arXiv subjects

Alessandro Santini

Publications and source records attributed to Alessandro Santini.

At least 19 recordsLinked to original sources

arXiv2026

Quantifying the Scientific Potential of Intermediate and Extreme Mass Ratio Inspirals with the Laser Interferometer Space Antenna

The Laser Interferometer Space Antenna (LISA) will enable precision studies of Extreme and Intermediate Mass Ratio Inspirals (EMRIs/IMRIs), providing unique probes of astrophysical environments of galactic nuclei and strong-field gravity. Using a fully relativistic pipeline across primary masses $m_1 \in [5\times10^4, 10^7]\,M_\odot$ and secondary masses $m_2 \in [1, 10^4]\,M_\odot$, we map instrumental performance directly to detection horizons and parameter measurement precision. EMRIs with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ are the most sensitive to instrument degradation, with redshift horizons at $z \sim 0.01$, while IMRIs are the least sensitive to degradation and reach redshifts $z \sim 1-3$. All prograde systems considered achieve sub-percent spin precision within three months of observation. The full 4.5-year mission increases the horizon of systems with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ by a factor of $\sim 4$ and improves sky localization by one to two orders of magnitude reaching $ < 10\,\mathrm{deg}^2$. IMRI detection is robust against degradation, but their parameter estimation is more vulnerable due to fewer cycles in band. With the full baseline, EMRI observations constrain scalar dipole emission and Kerr quadrupole deviations below ground-based bounds by one to two orders of magnitude. We release the accompanying software and an interactive website to enable the community to rapidly quantify the scientific potential of EMRIs and IMRIs.

astro-ph.IM↗

arXiv2026

Inferring the population properties of galactic binaries from LISA's stochastic foreground

Galactic binaries are expected to be the most numerous LISA sources and to produce a stochastic gravitational-wave foreground whose spectral shape encodes information about the underlying population. Extracting this information with standard hierarchical methods is challenging due to the high dimensionality of the problem and the computational cost of global-fit analyses. We present a simulation-based inference framework to measure the population properties of galactic binaries directly from the reconstructed foreground. Adopting an astrophysically agnostic parametrization in the observable space -- defined by signal amplitude, frequency, and frequency derivative -- we generate synthetic catalogs and foreground spectra using a global-fit-inspired subtraction algorithm. We then train a neural posterior estimator to map spectra to population parameters. We validate our method on simulated data and recover population parameters with good accuracy, including the total number of binaries. As a by-product, we present a GPU-accelerated version of the subtraction algorithm, which delivers a ~100X speed-up compared to previous implementations in the literature. Our results demonstrate that LISA's stochastic foreground alone carries significant information about the Galactic binary population and provide a practical step toward joint inference from resolved and unresolved sources.

astro-ph.HE↗

arXiv2025

When Less is More: Approximating the Quantum Geometric Tensor with Block Structures

The natural gradient is central in neural quantum states optimizations but it is limited by the cost of computing and inverting the quantum geometric tensor, the quantum analogue of the Fisher information matrix. We introduce a block-diagonal quantum geometric tensor that partitions the metric by network layers, analogous to block-structured Fisher methods such as K-FAC. This layer-wise approximation preserves essential curvature while removing noisy cross-layer correlations, improving conditioning and scalability. Experiments on Heisenberg and frustrated $J_1$-$J_2$ models show faster convergence, lower energy, and improved stability.

quant-ph↗

arXiv2025

Classically Prepared, Quantumly Evolved: Hybrid Algorithm for Molecular Spectra

We introduce a hybrid classical-quantum algorithm to compute dynamical correlation functions and excitation spectra in many-body quantum systems, with a focus on molecular systems. The method combines classical preparation of a perturbed ground state with short-time quantum evolution of product states sampled from it. The resulting quantum samples define an effective subspace of the Hilbert space, onto which the Hamiltonian is projected to enable efficient classical simulation of long-time dynamics. This subspace-based approach achieves high-resolution spectral reconstruction using shallow circuits and few samples. Benchmarks on molecular systems show excellent agreement with exact diagonalization and demonstrate access to dynamical timescales beyond the reach of purely classical methods, highlighting its suitability for near-term and early fault-tolerant quantum hardware.

quant-ph↗

arXiv2025

A flexible, GPU-accelerated approach for the joint characterization of LISA instrumental noise and Stochastic Gravitational Wave Backgrounds

