arXiv subjects

Giuseppe Falci

Publications and source records attributed to Giuseppe Falci.

At least 19 recordsLinked to original sources

arXiv2026

Machine-Learning-Empowered Quantum Sensing of the Plaquette Phase in a Three-Level Delta System

We propose a machine-learning-empowered approach to the quantum sensing of the plaquette phase, a gauge-invariant quantity arising in three-level $\Delta$ systems. This phase profoundly affects the system dynamics, breaking coherent population trapping and inducing a non-trivial phase dependence of the dynamics. We demonstrate that a multi-layer perceptron (MLP), trained in a supervised-learning framework, can accurately estimate the plaquette phase from STImulated Raman Adiabatic Passage (STIRAP) population transfer efficiencies measured under different driving conditions, which provide experimentally accessible observables. Our results highlight how the combination of coherent control and machine learning (ML) enables effective phase identification in closed-loop quantum systems, opening new perspectives for quantum technologies, specifically quantum sensing applications including synthetic gauge fields.

quant-ph↗

arXiv2026

Quantum decoherence: a study applied to quarkonium-like bound states in strongly interacting matter

We study the quantum decoherence of a bound state interacting with a reservoir of strongly interacting matter within the framework of open quantum systems. The bound state is modeled as a quantum harmonic oscillator whose parameters are tuned to reproduce the root-mean-square radius of $J/\Psi$ particle. The surrounding medium, representing the many degrees of freedom of strongly interacting matter, acts as an environment that induces dissipation and decoherence through system-reservoir coupling. By analyzing the time evolution of the reduced density matrix, we quantify the loss of quantum coherence and its dependence on medium properties. Subsequently, we extend the model by introducing a time dependence in the system-thermal bath coupling, thereby simulating a temperature evolution similar to that occurring during the expansion of a fireball in the central region of heavy-ion collisions. We find that a temperature evolution has a relevant impact on the way the system loses coherence through the coupling with the expanding medium. Finally, we estimate the impact of the time-dependent temperature on the decoherence process, also analyzing a scenario that includes viscous effects without finding a significant change with respect to ideal hydrodynamical evolution.

hep-ph↗

arXiv2026

Jaynes-Cummings dynamics in strong coupling for many-interacting-qubit quantum Rabi models

The present work focuses on the strong/weak interaction of many-body spin-systems with a cavity mode. It introduces the necessity of redefining the physical conditions determining the strong/weak coupling regime in those systems. In more complex systems, the effective coupling emerging from the collective dynamics may differ indeed from the actual coupling of each individual subsystem with the bosonic field. This is shown by highlighting some counter-intuitive dynamical effects properly related to the coupling regime: a Jaynes-Cummings dynamics emerging although a strong interaction is present. The universality of this result is demonstrated through the analysis of three distinct systems: a two-qubit, a two-qutrit, and an $N$-qubit chain quantum Rabi models.

quant-ph↗

arXiv2025

Machine Learning-aided Optimal Control of a noisy qubit

We apply a graybox machine-learning framework to model and control a qubit undergoing Markovian and non-Markovian dynamics from environmental noise. The approach combines physics-informed equations with a lightweight transformer neural network based on the self-attention mechanism. The model is trained on simulated data and learns an effective operator that predicts observables accurately, even in the presence of memory effects. We benchmark both non-Gaussian random-telegraph noise and Gaussian Ornstein-Uhlenbeck noise and achieve low prediction errors even in challenging noise coupling regimes. Using the model as a dynamics emulator, we perform gradient-based optimal control to identify pulse sequences implementing a universal set of single-qubit gates, achieving fidelities above 99% for the lowest considered value of the coupling and remaining above 90% for the highest.

quant-ph↗

arXiv2025

Selective decoupling in multi-level quantum systems by the SU(2) sign anomaly

We investigate dynamical decoupling operated by $2\pi$-pulses in a two-level subspaces of a multilevel system showing that it may leads to selective decoupling. This provides a flexible strategy for decoupling transitions in a quantum network, when control to directly address them is not available which can be use to control internode interaction or actively suppress decoherence

quant-ph↗

arXiv2025

Dissipation and non-thermal states in cryogenic cavities

We study the properties of photons in a cryogenic cavity, made by cryo-cooled mirrors surrounded by a room temperature environment. We model such a system as a multimode cavity coupled to two thermal reservoirs at different temperatures. Using a Lindblad master equation approach, we derive the photon distribution and the statistical properties of the cavity modes, finding an overall non-thermal state described by a mode-dependent effective temperature. We also calculate the dissipation rates arising from the interaction of the cavity field with the external environment and the mirrors, relating such rates to measurable macroscopic quantities. These results provide a simple theory to calculate the dissipative properties and the effective temperature of a cavity coupled to different thermal reservoirs, offering potential pathways for engineering dissipations and photon statistics in cavity settings.

