arXiv subjects

Enrico Rinaldi

Publications and source records attributed to Enrico Rinaldi.

At least 55 records · Page 3Linked to original sources

arXiv2024

Estimating truncation effects of quantum bosonic systems using sampling algorithms

To simulate bosons on a qubit- or qudit-based quantum computer, one has to regularize the theory by truncating infinite-dimensional local Hilbert spaces to finite dimensions. In the search for practical quantum applications, it is important to know how big the truncation errors can be. In general, it is not easy to estimate errors unless we have a good quantum computer. In this paper, we show that traditional sampling methods on classical devices, specifically Markov Chain Monte Carlo, can address this issue for a rather generic class of bosonic systems with a reasonable amount of computational resources available today. As a demonstration, we apply this idea to the scalar field theory on a two-dimensional lattice, with a size that goes beyond what is achievable using exact diagonalization methods. This method can be used to estimate the resources needed for realistic quantum simulations of bosonic theories, and also, to check the validity of the results of the corresponding quantum simulations.

quant-ph↗

arXiv2023

Stealth dark matter spectrum using LapH and Irreps

We present non-perturbative lattice calculations of the low-lying meson and baryon spectrum of the SU(4) gauge theory with fundamental fermion constituents. This theory is one instance of stealth dark matter, a class of strongly coupled theories, where the lowest mass stable baryon is the dark matter candidate. This work constitutes the first milestone in the program to study stealth dark matter self-interactions. Here, we focus on reducing excited state contamination in the single baryon channel by applying the Laplacian Heaviside method, as well as projecting our baryon operators onto the irreducible representations of the octahedral group. We compare our resulting spectrum to previous work involving Gaussian smeared non-projected operators and find good agreement with reduced statistical uncertainties. We also present the spectrum of the low-lying odd-parity baryons for the first time.

hep-lat↗

arXiv2023

First-order bulk transitions in large-$N$ lattice Yang--Mills theories using the density of states

We use the Logarithmic Linear Relaxation (LLR) density of states algorithm to study the bulk phase transitions of pure-gauge SU($N$) lattice Yang--Mills theories with $4 \leq N \leq 8$. This approach avoids super-critical slowing down at such transitions, which poses a problem for traditional importance sampling Monte-Carlo methods. We analyse the effect of different updating strategies within the LLR algorithm, different reconstruction techniques of the density of states and different lattice volumes. By comparing our results for the weakly first-order SU(5) bulk phase transition against those for the stronger transitions with $N \geq 6$, we demonstrate the advantages of the LLR method for analyses of strong transitions with large latent heat.

hep-lat↗

arXiv2023

The deconfinement phase transition in $Sp(2N)$ gauge theories and the density of states method

First-order phase transitions in the early universe might produce a detectable background of gravitational waves. As these phase transitions can be generated by new physics, it is important to quantify these effects. Many pure Yang-Mills gauge theories are known to undergo first-order deconfinement phase transitions, with properties that can be studied with lattice simulations. Despite the recent surge of interest in $Sp(2N)$ gauge theories as a candidate for models of physics beyond the standard model, studies of these theories at finite temperature are still very limited. In this contribution we will present preliminary results of an ongoing numerical investigation of the thermodynamic properties of the deconfinement phase transition in $Sp(4)$ Yang-Mills theory, using the linear logarithmic relaxation algorithm. This method enables us to obtain a highly accurate determination of the density of states, allowing for a precise reconstruction of thermodynamic observables. In particular, it gives access to otherwise difficult to determine quantities such as the free energy of the system, even along metastable and unstable branches, hence providing an additional direct observable to study the dynamics of the phase transition.

hep-lat↗

arXiv2023

Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks

We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings.

quant-ph↗

arXiv2023

First-order phase transitions in Yang-Mills theories and the density of state method

When studied at finite temperature, Yang-Mills theories in $3+1$ dimensions display the presence of confinement/deconfinement phase transitions, which are known to be of first order -- the $SU(2)$ gauge theory being the exception. Theoretical as well as phenomenological considerations indicate that it is essential to establish a precise characterisation of these physical systems in proximity of such phase transitions. We present and test a new method to study the critical region of parameter space in non-Abelian quantum field theories on the lattice, based upon the Logarithmic Linear Relaxation (LLR) algorithm. We apply this method to the $SU(3)$ Yang Mills lattice gauge theory, and perform extensive calculations with one fixed choice of lattice size. We identify the critical temperature, and measure interesting physical quantities near the transition. Among them, we determine the free energy of the model in the critical region, exposing for the first time its multi-valued nature with a numerical calculation from first principles, providing this novel evidence in support of a first order phase transition. This study sets the stage for future high precision measurements, by demonstrating the potential of the method.

hep-lat↗

arXiv2023

Approximate Autonomous Quantum Error Correction with Reinforcement Learning

Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances $d\geq 2$. Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning (RL), we identify the optimal bosonic set of codewords (denoted here by RL code), which, surprisingly, is composed of the Fock states $\vert 2\rangle$ and $\vert 4\rangle$. As we show, the RL code, despite its approximate nature, successfully suppresses single-photon loss, reducing it to an effective dephasing process that well surpasses the break-even threshold. It may thus provide a valuable building block toward full error protection. The error-correcting Hamiltonian, which includes ancilla systems that emulate the engineered dissipation, is entirely based on the Hamiltonian distance $d=1$, significantly reducing model complexity. Single-qubit gates are implemented in the RL code with a maximum distance $d_g=2$.

