induced by (sub)percent Higgs coupling modifications is mended. We comment on how an intermediate precision FCC-ee programme can corroborate such deviations.

hep-ph↗
arXiv2026

The LHC is not enough: the LZ High-Recoil Event at FCC-hh and a Muon Collider

The high-energy nuclear-recoil candidate reported by LUX-ZEPLIN could point to dark matter beyond the reach of the LHC. We examine how future colliders could test this interpretation, starting with the thermal Higgsino and extending to a broad class of electroweak multiplets containing neutral and charged particles. We assume an approximately pure multiplet protected by an exact stabilizing symmetry, with dominant inelastic Z exchange and the full dark-matter abundance set by gauge-dominated thermal freeze-out, where mixing and additional interactions affecting production or freeze-out are negligible. In this setting, heavy thermal masses, rare production and invisible neutral transitions make the Higgsino's main collider obstacles generic across the class, independently of supersymmetry. Charged lifetimes vary, but the benchmark spectra studied, including radiative doublet splittings, remain beyond the projected HL-LHC reach. We show how FCC-hh and a high-energy muon collider can overcome these obstacles. Our FCC-hh estimates show sensitivity to the doublets and several larger multiplets through disappearing charged tracks and complementary channels that do not depend on the charged lifetime. Muon-collider projections cover the fermionic doublet and triplet at 10 TeV and the full fermionic family considered here at 30 TeV, subject to the stated spectrum and systematic assumptions. This connects a possible underground signal to a concrete program for testing thermal electroweak dark matter at future colliders.

hep-ph↗
arXiv2026

Equivalence of maximal and generic reachability for non-universal Variational Quantum Circuits

Employing problem-specific non-universal Variational Quantum Circuits aligned with a suitable state preparation has become a standard approach to counter the difficulty in training universal ansätze. However, due to their non-universality, diagnosing their reachability and trainability remains reference-state-specific. In this work, we establish the equivalence of maximal reachability and generic reachability over arbitrary reference states as a consequence of the \emph{principal orbit-type theorem}. Thereafter, assuming that the global minimum of the cost function is achieved on a subset that can be described as the image of a smooth function, we derive necessary and sufficient conditions for non-zero probability of reachability under generically sampled reference states. Furthermore, when the solution set is assumed to be realised through a real analytic map that embeds a solution manifold, we show that local surjectivity is generically obtained on the entire solution manifold if it is attained at a single point. As a practical design rule, the need for local surjectivity automatically translates to a necessary dimensional criterion: reachability requires the target set's topological dimension to be at least as large as the co-dimension of the generic orbit. Numerical simulations show that finite-depth optimisation consistently fails when the dimensional obstruction applies, while unobstructed cases show improved convergence.

quant-ph↗
arXiv2026

Looking inside jets: an introduction to jet substructure and boosted-object phenomenology

The study of the internal structure of hadronic jets has become in recent years a very active area of research in particle physics. Jet substructure techniques are increasingly used in experimental analyses by the LHC collaborations, both in the context of searching for new physics and for Standard Model measurements. On the theory side, the quest for a deeper understanding of jet substructure algorithms has contributed to a renewed interest in all-order calculations in QCD. This has resulted in new ideas about how to design better observables and how to provide a solid theoretical description for them. In the last years, jet substructure has seen its scope extended, for example, with an increasing impact in the study of heavy-ion collisions, or with the exploration of deep-learning techniques. Furthermore, jet physics is an area in which experimental and theoretical approaches meet together, where cross-pollination and collaboration between the two communities often bear the fruits of innovative techniques. Despite the wealth of literature on this topic, we feel that a comprehensive and, at the same time, pedagogical introduction to jet substructure is still missing. This makes the endeavour of approaching the field particularly hard, as newcomers have to digest an increasing number of substructure algorithms and techniques, too often characterised by opaque terminology and jargon. Furthermore, while first-principle calculations in QCD have successfully been applied in order to understand and characterise the substructure of jets, they often make use of calculational techniques, such as resummation, which are not the usual textbook material. Thus, the idea of combining our experience in different aspects of jet substructure phenomenology to put together this set of lecture notes, which we hope could help and guide someone who moves their first steps in the physics of jet substructure.