quant-ph↗

arXiv2024

Noise Classification in Three-Level Quantum Networks by Machine Learning

We investigate a machine learning based classification of noise acting on a small quantum network with the aim of detecting spatial or multilevel correlations, and the interplay with Markovianity. We control a three-level system by inducing coherent population transfer exploiting different pulse amplitude combinations as inputs to train a feedforward neural network. We show that supervised learning can classify different types of classical dephasing noise affecting the system. Three non-Markovian (quasi-static correlated, anti-correlated and uncorrelated) and Markovian noises are classified with more than $99\%$ accuracy. On the contrary, correlations of Markovian noise cannot be discriminated with our method. Our approach is robust to statistical measurement errors and retains its effectiveness for physical measurements where only a limited number of samples is available making it very experimental-friendly. Our result paves the way for classifying spatial correlations of noise in quantum architectures.

quant-ph↗

arXiv2024

Detecting virtual photons in ultrastrongly coupled superconducting quantum circuits

Light-matter interaction and understanding the fundamental physics behind is essential for emerging quantum technologies. Solid-state devices may explore new regimes where coupling strengths are "ultrastrong", i.e., comparable to the energies of the subsystems. New exotic phenomena occur the common root of many of them being the fact that the entangled vacuum contains virtual photons. They herald the lack of conservation of the number of excitations which is the witness of ultrastrong coupling breaking the U(1) symmetry. Despite more than a decade of research, the detection of ground-state virtual photons still awaits demonstration. In this work, we recognize the "conspiring" set of experimental challenges and show how to overcome them, thus providing a solution to this long-standing problem. We find that combining a superinductor-based unconventional "light fluxonium" qudit and coherent control yields a highly efficient, faithful, and selective conversion of virtual photons into real ones. This enables their detection with resources available to present-day quantum technologies.

quant-ph↗

arXiv2024

Integrated conversion and photodetection of virtual photons in an ultrastrongly coupled superconducting quantum circuit

The ground-state of an artificial atom ultrastrongly coupled to quantized modes is entangled and contains an arbitrary number of virtual photons. The problem of their detection has been raised since the very birth of the field but despite the theoretical efforts still awaits experimental demonstration. Recently experimental problems have been addressed in detail showing that they can be overcome by combining an unconventional design of the artificial atom with advanced coherent control. In this work we study a simple scheme of control-integrated continuous measurement which makes remarkably favourable the tradeoff between measurement efficiency and backaction showing that the unambiguous detection of virtual photons can be achieved within state-of-the art quantum technologies.

quant-ph↗

arXiv2024

1/f noise in quantum nanoscience

Fundamental issues of 1/f noise in quantum nanoscience are reviewed starting from basic statistical noise processes. Fundamental noise models based on two-level systems (TLS) are described. We emphasize the importance of TLSs in materials parameter fluctuations, such as dielectric constant. The present understanding of 1/f noise in superconducting quantum interferometers and in single electron devices is summarized. For coherent quantum nanoscience, we introduce superconducting qubits and the relation between decoherence and 1/f noise using the filter function formulation. We also clarify the qubit noise spectroscopy and emphasize the importance of materials with reduced 1/f noise for future quantum coherent nanodevices.

cond-mat.mes-hall↗

arXiv2024

Open-loop quantum control of small-size networks for high-order cumulants and cross-correlations sensing

