arXiv subjects

Jamal Slim

Publications and source records attributed to Jamal Slim.

6 recordsLinked to original sources

arXiv2026

Interferometric Quantum Polynomial Chaos Expansion as a Generative Model for Calorimeter Shower Simulation

We present the quantum polynomial chaos expansion, a generative algorithm in which a single circuit is the entire model, and we use it to learn calorimeter images. In a classical chaos expansion the randomness is the input and the coefficients are fitted. Here the randomness is still the only input, entering the circuit as rotation angles and re-uploaded at every block, so that each measured observable is a chaos expansion of the latent variables whose order equals the circuit depth, and what is fitted are the gate angles themselves. Expressivity therefore grows with depth rather than with classical coefficients, correlations between outputs arise only from entangling gates, and a single latent wire read by all qubits carries the collective mode of the data. Nothing fitted stands between the circuit and the sample, so switching the entanglers off is a setting of the model itself and provably yields independent outputs, and attribution of the learned correlations to individual gates becomes a measurement. Choosing between two measurement bases shot by shot sharpens attribution into certification, and the trained model violates the Bell bound obeyed by every classical generative model with local response, whatever its size. We train the model on Geant4 shower data, execute the identical circuit on a superconducting processor with its accuracy loss predicted in advance, prove a no-go theorem for the tail dependence of every smooth generator read out through expectation values, and identify the circuit primitive that removes this limit.

quant-ph↗

arXiv2026

An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment

The challenge to scaling quantum generative models on near-term hardware is training. Variational circuit Born machines require repeated quantum sampling and are prone to barren plateaus. Instantaneous Quantum Polynomial-time (IQP) Born machines sidestep both, since their loss is built from low-order Pauli-Z correlators that admit an unbiased classical estimator, while sampling from worst-case circuits in the class is conjectured to be classically hard. We take this train-on-classical, deploy-on-quantum workflow to a real high-energy-physics generative task, learning calorimeter shower profiles at 64 qubits and running the trained model on an IBM Heron r2 superconducting processor at 67 physical qubits. Three ingredients make it work. A uniform mixture of IQP circuits (MoIQP) widens the model class at single-circuit training cost. The Pearson-Stabilized Correlation Kernel (PSCK) biases descent toward the pairwise correlations that carry the shower-development physics, which the standard heat kernel systematically compresses. An exact deferred-measurement compilation collapses the mixture into a single IQP circuit, realized on hardware as a constant-depth dynamic circuit with zero SWAP insertions on the device's native heavy-hex graph. The trained model reconstructs the correlation structure to within 0.016 of the floor imposed by the encoding itself. Raw device samples reproduce the per-cell energy spectra and the full pairwise correlation structure at Pearson r = 0.989, up to a single global amplitude compression of depolarizing origin. A Gaussian copula fitted to the same training split matches the pairwise target more accurately than the quantum model at negligible cost. The contribution is therefore the classical trainability, exact compilation, and hardware deployability of a quantum generative model at this scale, not superiority over classical surrogates.

quant-ph↗

arXiv2026

Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

Simulating calorimeter showers is among the largest computational costs in high-energy physics, and quantum generative models have been proposed as alternatives to classical surrogates. Their progress is hindered by a resource problem. In existing gate-model proposals, the quantum register grows with the image size. Benchmark data sets have thousands of cells and are therefore out of reach. We introduce the Quantum Feature Amplification Network (QFAN), which breaks the link between register size and image size. QFAN splits an image into consecutive blocks of pixels and generates them one block at a time, each produced by the same small circuit conditioned on a fixed-length summary of the pixels already generated. The number of qubits is set by the block size, not by the image dimension. The circuit is used as a sampler. Each block is decoded from a finite set of Born measurement records, so the stochasticity of the generated shower arises from measurement randomness rather than classical noise. A tunable fraction of the records is shared among the pixels within a block to control their correlations. Fast training is performed on a noiseless simulator using analytic gradients, and the resulting model is then deployed on IBM's Heron QPU. Using only three qubits and 12 (18) shared quantum-circuit parameters, QFAN reproduces pixel-intensity spectra, inter-pixel correlations, and total deposited energy for 12- and 25-pixel benchmarks. We quantify the contribution of the quantum component through an ablation study in which individual pipeline elements are removed and the remainder refitted. Replacing the sampled records by their conditional means, which removes only the measurement randomness, collapses the model to a single deterministic image. Leaving the circuit untrained while refitting every classical stage reproduces neither the pixel spectra nor the correlations at either image size.