LISA data analysis represents one of the most challenging tasks ahead for the future of gravitational-wave (GW) astronomy. Characterizing the instrument's noise properties while fitting for all the other detectable sources is a key requirement of any robust inference pipeline. Noise estimation will also play a crucial role in searches and parameter estimation of cosmological and astrophysical stochastic signals. Previous studies have tackled this topic by assuming perfect knowledge of the spectral shape of the instrumental noise and of different possible types of GW Stochastic Backgrounds (SGWBs), usually resorting to parametrized templates. Recently, various works that employ template-agnostic methods have been presented. In this work, we take an additional step further, introducing flexible spectral shapes in both the instrumental noise and the stochastic signals. We account for the lack of knowledge of the exact shape of the individual contributions to the overall power spectral density by using splines to represent arbitrary perturbations of the noise and signal spectral densities. We implement a data-driven Reversible Jump MCMC algorithm to fit different components simultaneously and to infer the level of flexibility required under different scenarios. We test this approach on simulated LISA data produced under different assumptions. We investigate the impact of this increased flexibility on the reconstruction of both the injected signal and the noise level, and we discuss the prospects for claiming a successful SGWB detection.

gr-qc↗

arXiv2025

Looking elsewhere: improving variational Monte Carlo gradients by importance sampling

Neural-network quantum states (NQS) offer a powerful and expressive ansatz for representing quantum many-body wave functions. However, their training via Variational Monte Carlo (VMC) methods remains challenging. It is well known that some scenarios - such as sharply peaked wave functions emerging in quantum chemistry - lead to high-variance gradient estimators hindering the effectiveness of variational optimizations. In this work we investigate a systematic strategy to tackle those sampling issues by means of adaptively tuned importance sampling. Our approach is explicitly designed to (i) target the gradient estimator instead of the loss function, (ii) not introduce additional hyperparameters and (iii) be computationally inexpensive. We benchmarked our approach across the ground-state search of a wide variety of hamiltonians, including frustrated spin systems and ab-initio quantum chemistry. We also show systematic improvements on the infidelity minimization in the context of neural projected quantum dynamics. Overall, our approach can reduce the computational cost of vanilla VMC considerably, up to a factor of 100x when targeting highly peaked quantum chemistry wavefunctions.

quant-ph↗

arXiv2025

The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes

Observations of gravitational-wave signals emitted by compact binary inspirals provide unique insights into their properties, but their analysis requires accurate and efficient waveform models. Intermediate- and extreme-mass-ratio inspirals (I/EMRIs), with mass ratios $q \gtrsim 10^2$, are promising sources for future detectors such as the Laser Interferometer Space Antenna (LISA). Modelling waveforms for these asymmetric-mass binaries is challenging, entailing the tracking of many harmonic modes over thousands to millions of cycles. The FastEMRIWaveforms (FEW) modelling framework addresses this need, leveraging precomputation of mode data and interpolation to rapidly compute adiabatic waveforms for eccentric inspirals into zero-spin black holes. In this work, we extend FEW to model eccentric equatorial inspirals into black holes with spin magnitudes $|a| \leq 0.999$. Our model supports eccentricities $e < 0.9$ and semi-latus recta $p < 200$, enabling the generation of long-duration IMRI waveforms, and produces waveforms in $\sim 100$ ms with hardware acceleration. Characterising systematic errors, we estimate that our model attains mismatches of $\sim 10^{-5}$ (for LISA sensitivity) with respect to error-free adiabatic waveforms over most of parameter space. We find that kludge models introduce errors in signal-to-noise ratios (SNRs) as great as $^{+60\%}_{-40\%}$ and induce marginal biases of up to $\sim 1\sigma$ in parameter estimation. We show LISA's horizon redshift for I/EMRI signals varies significantly with $a$, reaching a redshift of $3$ ($15$) for EMRIs (IMRIs) with only minor $(\sim10\%)$ dependence on $e$ for an SNR threshold of 20. For signals with SNR $\sim 50$, spin and eccentricity-at-plunge are measured with uncertainties of $\delta a \sim 10^{-7}$ and $\delta e_f \sim 10^{-5}$. This work advances the state-of-the-art in waveform generation for asymmetric-mass binaries.

gr-qc↗

arXiv2025

Reconstructing parametric gravitational-wave population fits from non-parametric results without refitting the data

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation.

astro-ph.HE↗

arXiv2025

Impact of relativistic waveforms in LISA's science objectives with extreme-mass-ratio inspirals

