arXiv subjects

Saverio Monaco

Publications and source records attributed to Saverio Monaco.

5 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.

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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↗

arXiv2023

Quantum phase detection generalisation from marginal quantum neural network models

Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising (ANNNI) Hamiltonian, which possesses a ferromagnetic, paramagnetic and antiphase, showing that the whole phase diagram can be reproduced.

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