hep-ph↗
arXiv2026

Optimal Symmetries in Binary Classification

We develop a theoretical foundation for designing group-equivariant neural networks that align the choice of symmetries with the underlying probability distributions of the data. Utilising the general structure of fibre decompositions on the domain under group equivariant maps and its relation to that of the likelihood ratio, we present a theoretical framework for identifying group actions that maintain optimal classification performance via the Neyman-Pearson lemma. This provides a unified methodology for improving classification accuracy especially in fundamental applications where one has knowledge of the inherent symmetries of the distributions and how they are broken by measurement. As an application to jet classification at the Large Hadron Collider, we find that there can be performance gains when one utilises smaller permutation symmetries within the constituents. This work offers insights and practical guidelines for constructing more effective group equivariant architectures in diverse machine-learning contexts.

cs.LG↗
arXiv2026

Electroweak and Single Top-Quark Conspiracy in Current LHC Data

Recent LHC measurements of electroweak single top-quark and top-quark associated Higgs boson production show a number of rate shifts, while related multiboson channels remain close to their Standard Model predictions. Gauge symmetry relates these processes such that deviations can be organised as the low-energy imprint of a common ultraviolet origin. A Standard Model Effective Field Theory (SMEFT) analysis of these rates together with $Z$-pole observables identifies the pattern: a weak top dipole and a triple-gauge deformation carry the largest pulls, accompanied by a positive left-handed top-quark current. An acceptable fit is also obtained with the pure triple-gauge coefficient set to zero. We then ask whether a gauge-invariant model can produce this set of operators and still pass the numerous constraints from experimental data. Within weakly coupled renormalisable matching, no single scalar, vector or vector-like-quark multiplet is sufficient to reproduce the full pattern. A minimal possibility is a multi-threshold vector-like-quark sector (a singlet, a doublet and two triplets) whose currents arise at tree level, while the dipole requires additional loop dynamics that the current sector does not fix. A representative realisation, confronted with direct, indirect and flavour constraints, points to correlated measurements of electroweak single top-quark production, improved $tH$ and $t \bar t H$ sensitivity, and updated searches for vector-like quarks.

hep-ph↗
arXiv2026

Robust Quantum Machine Learning for Collider Event Selection under Detector Variability

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.

quant-ph↗
arXiv2026

Qubit-qubit-qutrit quantum correlations in $H \to f \bar f V$

We perform an extensive analysis of the quantum correlations carried by the qubit-qubit-qutrit pure state arising in the decay of a massive scalar into a fermion-antifermion pair and a massive gauge boson, $H \to f \bar f V$, specialising to the Higgs boson decay $h \to τ^- τ^+ Z$. Working with the exact tree-level spin state and its systematic expansion around the massless-fermion limit, we obtain analytic control over the entire phase space: the bipartite entanglement measures, the genuine $2 \otimes 2 \otimes 3$ entanglement structure (the Miyake classification), as well as the Bell-inequality violations and the non-stabiliserness (magic) are all mapped and reproduced by compact formulas. The bipartite measures exhibit a monogamy-like trade-off between the fermion pair and the fermion-boson pairs. The state is genuinely $2 \otimes 2 \otimes 3$ entangled over almost the entire phase space, most strongly in the collinear regions. We derive, for the first time, semi-analytical expressions for the tight $4 \times 4 \times 2$ Bell inequalities of the $2 \otimes 2 \otimes 3$ system, generalising the optimisation previously available only for three qubits, and find that the local-hidden-variable bound is violated over the entire phase space, reaching within a few per cent of the quantum bound at the upper endpoint of the di-tau mass spectrum. We further extend the stabiliser Rényi entropy and the non-local magic to systems with unequal local dimensions, and show that the near-endpoint state carries almost exactly one bit of non-local magic, which peaks at $\log_2 \frac{27}{7} \simeq 1.95$ in the collinear regions. The differential decay rate concentrates precisely in the most nonclassical region of the phase space.