Extreme-Mass-Ratio Inspirals (EMRIs) are one of the key targets for future space-based gravitational wave detectors, such as LISA. The scientific potential of these sources can only be fully realized with fast and accurate waveform models. In this work, we extend the \textsc{FastEMRIWaveform} (\texttt{FEW}) framework by providing fully relativistic waveforms at adiabatic order for circular, equatorial orbits in Kerr spacetime, for mass ratios up to $10^{-3}$. We investigate the significance of including relativistic corrections in the waveform for both vacuum and non-vacuum environments. Specifically, we develop relativistic non-vacuum EMRI waveforms including two different environmental effects in the EMRI waveforms: power-law migration torques, and superradiance scalar clouds. For EMRIs in vacuum, we find that non-relativistic waveforms incorrectly estimate the predicted source's horizon redshift by approximately $35\%$ error. Our analysis shows that incorporating relativistic corrections enhances constraints on accretion disks, modeled through power-law torques, and improves the constraints on disk parameter estimates (error $\simeq 8\%$), representing a significant improvement over previous estimates. Additionally, we assess the evidence for models in a scenario where ignoring the accretion disk biases the parameter estimation (PE), reporting a $\log_{10}$ Bayes factor of $1.1$ in favor of the accretion disk model. In a fully relativistic setup, we also estimate the parameters of superradiant scalar clouds with relative errors $\simeq 0.3\%$ for the scalar cloud's mass. These results demonstrate that incorporating relativistic effects is essential for LISA science objectives with EMRIs.

gr-qc↗

arXiv2025

Tensor Network Techniques for Quantum Computation

This book serves as an introductory yet thorough guide to tensor networks and their applications in quantum computation and quantum information, designed for advanced undergraduate and graduate-level readers. In Part I, foundational topics are covered, including tensor structures and network representations like Matrix Product States (MPS) and Tree Tensor Networks (TTN). These preliminaries provide readers with the core mathematical tools and concepts necessary for quantum physics and quantum computing applications, bridging the gap between multi-linear algebra and complex quantum systems. Part II explores practical applications of tensor networks in simulating quantum dynamics, with a particular focus on the efficiency they offer for systems of high computational complexity. Key topics include Hamiltonian dynamics, quantum annealing, open system dynamics, and optimization strategies using TN frameworks. A final chapter addresses the emerging role of "quantum magic" in tensor networks. It delves into non-stabilizer states and their contribution to quantum computational power beyond classical simulability, featuring methods such as stabilizer-enhanced MPS and the Clifford-dressed TDVP.

quant-ph↗

arXiv2025

Clifford-Dressed Variational Principles for Precise Loschmidt Echoes

We extend the recently introduced Clifford dressed Time-Dependent Variational Principle (TDVP) to efficiently compute many-body wavefunction amplitudes in the computational basis. This advancement enhances the study of Loschmidt echoes, which generally require accurate calculations of the overlap between the evolved state and the initial wavefunction. By incorporating Clifford disentangling gates during TDVP evolution, our method effectively controls entanglement growth while keeping the computation of these amplitudes accessible. Specifically, it reduces the problem to evaluating the overlap between a Matrix Product State (MPS) and a stabilizer state, a task that remains computationally feasible within the proposed framework. To demonstrate the effectiveness of this approach, we first benchmark it on the one-dimensional transverse-field Ising model. We then apply it to more challenging scenarios, including a non-integrable next-to-nearest-neighbor Ising chain and a two-dimensional Ising model. Our results highlight the versatility and efficiency of the Clifford-augmented MPS, showcasing its capability to go beyond the evaluation of simple expectation values. This makes it a powerful tool for exploring various aspects of many-body quantum dynamics.

quant-ph↗

arXiv2025

Dynamical deconfinement transition driven by density of excitations

We investigate the deconfinement transition driven by excitations in long-range spin models. At low temperatures, these models exhibit a confined phase where domain-wall (or kinks) are localized. As temperature increases, kinks interact and propagate, leading to a transition to a de-confined phase. This transition is influenced by the interplay between thermal energy and interaction effects, resulting in extended, de-confined regions. Although kinks density is dynamically stable, non-equilibrium changes in their fluctuations characterize the transition. Our findings provide insights into the mechanisms of confinement and deconfinement in long-range spin models, with implications for both condensed matter physics and lattice gauge theories. Bridging these fields, this study sheds light on the universal aspects of confinement and opens avenues for further exploration and experimental verification.

cond-mat.str-el↗

arXiv2024

Cahier de l'Institut Pascal: Noisy Quantum Dynamics and Measurement-Induced Phase Transitions

This is a conference proceeding in the framework of workshop "OpenQMBP2023" at Institute Pascal (Orsay, France) and associated to the lecture given by Prof. Ehud Altman. We provide a comprehensive analysis of recent results in the context of measurement-induced phase transitions (MIPT) in quantum systems, with a particular focus on hybrid quantum circuits as a model system in one-dimension. Recent results, demonstrate how varying the rate of projective measurements can induce phase transitions, resulting in abrupt changes in the properties of the entanglement. The interplay between unitary evolution and measurement processes can be investigated, through mappings to classical statistical models and the application of replica field theory techniques. Starting from a low-entangled state, there can be three regimes characterized by different dynamics of bipartite entanglement entropies for a portion of the system: high-rate measurements leading to rapid entanglement saturation (area law), low-rate measurements allowing linear entanglement growth (up to volume law), and a critical rate at which entanglement grows logarithmically. Finally, we present results on the non-local effects of local measurements by examining the field theory of critical ground states in Tomonaga-Luttinger liquids.

