arXiv subjects

L. Mariani

Publications and source records attributed to L. Mariani.

3 recordsLinked to original sources

arXiv2025

Forecasting Low-Dimensional Turbulence via Multi-Dimensional Hybrid Quantum Reservoir Computing

The prediction of complex dynamics remains an open problem across many domains of physics, where nonlinearities and multiscale interactions severely limit the reliability of conventional forecasting methods. Quantum reservoir computing (QRC) has emerged as a promising paradigm for information processing by exploiting the high dimensionality of the Hilbert space, where the dynamics of quantum systems take place. Here, we introduce a hybrid quantum-classical reservoir architecture capable of handling multivariate time series through quantum evolution combined with classical memory enhancement. Our model employs a five-qubit transverse-field Ising Hamiltonian with input-modulated dynamics and temporal multiplexing, enabling the encoding of input signals over multiple timescales. We apply this framework to two paradigmatic models of chaotic behavior in fluid dynamics, where multiscale dynamics and nonlinearities play a dominant role: a low-dimensional truncation of the two-dimensional Navier-Stokes equations and the Lorenz-63 system. By systematically scanning the quantum system's parameter space, we identify regions that maximize forecasting performance, as measured by the Valid Prediction Time. The observed robustness and reliable performances for both dynamical systems suggest that this hybrid quantum approach offers a flexible platform for modelling complex nonlinear time series.

quant-ph↗

arXiv2025

An array of bulk-acoustic-wave sensors as a high-frequency antenna for gravitational waves

In their simplest form, bulk acoustic wave (BAW) devices consist of a piezoelectric crystal between two electrodes that transduce the material's vibrations into electrical signals. They are adopted in frequency control and metrology, with well-established standards at frequencies of 5~MHz and above. Their use as a resonant-mass strain antenna for high-frequency gravitational waves has been recently proposed (Goryachev and Tobar, 2014). The estimated power spectral density sensitivity at the resonant frequencies is of the order of $10^{-21}\, \textrm{strain}/\sqrt{\textrm{Hz}}$. In this paper, after introducing the science opportunity and potential of gravitational wave detection with BAWs, we describe the two-stage BAUSCIA project plan to build a multimode antenna based on commercial BAWs, followed by an optimized array of custom BAWs. We show that commercially available BAWs already provide sensitivity comparable to current experiments around 10~MHz. Finally, we outline options for optimization of custom devices to improve sensitivity in an unexplored region, probe multiple frequencies between 0.1 and 10 MHz, and target specific signals, such as post-merger emission from neutron stars or emission from various dark matter candidates.

physics.ins-det↗

arXiv2024

Memory-Augmented Hybrid Quantum Reservoir Computing

Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning phase linear, which simplifies training and reduces computational complexity compared to fully trained recurrent neural networks (RNNs). Quantum reservoir computing (QRC) uses the exponential growth of Hilbert spaces in quantum systems, allowing for greater information processing, memory capacity, and computational power. However, the original QRC proposal requires coherent injection of inputs multiple times, complicating practical implementation. We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. This approach avoids the need for multiple coherent input injections and is evaluated on benchmark tasks, including the chaotic Mackey-Glass time series prediction. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array. The optimized model demonstrates promising predictive capabilities, achieving a higher number of steps compared to previously reported approaches.

quant-ph↗