arXiv subjects

Federico Dell'Anna

Publications and source records attributed to Federico Dell'Anna.

6 recordsLinked to original sources

arXiv2025

Maximum Independent Set via Probabilistic and Quantum Cellular Automata

We study probabilistic cellular automata (PCA) and quantum cellular automata (QCA) as frameworks for solving the Maximum Independent Set (MIS) problem. We first introduce a synchronous PCA whose dynamics drives the system toward the manifold of maximal independent sets. Numerical evidence shows that the MIS convergence probability increases significantly as the activation probability p tends to 1, and we characterize how the steps required to reach the absorbing state scale with system size and graph connectivity. Motivated by this behavior, we construct a QCA combining a pure dissipative phase with a constraint-preserving unitary evolution that redistributes probability within this manifold. Tensor Network simulations reveal that repeated dissipative--unitary cycles concentrate population on MIS configurations. We also provide an empirical estimate of how the convergence time scales with graph size, suggesting that QCA dynamics can provide an efficient alternative to adiabatic and variational quantum optimization methods based exclusively on local and translationally invariant rules.

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arXiv2025

Quantum Natural Gradient optimizer on noisy platforms: QAOA as a case study

We investigate the performance of the Quantum Natural Gradient (QNG) optimizer in the presence of noise. Specifically, we evaluate the efficacy of QNG within the Quantum Approximate Optimization Algorithm (QAOA) for finding the ground state of the Transverse Field Ising Model (TFIM). Its performance is benchmarked against the Vanilla Gradient Descent optimizer across two prominent quantum computing platforms: Rydberg atoms and superconducting circuits. Our analysis includes simulations under both idealized noise-free conditions and realistic noisy environments based on calibration data from actual devices. Results demonstrate that QNG consistently outperforms Vanilla Gradient Descent, exhibiting faster convergence on average and greater robustness against random initializations of parameters. This robustness is attributed to the distance regularization in parameter space inherent to QNG. Additionally, QNG achieves a higher convergence rate to the solution, effectively avoiding certain local minima. These findings highlight QNG as a promising tool for optimizing variational quantum algorithms in noisy intermediate-scale quantum (NISQ) devices.

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arXiv2025

A digital Rydberg simulation of dynamical quantum phase transitions in the Schwinger model

We present the simulation of the quench dynamics of the Z3 Schwinger model, that describes an approximation of one-dimensional Quantum Electrodynamics, on a digital noisy Rydberg atom platform, aiming at the observation of multiple dynamical quantum phase transitions. In order to reach long-time dynamics, we exploit an enconding dictated by the symmetries, combined with a circuit compression procedure. We focus on a quench that evolves the Dirac vacuum by means of a Hamiltonian depending on a negative mass parameter. This leads to resonant Rabi oscillations between the Dirac vacuum and mesonic states. The population concentration exhibits oscillations with negligible fluctuations of detuned states also with the inclusion of combined noise sources, from which we can clearly detect multiple dynamical phase transitions.

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arXiv2025

Density Classification with Non-Unitary Quantum Cellular Automata

The density classification (DC) task, a computation which maps global density information to local density, is studied using one-dimensional non-unitary quantum cellular automata (QCAs). Two approaches are considered: one that preserves the number density and one that performs majority voting. For number preserving DC, two QCAs are introduced that reach the fixed point solution in a time scaling quadratically with the system size. One of the QCAs is based on a known classical probabilistic cellular automaton which has been studied in the context of DC. The second is a new quantum model that is designed to demonstrate additional quantum features and is restricted to only two-body interactions. Both can be generated by continuous-time Lindblad dynamics. A third QCA is a hybrid rule defined by both discrete-time and continuous-time three-body interactions that is shown to solve the majority voting problem within a time that scales linearly with the system size.

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arXiv2025

Quantum error mitigation in optimized circuits for particle-density correlations in real-time dynamics of the Schwinger model

Quantum computing gives direct access to the study of real-time dynamics of quantum many-body systems. In principle, it is possible to directly calculate non-equal-time correlation functions, from which one can detect interesting phenomena, such as the presence of quantum scars or dynamical quantum phase transitions. In practice, these calculations are strongly affected by noise, due to the complexity of the required quantum circuits. As a testbed for the evaluation of real-time evolution of observables and correlations, the dynamics of the Zn Schwinger model in a one dimensional lattice is considered. To control the computational cost, we adopt a quantum-classical strategy that reduces the dimensionality of the system by restricting the dynamics to the Dirac vacuum sector and optimizes the embedding into a qubit model by minimizing the number of three-qubit gates. We derive a digital circuit implementation of the time-evolution of particle-density correlation operators and their correlation, comparing results from exact evolution, bare noisy simulations and simulations with different error mitigation techniques. For the evolution of the particle-density operators we also perform runs on a physical IBM quantum device.

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arXiv2023

Quantum Fisher Information and multipartite entanglement in spin-1 chains

In this paper, we study the ground state Quantum Fisher Information (QFI) in one-dimensional spin-1 models, as witness to Multipartite Entanglement. The models addressed are the Bilinear-Biquadratic model, the most general isotropic SU(2)-invariant spin-1 chain, and the XXZ spin-1 chain, both with nearest-neighbor interactions and open boundary conditions. We show that the scaling of the QFI of strictly non-local observables can be used for characterizing the phase diagrams and, in particular, for studying topological phases, where it scales maximally. Analysing its behavior at the critical phases we are also able to recover the scaling dimensions of the order parameters both for local and string observables. The numerical results have been obtained by exploiting the Density Matrix Renormalization Group algorithm and Tensor Network techniques.

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