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Irene Graziotti

Publications and source records attributed to Irene Graziotti.

4 recordsLinked to original sources

Current and future constraints on the expansion history of the GREA model

In this work, we investigate the General Relativistic Entropic Acceleration (GREA) framework, in which late-time acceleration emerges from entropy production associated with the cosmological horizon, and compare its performance with the standard $Λ$CDM description of the Universe. We first confront GREA with current background observations, including baryon acoustic oscillations, type Ia supernovae, compressed CMB information, and cosmic chronometers, with particular emphasis on the geometric horizon parameter $\sqrt{-k}η_0$. We then introduce a phenomenological extension of the theory by allowing for an additional dark energy component, $Ω_{de}$, enabling the recovery of a $Λ$CDM-like expansion history as a limiting case. We perform a Bayesian parameter inference and model comparison analysis using both current data and mock datasets representative of future surveys, including SKAO, LSST, and ET. While current data statistically prefer $Λ$CDM when compressed CMB information is included, GREA remains competitive for low-redshift combinations. Forecasts indicate that gravitational wave standard sirens are expected to enhance the ability to discriminate between entropic-driven and dark-energy-driven expansion scenarios, and to identify the underlying cosmological model favored by the data.

astro-ph.CO

A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize $χ^2$ functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.

astro-ph.CO

Quantum Markov Chain Monte Carlo for Cosmological Functions

We present an implementation of Quantum Computing for a Markov Chain Monte Carlo method with an application to cosmological functions, to derive posterior distributions from cosmological probes. The algorithm proposes new steps in the parameter space via a quantum circuit whose resulting statevector provides the components of the shift vector. The proposed point is accepted or rejected via the classical Metropolis-Hastings acceptance method. The advantage of this hybrid quantum approach is that the step size and direction change in a way independent of the evolution of the chain, thus ideally avoiding the presence of local minima. The results are consistent with analyses performed with classical methods, both for a test function and real cosmological data. The final goal is to generalize this algorithm to test its application to complex cosmological computations.

astro-ph.CO

The Application of Quantum Fourier Transform in Cosmic Microwave Background Data Analysis

The Cosmic Microwave Background (CMB) data analysis and the map-making process rely heavily on the use of spherical harmonics. For suitable pixelizations of the sphere, the (forward and inverse) Fourier transform plays a crucial role in computing all-sky map from spherical harmonic expansion coefficients -- or from angular power spectrum -- and vice versa. While the Fast Fourier Transform (FFT) is traditionally employed in these computations, the Quantum Fourier Transform (QFT) offers a theoretical advantage in terms of computational efficiency and potential speedup. In this work, we study the potential advantage of using the QFT in this context by exploring the substitution of the FFT with the QFT within the \textit{healpy} package. Performance evaluations are conducted using the Aer simulator. Our results indicate that QFT exhibits potential advantages over FFT that are particularly relevant at high-resolution. However, classical-to-quantum data encoding overhead represents a limitation to current efficiency. In this work, we adopted amplitude encoding, due to its efficiency on encoding maximum data to minimum number of qubits. We identify data encoding as a potential significant bottleneck and discuss its impact on quantum speedup. Future improvements in quantum encoding strategies and algorithmic optimizations could further enhance the feasibility of QFT in CMB data analysis.

astro-ph.IM