cond-mat.stat-mech↗

arXiv2024

Semiclassical Quantum Trajectories in the Monitored Lipkin-Meshkov-Glick Model

Monitored quantum system have sparked great interest in recent years due to the possibility of observing measurement-induced phase transitions (MIPTs) in the full-counting statistics of the quantum trajectories associated with different measurement outcomes. Here, we investigate the dynamics of the Lipkin-Meshkov-Glick model, composed of $N$ all-to-all interacting spins $1/2$, under a weak external monitoring. We derive a set of semiclassical stochastic equations describing the evolution of the expectation values of global spin observables, which become exact in the thermodynamic limit. Our results shows that the limit $N\to\infty$ does not commute with the long-time limit: while for any finite $N$ the esamble average over the noise is expected to converge towards a trivial steady state, in the thermodynamic limit a MIPT appears. The transition is not affected by post-selection issues, as it is already visible at the level of ensemble averages, thus paving the way for experimental observations. We derive a quantitative theoretical picture explaining the nature of the transition within our semiclassical picture, finding an excellent agreement with the numerics.

quant-ph↗

arXiv2024

Clifford Dressed Time-Dependent Variational Principle

We propose an enhanced Time-Dependent Variational Principle (TDVP) algorithm for Matrix Product States (MPS) that integrates Clifford disentangling techniques to efficiently manage entanglement growth. By leveraging the Clifford group, which maps Pauli strings to other Pauli strings while maintaining low computational complexity, we introduce a Clifford dressed single-site 1-TDVP scheme. During the TDVP integration, we apply a global Clifford transformation as needed to reduce entanglement by iteratively sweeping over two-qubit Clifford unitaries that connect neighboring sites in a checkerboard pattern. We validate the new algorithm numerically using various quantum many-body models, including both integrable and non-integrable systems. Our results demonstrate that the Clifford dressed TDVP significantly improves entanglement management and computational efficiency, achieving higher accuracy, extended simulation times, and enhanced precision in computed observables compared to standard TDVP approaches. Additionally, we propose incorporating Clifford gates directly within the two-site 2-TDVP scheme.

quant-ph↗

arXiv2024

Hybrid Stabilizer Matrix Product Operator

We introduce a novel hybrid approach combining tensor network methods with the stabilizer formalism to address the challenges of simulating many-body quantum systems. By integrating these techniques, we enhance our ability to accurately model unitary dynamics while mitigating the exponential growth of entanglement encountered in classical simulations. We demonstrate the effectiveness of our method through applications to random Clifford T-doped circuits and Random Clifford Floquet Dynamics. This approach offers promising prospects for advancing our understanding of complex quantum phenomena and accelerating progress in quantum simulation.

quant-ph↗

arXiv2023

Black-hole mergers in disklike environments could explain the observed $q$-$χ_\mathrm{eff}$ correlation

Current gravitational-wave data from stellar-mass black-hole binary mergers suggest a correlation between the binary mass ratio $q$ and the effective spin $χ_\mathrm{eff}$: more unequal-mass binaries consistently show larger and positive values of the effective spin. Multiple generations of black-hole mergers in dense astrophysical environments may provide a way to form unequal-mass systems, but they cannot explain the observed correlation on their own. We show that the symmetry of the astrophysical environment is a crucial feature to shed light on this otherwise puzzling piece of observational evidence. We present a toy model that reproduces, at least qualitatively, the observed correlation. The model relies on axisymmetric, disk-like environments where binaries participating in hierarchical mergers share a preferential direction. Migration traps in AGN disks are a prime candidate for this setup, hinting at the exciting possibility of constraining their occurrence with gravitational-wave data.

astro-ph.HE↗

arXiv2023

Full counting statistics as probe of measurement-induced transitions in the quantum Ising chain

Non-equilibrium dynamics of many-body quantum systems under the effect of measurement protocols is attracting an increasing amount of attention. It has been recently revealed that measurements may induce different non-equilibrium regimes and an abrupt change in the scaling-law of the bipartite entanglement entropy. However, our understanding of how these regimes appear, how they affect the statistics of local quantities and, finally whether they survive in the thermodynamic limit, is much less established. Here we investigate measurement-induced phase transitions in the Quantum Ising chain coupled to a monitoring environment. In particular we show that local projective measurements induce a quantitative modification of the out-of-equilibrium probability distribution function of the local magnetization. Starting from a GHZ state, the relaxation of the paramagnetic and the ferromagnetic order is analysed. In particular we describe how the probability distribution of the former shows different behaviour in the area-law and volume-law regimes.

cond-mat.stat-mech↗