hep-ph↗
arXiv2026

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

We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through quadrature displacements and propagated through a fixed-time Gaussian quantum substrate. The final readout occurs through Gaussian-compatible measurements to produce a high-dimensional random feature map. Only a linear classifier is trained, using a single logistic regression, so retraining is fast, and the optical path and detector response set the analytical and inference latency. We evaluate this architecture on two representative classification tasks, top-jet tagging and Higgs-boson identification, with parameter-matched multi-layer perceptron (MLP) baselines. Using standard public datasets and identical train, validation, and test splits, the photonic Quantum Extreme Learning Machine (QELM) outperforms an MLP with two hidden units for all considered training sizes, and matches or exceeds an MLP with ten hidden units at large sample sizes, while training only the linear readout. These results indicate that Gaussian photonic extreme-learning machines can provide compact and expressive random features at fixed latency. The combination of deterministic timing, rapid retraining, low optical power, and room temperature operation makes photonic QELMs a credible building block for online data selection and even first-stage trigger integration at future collider experiments.

quant-ph↗
arXiv2026

Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches

Machine-learned anomaly detection is reshaping searches for new physics, but it has outrun the statistics used to interpret it. A raw anomaly score has no calibrated meaning, a model that scans many regions inflates the look-elsewhere effect, and the asymptotic significances the field relies on are blind to the background mismodelling that anomaly detectors are especially prone to. We propose a calibration layer, built on conformal prediction, that turns any anomaly score into a defensible significance with distribution-free, finite-sample guarantees. Conformal prediction converts scores into valid local p-values, weighted and Mondrian variants repair the sideband-to-signal-region exchangeability failures that resonant searches suffer, and a Gross-Vitells step carries the result through to a look-elsewhere-aware global significance. The layer does two things at once. It exposes miscalibration that the standard pipeline cannot see, and it corrects it without retraining the detector. On public LHC Olympics data, a classifier develops a substructure-mass correlation that makes sideband-calibrated background p-values anti-conservative. Taken at face value, this manufactures a $\sim 46σ$ excess from background sculpting alone, which the label-free weighted correction removes, restoring an honest null. When run as a blind wide-mass bump hunt, the standard asymptotic and unweighted procedures fabricate $\gtrsim10σ$ excesses and $\approx5σ$ excesses even in signal-free windows, while the conformal layer raises no false alarms and its global false-positive rate is verified on background-only pseudoexperiments. The result is an auditable, detector-agnostic path from an uncalibrated score to a trials-factor-aware significance, ready to be folded into experimental anomaly searches.

hep-ph↗
arXiv2026

QUIVER: Quantum-Informed Views for Enhanced Representations in Large ML Models

Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce QUIVER (QUantum-Informed Views for Enhanced Representations, a paradigm that enriches classical data-driven features with a quantum Fisher view: a geometrically motivated, basis-independent summary of higher-order correlations captured by a variational quantum circuit (VQC) trained to perform the same task. Unlike classical feature augmentation, the quantum Fisher information matrix encodes the intrinsic geometry of the learned quantum state manifold. While this feature map, motivated by quantum information theory, is ordinarily non-trivial to model classically, it can surface statistical structure that additional classical data or model capacity finds difficult to learn. This makes the quantum Fisher view a genuinely complementary modality rather than a redundant one. We demonstrate that QUIVER improves standard performance metrics on two benchmark datasets from very different fields: QM9 for predicting molecule properties, and JetClass for predicting jet flavor at the Large Hadron Collider (LHC). The core contribution, however, is domain-agnostic: the quantum Fisher view can be fused into a broad class of model architectures via targeted modifications to the base architecture, to incorporate information about the quantum geometry of the problem. These results demonstrate that quantum-geometric features, extracted from simulated variational circuits, can deliver measurable value for standard machine learning tasks, well before the advent of fault-tolerant quantum hardware.

