arXiv subjects

Marta Gili

Publications and source records attributed to Marta Gili.

3 recordsLinked to original sources

arXiv2026

Learning functions of quantum states with distributed architectures

Distributed architectures are gaining prominence in quantum machine learning as a means to overcome hardware limitations and enable scalable quantum information processing. In this context, we analyze the design and performance of distributed Quantum Extreme Learning Machine (QELM) architectures for learning functions of quantum states directly from data, restricting measurements to easily implementable projective measurements in the computational basis. The aim is to determine which schemes can effectively recover specific properties of input quantum states, including both linear and nonlinear features, while also quantifying the resource requirements in terms of measurements and reservoir dimensionality. We compare standard three-layer QELM with a spatially multiplexed architecture composed of multiple independent three-layer units for linear (quantum) tasks, showing a linear reduction in resource requirements per unit. For nonlinear properties, the study examines the multiple-injection architecture and introduces a novel distributed design that incorporates entanglement between subsystems within a spatially multiplexed framework, evaluating its performance through the reconstruction of complex nonlinear quantities such as polynomial targets, R\'enyi entropy, and entanglement measures. Our results demonstrate that the distributed design enables the reconstruction of higher-order nonlinearities by increasing the number of interacting subsystems with reduced resources, rather than increasing the size of an individual reservoir, providing a scalable and hardware-efficient route to quantum property learning.

quant-ph↗

arXiv2025

Reconstructing post-common envelope white dwarf+main sequence binary histories through inverse population synthesis techniques

The evolution of binary stellar systems involves a wide range of physical processes, many of which are not yet well understood. We aim to build a general-purpose algorithm based on inverse population synthesis techniques, able to reconstruct the past history of binary systems. This algorithm will be applied to a sample of eclipsing binaries, aiming to ascertain their progenitors and past histories. Once validated, it was applied to a sample 30 white dwarf plus main-sequence eclipsing binaries observed by the Zwicky Transient Facility survey. We determined the input space parameters of the progenitors for the 30 eclipsing binary systems to which the algorithm was applied. These parameters included the initial primary and secondary masses, the orbital separation and eccentricity, the common-envelope efficiency ($\alpha_{\rm CE}$), and the age at which the system was formed. Furthermore, the analysis of the global properties revealed some important features: a mild anticorrelation between the common-envelope efficiency parameter and the secondary mass, the absence of a universal value of $\alpha_{\rm CE}$ along with no need for internal energy, although in the low-mass regime, the high values of $\alpha_{\rm CE}$ suggest a possible contribution, and an initial thermalized eccentricity distribution. Although a strong degeneracy among the input parameters exists in the reconstruction of post-common envelope binary systems, the high accuracy obtained for the eclipsing-binary systems analyzed here has allowed our algorithm to make a reasonable determination of the initial parameters without the need to include external constraints. The global properties found here so far, can be substantially improved when analyzing a future volume-complete sample.

astro-ph.SR↗

arXiv2024

Optimization of Flight Routes: Quantum Approximate Optimization Algorithm for the Tail Assignment Problem

The Tail Assignment Problem (TAP) is a critical optimization challenge in airline operations, requiring the optimal assignment of aircraft to scheduled flights to maximize efficiency and minimize costs. To address the TAP, this work applies the Quantum Approximate Optimization Algorithm (QAOA), a promising quantum computing algorithm developed for tackling complex combinatorial optimization problems. A detailed formulation of the TAP is provided and QAOA's performance is evaluated on realistic problem instances, examining its strengths and weaknesses. Additionally, QAOA is compared with classical methods such as brute force and branch-and-price, as well as Quantum Annealing (QA), another quantum approach. The analysis reveals the current limitations of quantum hardware but suggests potential advantages as technology advances.

quant-ph↗