quant-ph↗

arXiv2023

GRB Optical and X-ray Plateau Properties Classifier Using Unsupervised Machine Learning

The division of Gamma-ray bursts (GRBs) into different classes, other than the "short" and "long", has been an active field of research. We investigate whether GRBs can be classified based on a broader set of parameters, including prompt and plateau emission ones. Observational evidence suggests the existence of more GRB sub-classes, but results so far are either conflicting or not statistically significant. The novelty here is producing a machine-learning-based classification of GRBs using their observed X-rays and optical properties. We used two data samples: the first, composed of 203 GRBs, is from the Neil Gehrels Swift Observatory (Swift/XRT), and the latter, composed of 134 GRBs, is from the ground-based Telescopes and Swift/UVOT. Both samples possess the plateau emission (a flat part of the light curve happening after the prompt emission, the main GRB event). We have applied the Gaussian Mixture Model (GMM) to explore multiple parameter spaces and sub-class combinations to reveal if there is a match between the current observational sub-classes and the statistical classification. With these samples and the algorithm, we spot a few micro-trends in certain cases, but we cannot conclude that any clear trend exists in classifying GRBs. These microtrends could point towards a deeper understanding of the physical meaning of these classes (e.g., a different environment of the same progenitor or different progenitors). However, a larger sample and different algorithms could achieve such goals. Thus, this methodology can lead to deeper insights in the future.

astro-ph.HE↗

arXiv2023

A Stochastic Approach To Reconstruct Gamma Ray Burst Lightcurves

Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, we must have good coverage of GRB light curves (LCs). However, gaps in the LC hinder the precise determination of GRB properties and are often unavoidable. Therefore, extensive categorization of GRB LCs remains a hurdle. We address LC gaps using a 'stochastic reconstruction,' wherein we fit two pre-existing models (Willingale 2007; W07 and Broken Power Law; BPL) to the observed LC, then use the distribution of flux residuals from the original data to generate data to fill in the temporal gaps. We also demonstrate a model-independent LC reconstruction via Gaussian Processes. At 10% noise, the uncertainty of the end time of the plateau, its correspondent flux, and the temporal decay index after the plateau decreases, on average, by 33.3% 35.03%, and 43.32%, respectively for the W07, and by 33.3%, 30.78%, 43.9% for the BPL. The slope of the plateau decreases by 14.76% in the BPL. After using the Gaussian Process technique, we see similar trends of a decrease in uncertainty for all model parameters for both the W07 and BPL models. These improvements are essential for the application of GRBs as standard candles in cosmology, for the investigation of theoretical models and for inferring the redshift of GRBs with future machine learning analysis.

astro-ph.HE↗

arXiv2023

MEET: A Monte Carlo Exploration-Exploitation Trade-off for Buffer Sampling

Data selection is essential for any data-based optimization technique, such as Reinforcement Learning. State-of-the-art sampling strategies for the experience replay buffer improve the performance of the Reinforcement Learning agent. However, they do not incorporate uncertainty in the Q-Value estimation. Consequently, they cannot adapt the sampling strategies, including exploration and exploitation of transitions, to the complexity of the task. To address this, this paper proposes a new sampling strategy that leverages the exploration-exploitation trade-off. This is enabled by the uncertainty estimation of the Q-Value function, which guides the sampling to explore more significant transitions and, thus, learn a more efficient policy. Experiments on classical control environments demonstrate stable results across various environments. They show that the proposed method outperforms state-of-the-art sampling strategies for dense rewards w.r.t. convergence and peak performance by 26% on average.

cs.LG↗

arXiv2023

Linear confinement in the partially-deconfined phase

We consider the partially-deconfined saddle of large-$N$ pure Yang-Mills theory lying between confined and deconfined phases, in which the color degrees of freedom split into confined and deconfined sectors. Based on the microscopic mechanism of deconfinement, we argue that a flux tube is formed in the confined sector and a linear confinement potential is generated. The string tension should not depend on the size of the confined sector. We provide evidence by studying the finite-temperature strong-coupling lattice gauge theory. In particular, we make analytic predictions assuming linear confinement in the confined sector, and then confirm these by numerical simulations. We discuss some implications of the conjecture to QCD and holography.

hep-th↗

arXiv2023

Precision test of gauge/gravity duality in D0-brane matrix model at low temperature