cs.LG↗
arXiv2026

From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs

Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise correlations. We show that this missing structure is naturally supplied by higher-order Fisher tensors. In a finite basis of binned EECs, ECFs, or EFPs, and in the natural exponential-family coordinates generated by that basis, the same local tensor has three equivalent interpretations: a coefficient in the local Kullback-Leibler expansion, a connected cumulant of the chosen correlator observables, and a signed weight on a hyperedge linking those observables. This gives an exact Fisher-correlator-hypergraph triality in the local exponential-family embedding. The triality provides a direct construction of physics-informed hypergraphs from correlator data. Extending the quadratic Fisher matrix to the first non-trivial higher tensor identifies genuinely connected multi-observable radiation patterns, supplies hyperedge weights for higher-order Laplacians and message passing, and gives a principled criterion for compressing observable bases beyond pairwise information. We develop these constructions and spell out why the exact cumulant interpretation is special to natural exponential-family coordinates. We illustrate the framework in four applications. In a minimal local-KL study, the cubic Fisher tensor reduces the KL truncation error and isolates the dominant triplet structure. In a two-versus-three prong jet substructure benchmark, the hypergraph selector improves compressed-basis classification. In a 33-observable basis-design problem, the Fisher hypergraph retains more third-order local response at twelve observables. A low-capacity learning benchmark then shows how the same Fisher hyperedges can be used as an interpretable inductive bias for message passing on correlator observables.

hep-ph↗
arXiv2026

Searching for axions with quantum interferometry

Quantum phase measurements offer a complementary route to axion searches. We show that axion-photon interactions can imprint both Aharonov-Bohm (AB) and Berry phases in experimentally motivated quantum setups. For a coherently oscillating axion dark matter background, the induced effective current generates a time dependent magnetic flux in an rf-SQUID, leading to a measurable voltage signal through the Josephson phase. For representative benchmarks, this AB phase search reaches the minimum axion-photon coupling $g_{aγγ}^{\mathrm{min}}\sim 7.8\times10^{-14}~\mathrm{GeV}^{-1}$ at axion mass $m_a\sim 10^{-10}~\mathrm{eV}$, with projected sensitivity that can improve on existing limits in that parameter space by roughly one to two orders of magnitude. We also identify a geometric phase observable in a Mach-Zehnder interferometer with an adiabatically rotating magnetic field, providing a proof-of-principle phase-based probe of meV-scale axions even when they do not constitute the dark matter, although sensitivity on the coupling remains weaker than current bounds with conservative tabletop benchmarks. Extending the analysis to a three level photon-axion quasiparticle (AQP)-axion system, with the AQP realized in a topological magnetic insulator, we find a potentially measurable THz Berry phase dominated by the AQP sector, furnishing a nontrivial validation of the formalism in a richer coupled system. These setups establish quantum phase observables as a useful new framework for axion searches, with immediate phenomenological promise in superconducting circuits and longer term potential in quantum enhanced interferometry.

hep-ph↗
arXiv2026

From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networks

Classical deep networks are effective because depth enables adaptive geometric deformation of data representations. In quantum neural networks (QNNs), however, depth or state reachability alone does not guarantee this feature-learning capability. We study this question in the pure-state setting by viewing encoded data as an embedded manifold in $\mathbb{C}P^{2^n-1}$ and analysing infinitesimal unitary actions through Lie-algebra directions. We introduce Classical-to-Lie-algebra (CLA) maps and the criterion of almost Complete Local Selectivity (aCLS), which combines directional completeness with data-dependent local selectivity. Within this framework, we show that data-independent trainable unitaries are complete but non-selective, i.e. learnable rigid reorientations, whereas pure data encodings are selective but non-tunable, i.e. fixed deformations. Hence, geometric flexibility requires a non-trivial joint dependence on data and trainable weights. We further show that accessing high-dimensional deformations of many-qubit state manifolds requires parametrised entangling directions; fixed entanglers such as CNOT alone do not provide adaptive geometric control. Numerical examples validate that aCLS-satisfying data re-uploading models outperform non-tunable schemes while requiring only a quarter of the gate operations. Thus, the resulting picture reframes QNN design from state reachability to controllable geometry of hidden quantum representations.

quant-ph↗