Quantum control techniques represent one of the most efficient tools to attain high-fidelity quantum operations and a convenient approach for quantum sensing and quantum noise spectroscopy. In this work, we investigate dynamical decoupling while processing an entangling two-qubit gate based on an Ising-xx interaction, each qubit being affected by pure dephasing classical correlated 1/ f -noises. To evaluate the gate error, we used the Magnus expansion introducing generalized filter functions that describe decoupling while processing and allow us to derive an approximate analytic expression as a hierarchy of nested integrals of noise cumulants. The error is separated in contributions of Gaussian and non-Gaussian noise, the corresponding generalized filter functions being calculated up to the fourth order. By exploiting the properties of selected pulse sequences, we show that it is possible to extract the second-order statistics (spectrum and cross-spectrum) and to highlight non-Gaussian features contained in the fourth-order cumulant. We discuss the applicability of these results to state-of-the-art small networks based on solid-state platforms.

cond-mat.other↗

arXiv2023

Second spectrum of charge carrier density fluctuations in graphene due to trapping/detrapping processes

We investigate the second spectrum of charge carrier density fluctuations in graphene within the McWorther model, where noise is induced by electron traps in the substrate. Within this simple picture, we obtain a closed-form expression including both Gaussian and non-Gaussian fluctuations. We show that a very extended distribution of switching rates of the electron traps in the substrate leads to a carrier density power spectrum with a non-trivial structure on the scale of the measurement bandwidth. This explains the appearance of a $1/f$ component in the Gaussian part of the second spectrum, which adds up to the expected frequency-independent term. Finally, we find that the non-Gaussian part of the second spectrum can become quantitatively relevant by approaching extremely low temperatures.

cond-mat.mes-hall↗

arXiv2023

Coarse-grained effective Hamiltonian via the Magnus Expansion for a three-level system

Quantum state processing is one of the main tools of quantum technologies. While real systems are complicated and/or may be driven by non-ideal control they may nevertheless exhibit simple dynamics approximately confined to a low-energy Hilbert subspace. Adiabatic elimination is the simplest approximation scheme allowing us to derive in certain cases an effective Hamiltonian operating in a low-dimensional Hilbert subspace. However, these approximations may present ambiguities and difficulties hindering a systematic improvement of their accuracy in larger and larger systems. Here we use the Magnus expansion as a systematic tool to derive ambiguity-free effective Hamiltonians. We show that the validity of the approximations ultimately leverages only on a properly done coarse-graining in time of the exact dynamics. We validate the accuracy of the obtained effective Hamiltonians with suitably tailored fidelities of quantum operations.

quant-ph↗

arXiv2022

Effect of dilute impurities on short graphene Josephson junctions

We study the effect of a dilute homogeneous spatial distribution of non-magnetic impurities on the equilibrium supercurrent sustained by a ballistic graphene Josephson junction in the short junction limit. Within the Dirac-Bogoliubov-de Gennes approach and modeling impurities by the Anderson model we derive the supercurrent and its equilibrium power spectrum. We find a modification of the current-phase relation with a reduction of the skewness induced by disorder, and a nonmonotonic temperature dependence of the critical current. The potentialities of the supercurrent power spectrum for accurate spectroscopy of the hybridized Andreev bound states-impurities spectrum are highlighted. In the low temperature limit, the supercurrent zero frequency thermal noise directly probes the spectral function at the Fermi energy.

cond-mat.mes-hall↗

arXiv2022

A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies

Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved usefull to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github.

quant-ph↗

arXiv2022

Optimized state transfer in systems of ultrastrongly coupled matter and radiation

Ultrastrong coupling may allow faster operations for the development of quantum technologies at the expenses of increased sensitivity to new kind of intrinsic errors. We study state transfer in superconducting circuit QED architectures in the ultrastrong coupling regime. Using optimal control methods we find a protocol resilient to the main source of errors, coming from the interplay of the dynamical Casimir effect with cavity losses.

quant-ph↗

arXiv2022

Coarse-grained dynamics of ac-driven two-state systems

Magnus expansion is used to identify effective Hamiltonians describing the coarse-grained dynamics of more complex problems. Here, we apply this method to a two-level system driven by an and AC field. We derive Stark and Bloch-Siegert shifs of both diagonal and off-diagonal entries of the Hamiltonian as a result of coarse-graining only.

quant-ph↗

arXiv2021

Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems

We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer in a multi-level system. We constraint our strategy to the case of fixed coupling rates but time-varying detunings, a situation that would simplify considerably the implementation of population transfer in relevant experimental platforms, such as semiconducting and superconducting ones. Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols that, remarkably, differ from (and can be superior to) standard Raman, STIRAP or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.

quant-ph↗