We test the gauge/gravity duality between the matrix model and type IIA string theory at low temperatures with unprecedented accuracy. To this end, we perform lattice Monte Carlo simulations of the Berenstein-Maldacena-Nastase (BMN) matrix model, which is the one-parameter deformation of the Banks-Fischler-Shenker-Susskind (BFSS) matrix model, taking both the large $N$ and continuum limits. We leverage the fact that sufficiently small flux parameters in the BMN matrix model have a negligible impact on the energy of the system while stabilizing the flat directions so that simulations at smaller $N$ than in the BFSS matrix model are possible. Hence, we can perform a precision measurement of the large $N$ continuum energy at the lowest temperatures to date. The energy is in perfect agreement with supergravity predictions including estimations of $α'$-corrections from previous simulations. At the lowest temperature where we can simulate efficiently ($T=0.25λ^{1/3}$, where $λ$ is the 't Hooft coupling), the difference in energy to the pure supergravity prediction is less than $10\%$. Furthermore, we can extract the coefficient of the $1/N^4$ corrections at a fixed temperature with good accuracy, which was previously unknown.

hep-th↗

arXiv2022

Binary-coupling sparse SYK: an improved model of quantum chaos and holography

The sparse version of the Sachdev-Ye-Kitaev (SYK) model reproduces essential features of the original SYK model while reducing the number of disorder parameters. In this paper, we propose a further simplification of the model which we call the binary-coupling sparse SYK model. We set the nonzero couplings to be $\pm 1$, rather than being sampled from a continuous distribution such as Gaussian. Remarkably, this simplification turns out to be an improvement: the binary-coupling model exhibits strong correlations in the spectrum, which is the important feature of the original SYK model that leads to the quick onset of the random-matrix universality, more efficiently in terms of the number of nonzero terms. This model is better suited for analog or digital quantum simulations of quantum chaotic behavior and holographic metals due to its simplicity and scaling properties.

quant-ph↗

arXiv2022

The density of state method for first-order phase transitions in Yang-Mills theories

Lattice Field Theory can be used to study finite temperature first-order phase transitions in new, strongly-coupled gauge theories of phenomenological interest. Metastable dynamics arising in proximity of the phase transition can lead to large, uncontrolled numerical errors when analysed with standard methods. In this contribution, we discuss a prototype lattice calculation in which the first-order deconfinement transition in the strong Yang-Mills sector of the standard model is analysed using a novel lattice method, the logarithmic linear relaxation algorithm. This method provides a determination of the density of states of the system with exponential error suppression.

hep-lat↗

arXiv2022

The density of states method in Yang-Mills theories and first order phase transitions

Extensions of the standard model that lead to first-order phase transitions in the early universe can produce a stochastic background of gravitational waves, which may be accessible to future detectors. Thermodynamic observables at the transition, such as the latent heat, can be determined by lattice simulations, and then used to predict the expected signatures in a given theory. In lattice calculations, the emergence of metastabilities in proximity of the phase transition may make the precise determination of these observables quite challenging, and may lead to large uncontrolled numerical errors. In this contribution, we discuss as a prototype lattice calculation the first order deconfinement transition that arises in the strong SU(3) Yang-Mills sector. We adopt the novel logarithmic linear relaxation method, which can provide a determination of the density of states of the system with exponential error suppression. Thermodynamic observables can be reconstructed with a controlled error, providing a promising direction for accurate model predictions in the future.

hep-lat↗

arXiv2022

Lattice QCD and Particle Physics

Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).

hep-lat↗

arXiv2022

Nonperturbative test of the Maldacena-Milekhin conjecture for the BMN matrix model

We test a conjecture by Maldacena and Milekhin for the ungauged version of the Berenstein-Maldacena-Nastase (BMN) matrix model by lattice Monte Carlo simulation. The numerical results reproduce the perturbative and gravity results in the limit of large and small flux parameter, respectively, and are consistent with the conjecture.

hep-th↗

arXiv2022

Optical and X-ray GRB Fundamental Planes as Cosmological Distance Indicators

Gamma-Ray Bursts (GRBs), can be employed as standardized candles, extending the distance ladder beyond Supernovae Type Ia (SNe Ia, $z=2.26$). We standardize GRBs using the 3D fundamental plane relation (the Dainotti relation) among the rest-frame end time of the X-ray plateau emission, its corresponding luminosity, and the peak prompt luminosity. Combining SNe Ia and GRBs, we constrain $Ω_{\text{M}}= 0.299 \pm 0.009$ assuming a flat $Λ$CDM cosmology with and without correcting GRBs for selection biases and redshift evolution. Using a 3D optical Dainotti correlation, we find this sample is as efficacious in the determination of $Ω_{\text{M}}$ as the X-ray sample. We trimmed our GRB samples to achieve tighter planes to simulate additional GRBs. We determined how many GRBs are needed as standalone probes to achieve a comparable precision on $Ω_{\text{M}}$ to the one obtained by SNe Ia only. We reach the same error measurements derived using SNe Ia in 2011 and 2014 with 142 and 284 simulated optical GRBs, respectively, considering the errorbars on the variables halved. These error limits will be reached in 2038 and in 2047, respectively. Using a doubled sample (obtained by future machine learning approaches allowing a lightcurve reconstruction and the estimates of GRB redhifts when z is unknown) compared to the current sample, with errorbars halved we will reach the same precision as SNe Ia in 2011 and 2014, now and in 2026, respectively. If we consider the current SNe precision, this will be reached with 390 optical GRBs by 2054.

astro-ph.CO↗