arXiv subjects

Marcelo A. Moret

Publications and source records attributed to Marcelo A. Moret.

7 recordsLinked to original sources

arXiv2026

Molecular Docking with Quantum Circuit Evolution

Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.

quant-ph↗

arXiv2026

Robust q-negative Multifractal Detrended Cross-Correlation Coefficient

The multifractal detrended cross-correlation coefficient $ρ_q(n)$ is widely used to investigate scale-dependent interactions, but its application to negative fluctuation orders is affected by numerical instabilities, unbounded values, and interpretational difficulties. We propose a Signed Multifractal Detrended Cross-Correlation Coefficient, $ρ_{\mathrm{SMFDCCA}}(n,q)$, an amplitude-conditioned correlation observable for multifractal detrended analysis, based on locally normalized detrended correlations and regularized fluctuation amplitudes. The proposed coefficient preserves the sign of local interactions, remains strictly bounded within $[-1,1]$ for both positive and negative values of $q$, and eliminates the corrective procedures required by previous approaches. Validation using independent fractional Gaussian noise confirms the absence of spurious cross-correlations and the numerical stability of the method. Applications demonstrate that the proposed observable resolves how cross-correlations evolve jointly with temporal scale and fluctuation amplitude, revealing scale- and amplitude-dependent correlation structures, including stronger synchronization during large fluctuations in stock-market indices and heterogeneous coupling patterns in temperature records.

cond-mat.stat-mech↗

arXiv2025

Variational quantum computing for quantum simulation: principles, implementations, and challenges

This work presents a comprehensive overview of variational quantum computing and their key role in advancing quantum simulation. This work explores the simulation of quantum systems and sets itself apart from approaches centered on classical data processing, by focusing on the critical role of quantum data in Variational Quantum Algorithms (VQA) and Quantum Machine Learning (QML). We systematically delineate the foundational principles of variational quantum computing, establish their motivational and challenges context within the noisy intermediate-scale quantum (NISQ) era, and critically examine their application across a range of prototypical quantum simulation problems. Operating within a hybrid quantum-classical framework, these algorithms represent a promising yet problem-dependent pathway whose practicality remains contingent on trainability and scalability under noise and barren-plateau constraints.This review serves to complement and extend existing literature by synthesizing the most recent advancements in the field and providing a focused perspective on the persistent challenges and emerging opportunities that define the current landscape of variational quantum computing for quantum simulation.

quant-ph↗

arXiv2025

Qiskit Variational Quantum Classifier on the Pulsar Classification Problem

Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported. Keywords: Quantum Computing, Quantum Machine Learning, Astrophysics, Pulsars

quant-ph↗

arXiv2025

Variational quantum simulation of a nonadditive relaxation dynamics in a qubit coupled to a finite-temperature bath

In this paper, we present an application of the variational quantum simulation (VQS) framework to capture finite-temperature open-system dynamics on near-term quantum hardware. By embedding the generalized amplitude-damping channel into the VQS algorithm, we modeled the energy exchange with a thermal bath through its Lindblad representation and thereby simulated realistic dissipative effects. To explore a wide range of activation behaviors, we introduce a nonadditive relaxation-time model using a generalized form of the Arrhenius law, based on the phenomenological parameter q. We compare our method on a driven qubit subject to both static and composite time-dependent fields, comparing population evolution and trace distance errors against exact solutions. Our results demonstrate that (i) VQS accurately maps the effective nonunitary generator under generalized amplitude damping, (ii) smoother drive envelopes induced by nonaddtive parameters suppress high frequency components and yield lower simulation errors, and (iii) the variational manifold exhibits dynamical selectivity, maintaining mapping fidelity even as the exact solution's sensitivity to q increases. Our results demonstrate that (i) VQS accurately maps the effective nonunitary generator under generalized amplitude damping, (ii) smoother drive envelopes induced by nonaddtive parameters suppress high frequency components and yield lower simulation errors, and (iii) the variational manifold exhibits dynamical selectivity, maintaining mapping fidelity even as the exact solution's sensitivity to q increases.

quant-ph↗

arXiv2021

Why and How Coronavirus Has Evolved to Be Uniquely Contagious, with Uniquely Successful Stable Vaccines

Spike proteins, 1200 amino acids, are divided into two nearly equal parts, S1 and S2. We review here phase transition theory, implemented quantitatively by thermodynamic scaling. The theory explains the evolution of Coronavirus extremely high contagiousness caused by a few mutations from CoV2003 to CoV2019 identified among hundreds in S1. The theory previously predicted the unprecedented success of spike-based vaccines. Here we analyze impressive successes by McClellan et al., 2020, in stabilizing their original S2P vaccine to Hexapro. Hexapro has expanded the two proline mutations of S2P, 2017, to six combined proline mutations in S2. Their four new mutations are the result of surveying 100 possibilities in their detailed structure-based context Our analysis, based on only sparse publicly available data, suggests new proline mutations could improve the Hexapro combination to Octapro or beyond.

q-bio.BM↗

arXiv2020

How Does ENSO Impact the Solar Radiation Forecast in South America? The Self-affinity Analysis Approach

The major challenge we face today in the energy sector is to meet the growing demand for electricity with less impact on the environment. South America is an important player in the renewable energy resource. Brazil accelerated the growth of photovoltaic installed capacity in 2018. From April of 2017 to April of 2018, the capacity increased $1351.5\%$. It is expected to reach the value of $2.4\ GW$ until the end of the year. The new Chilean regulation request that 20\% of the total electricity production in 2025 must come from renewable energy sources. The aim of this paper is to establish time series behavior changes between El Niño Southern Oscillation and the solar radiation resource in South America. The results can be used to validate forecasts of energy production for new solar plants. The method used to verify the behavior of the time series was the Detrended Fluctuation Analysis. Solar radiation data were collected in twenty-five cities distributed inside the Brazilian solar belt, plus six cities in Chile, covering the continent from east to west, in a region with high potential of solar photovoltaic generation. The results shows the impact of El Niño Southern Oscillation on the climatic behavior of the evaluated data. It is a factor that may lead to the wrong forecast of the long term potential solar power generation for the region.

physics.ao-ph↗