arXiv subjects

Pedro M. Prado

Publications and source records attributed to Pedro M. Prado.

3 recordsLinked to original sources

arXiv2026

Quantum feedback algorithms for DNA assembly using FALQON variants

Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this problem, a usual strategy is to map the assembly to a Quadratic Unconstrained Binary Optimization (QUBO) formulation, which can be solved by different quantum algorithms. In this work, we focus on three versions of the Feedback-based Algorithm, a protocol that eliminates classical optimization loops via measurement feedback. We analyze long-read DNA fragments from SARS-CoV-2 and human mitochondrial DNA using standard FALQON, second-order FALQON (SO-FALQON), and time-rescaled FALQON (TR-FALQON). Numerical results show that both variants improve convergence to the ground state and increase success probabilities at reduced circuit depths. These findings indicate that enhanced feedback-driven dynamics are effective for solving combinatorial problems on near-term quantum hardware.

quant-ph↗

arXiv2026

Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm

Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to sufficiently concentrate probability on low-energy configurations. We introduce Measurement-Guided Initialization (MGI), an iterative strategy that uses measurement outcomes from previous executions to update the initialization of subsequent runs. The method extracts single-qubit marginal probabilities from dominant measurement outcomes and prepares a biased product-state initialization, allowing information obtained during optimization to be reused without introducing classical parameter optimization. We implement this approach in the context of the Feedback-Based Algorithm for Quantum Optimization (FALQON) and evaluate its performance on weighted MaxCut instances. Numerical results show that measurement-guided initialization improves the performance of shallow-depth circuits and enables iterative refinement toward high-quality solutions while preserving the non-variational structure of the algorithm. These results indicate that measurement statistics can be exploited to improve shallow quantum optimization protocols compatible with NISQ devices.

quant-ph↗

arXiv2025

Quantum Phases Classification Using Quantum Machine Learning with SHAP-Driven Feature Selection

In this study, we present an innovative methodology to classify quantum phases within the ANNNI (Axial Next-Nearest Neighbor Ising) model by combining Quantum Machine Learning (QML) techniques with the Shapley Additive Explanations (SHAP) algorithm for feature selection and interpretability. Our investigation focuses on two prominent QML algorithms: Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC). By leveraging SHAP, we systematically identify the most relevant features within the dataset, ensuring that only the most informative variables are utilized for training and testing. The results reveal that both QSVM and VQC exhibit exceptional predictive accuracy when limited to 5 or 6 key features, thereby enhancing performance and reducing computational overhead. This approach not only demonstrates the effectiveness of feature selection in improving classification outcomes but also offers insights into the interpretability of quantum classification tasks. The proposed framework exemplifies the potential of interdisciplinary solutions for addressing challenges in the classification of quantum systems, contributing to advancements in both machine learning and quantum physics.

quant-ph↗