quant-ph↗

arXiv2025

Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits

Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods. By employing Pauli propagation, it is possible to derive a symbolic representation of observables as analytic functions of a circuit's parameters. Although the number of terms in such functional representations grows rapidly with circuit depth, suitable choices of ansatz and controlled truncations on Pauli weights and frequency components yield accurate yet tractable estimators of the target observables. With the right ansatz design, this approach can be extended to system sizes beyond the reach of classical statevector simulation, enabling scalable training for larger quantum systems. This also enables a form of classical pre-training through gradient-based optimization prior to deployment on quantum hardware. The proposed approach is demonstrated on the Variational Quantum Eigensolver for obtaining the ground state of the ANNNI spin model on 32 qubits, showing that accurate results can be achieved with a scalable and computationally efficient procedure.

quant-ph↗

arXiv2025

Realizing the Scientific Program with Polarized Ion Beams at EIC

Polarized ion beams at the Electron Ion Collider are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, we summarize the science case and identify polarized $^2$H, $^3$He, $^6$Li and $^7$Li ion beams as critical technology that will enable experiments which address the most important science. Further, we discuss the required ion polarimetry and spin manipulation in EIC. The current EIC accelerator design is presented. We identify a significant R\&D effort involving both national laboratories and universities that is required over about a decade to realize the polarized ion beams and estimate (based on previous experience) that it will require about 20 FTE over 10 years (or a total of about 200 FTE-years) of personnel, including graduate students, postdoctoral researchers, technicians and engineers. Attracting, educating and training a new generation of physicists in experimental spin techniques will be essential for successful realization. AI/ML is seen as having significant potential for both acceleration of R\&D and amplification of discovery in optimal realization of this unique quantum technology on a cutting-edge collider. The R\&D effort is synergistic with research in atomic physics and fusion energy science.

nucl-ex↗

arXiv2022

First Search for Axion-Like Particles in a Storage Ring Using a Polarized Deuteron Beam

Based on the notion that the local dark-matter field of axions or axion-like particles (ALPs) in our Galaxy induces oscillating couplings to the spins of nucleons and nuclei (via the electric dipole moment of the latter and/or the paramagnetic axion-wind effect), we establish the feasibility of a new method to search for ALPs in storage rings. Based on previous work that allows us to maintain the in-plane polarization of a stored deuteron beam for a few hundred seconds, we performed a first proof-of-principle experiment at the Cooler Synchrotron COSY to scan momenta near 970 MeV/c. This entailed a scan of the spin precession frequency. At resonance between the spin precession frequency of deuterons and the ALP-induced EDM oscillation frequency there will be an accumulation of the polarization component out of the ring plane. Since the axion frequency is unknown, the momentum of the beam and consequently the spin precession frequency were ramped to search for a vertical polarization change that would occur when the resonance is crossed. At COSY, four beam bunches with different polarization directions were used to make sure that no resonance was missed because of the unknown relative phase between the polarization precession and the axion/ALP field. A frequency window of 1.5-kHz width around the spin precession frequency of 121 kHz was scanned. We describe the experimental procedure and a test of the methodology with the help of a radiofrequency Wien filter located on the COSY ring. No ALP resonance was observed. As a consequence an upper limit of the oscillating EDM component of the deuteron as well as its axion coupling constants are provided.

hep-ex↗