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Marwan Ait Haddou

Publications and source records attributed to Marwan Ait Haddou.

6 recordsLinked to original sources

Readout Orientation Controls Measurement-Accessible Quantum Tangent Geometry

A fixed quantum measurement can expose substantially more tangent information than a restricted observable readout retains. We study this second restriction. For a normalized covariance $C \succeq 0$, $\mathrm{Tr} C = 1$, of measurement-induced tangent scores in an $N$-dimensional centered score space, and a rank-$r$ readout projector $P$, we quantify retained tangent mass by $R=\mathrm{Tr}(PC)$. The ratio $ρ=R/(r/N)$ separates the actual retained mass from a rank-only random-orientation reference. Standard Grassmann averaging gives $\mathbb{E}ρ=1$ and $\mathrm{Var}(ρ) \le 2/(r d_{\mathrm{eff}})$, where $d_{\mathrm{eff}}=1/\mathrm{Tr}(C^2)$. We use this identity as a null model rather than as a new random-projection theorem. Numerically, family-balanced one- and two-body readouts remain close to the rank reference through $n=16$ even as the tangent covariance becomes strongly anisotropic. The decisive equal-rank comparison holds the circuit, measurement record, readout rank, and evaluation shot budget fixed. For Haar-$U(4)$ at $n=12$, cross-fitted alignment increases the mean directional gradient-energy proxy by a factor 9.584 and the finite-shot signal-to-noise ratio by a factor 3.111 relative to the physical one-body readout, while a random rank-matched subspace remains near the rank baseline. A half-filled $U(1)$-conserving family provides a structured counterexample to generic orientation: physical low-weight $Z$ readouts are already strongly aligned with leading tangent directions over the tested finite-size range. We treat this symmetry result as a case study, not as a claim that $U(1)$ symmetry generically prevents barren plateaus or that hydrodynamics is the established mechanism. The results isolate readout orientation as a degree of freedom invisible to rank alone that directly controls how much measured tangent information remains usable after readout restriction.

quant-ph↗

Readout-Rank Laws for Isotropic Quantum Tangents

Deep parameterized quantum circuits may remain sensitive to a parameter change while the observables retained by a learning model barely respond. We study this separation for a fixed computational-basis measurement. For a pure-state tangent, we compare the quantum Fisher information $F_Q$, the Fisher information $F_{\rm full}$ in the complete bitstring distribution, and the largest variance-normalized response $\mathcal I_{\mathcal A}$ available to a diagonal readout space $\mathcal A$. If the joint state--tangent frame is Haar random, we prove that the two successive information fractions are independent Beta variables whose means are $1/2$ and $r/(2^n-1)$, where $r$ is the centered dimension of the readout. Consequently, even the joint span of all computational-basis Pauli strings through any fixed weight $k$ retain only $O(n^k2^{-n})$ of the full-record information. Exact-statevector experiments across six circuit families show increasing finite-size agreement with this hierarchy in five nonconserving ensembles as the circuit depth grows. A number-conserving family departs strongly from the isotropic prediction even after correcting the support and readout rank, showing that rank alone is insufficient without tangent isotropy.

quant-ph↗

From Qubits to Couplings: A Hybrid Quantum Machine Learning Framework for LHC Physics

In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the $HH \to b\bar{b}γγ$ final state at $\sqrt{s}$ = 13.6 TeV. The proposed model combines parameterized quantum circuits with a classical neural network meta-model, enabling event-level features to be embedded in a quantum feature space while maintaining the optimization stability of classical learning. The hybrid model outperforms both a state-of-the-art XGBoost model and a purely quantum implementation by a factor of two, achieving an expected 95% CL upper limit on the non-resonant double Higgs boson production cross-section of $1.9\timesσ_{\text{SM}}$ and $2.1\timesσ_{\text{SM}}$ under background normalization uncertainties of 10% and 50%, respectively. In addition, expected constraints on the Higgs boson self-coupling $κ_λ$ and quartic vector-boson-Higgs coupling $κ_{2V}$ are found to be improved compared to the classical and purely quantum models.

hep-ex↗

Sculpting Quantum Landscapes: Fubini-Study Metric Conditioning for Geometry Aware Learning in Parameterized Quantum Circuits

We present a novel meta learning framework called Sculpture that explicitly conditions the Fubini Study metric tensor of parameterized quantum circuits to mitigate barren plateaus in variational quantum algorithms. Our theoretical analysis identifies the logarithmic condition number of the Fubini Study metric as a critical geometric quantity governing trainability, optimization dynamics, and generalization. Sculpture uses a classical meta model trained to generate data dependent quantum circuit initializations that minimize the logarithmic condition number, thereby promoting an isotropic and well conditioned parameter space. Empirical results show that meta training reduces the logarithmic condition number from approximately 1.47 to 0.64 by significantly increasing the minimum eigenvalue and slightly decreasing the maximum eigenvalue of the metric, effectively alleviating barren plateaus. This improved conditioning generalizes well to unseen data, consistently producing well conditioned quantum circuit initializations. In a downstream hybrid quantum classical classification task on the Kaggle diabetes dataset, increasing the meta scaling coefficient accelerates convergence, reduces training loss and gradient norms, and crucially improves generalization, with test accuracy increasing from about 0.68 to over 0.78. These findings demonstrate that sculpting the quantum landscape via meta learning serves as a principled geometric regularizer, substantially enhancing trainability, optimization, and generalization of parameterized quantum circuits and enabling more robust and efficient variational quantum algorithms.

cs.LG↗

HQCM-EBTC: A Hybrid Quantum-Classical Model for Explainable Brain Tumor Classification

We propose HQCM-EBTC, a hybrid quantum-classical model for automated brain tumor classification using MRI images. Trained on a dataset of 7,576 scans covering normal, meningioma, glioma, and pituitary classes, HQCM-EBTC integrates a 5-qubit, depth-2 quantum layer with 5 parallel circuits, optimized via AdamW and a composite loss blending cross-entropy and attention consistency. HQCM-EBTC achieves 96.48% accuracy, substantially outperforming the classical baseline (86.72%). It delivers higher precision and F1-scores, especially for glioma detection. t-SNE projections reveal enhanced feature separability in quantum space, and confusion matrices show lower misclassification. Attention map analysis (Jaccard Index) confirms more accurate and focused tumor localization at high-confidence thresholds. These results highlight the promise of quantum-enhanced models in medical imaging, advancing both diagnostic accuracy and interpretability for clinical brain tumor assessment.

cs.LG↗

Quasi-normal modes of near-extremal black holes in dRGT massive gravity using Physics-Informed Neural Networks (PINNs)

In this study, we demonstrate the use of physics-informed neural networks (PINNs) for computing the quasinormal modes (QNMs) of black holes in de Rham-Gabadadze-Tolley (dRGT) massive gravity. These modes describe the oscillation frequencies of perturbed black holes and are important in understanding the behavior of these objects. We show that by carefully selecting the hyperparameters of the PINN, including the network architecture and the training data, it is possible to achieve good agreement between the computed QNMs and the approximate analytical formula in the near-extremal limit for the smallest mode number. Our results demonstrate the effectiveness of PINNs for solving inverse problems in the context of QNMs and highlight the potential of these algorithms for providing valuable insights into the behavior of black holes.

gr-qc↗