arXiv subjects

Daniel Barragan-Yani

Publications and source records attributed to Daniel Barragan-Yani.

9 recordsLinked to original sources

arXiv2026

QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys

Machine-learned forward models can rapidly predict alloy properties, but their use for inverse design remains challenging when the search should also retain compatibility with quadratic unconstrained binary optimization (QUBO). Here, we develop a QUBO-compatible active-learning framework for inverse design of high-entropy alloys using a pretrained graph-neural-network predictor as a fixed property oracle. A property-guided binary variational autoencoder provides a binary latent representation, while an ensemble of quadratic factorization machines guides candidate selection. We systematically benchmark the framework through controlled latent-space ablations and comparison with direct composition-space optimization. The results show that candidate generation is a major determinant of search performance: local perturbations around previously high-performing latent codes provide the largest workflow-specific improvement, while surrogate-based selection further prioritizes candidates within the enriched search pool. The resulting QUBO-compatible workflow remains competitive with strong classical optimization strategies, although a composition-space genetic algorithm achieves the highest mean score. Finally, the learned quadratic surrogate can be exported directly as a QUBO. These results show that effective data acquisition can be separated from the final QUBO optimization endpoint, providing a benchmarked route for QUBO-compatible data-driven materials inverse design.

cond-mat.mtrl-sci↗

arXiv2026

A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional

We introduce a novel approach that exploits the intersection of quantum computing, machine learning and reduced density matrix functional theory to leverage the potential of quantum computing to improve simulations of interacting quantum particles. Our method focuses on obtaining the universal functional using a deep neural network trained with quantum algorithms. In addition, we use density matrix embedding theory to strengthen our approach by substantially expanding the space of Hamiltonians for which the obtained functional can be applied without the need for additional quantum resources. Since the obtained universal functional can be reused for any system where the interactions within the embedded fragment are identical, our work demonstrates a way to potentially achieve a cumulative quantum advantage within quantum computing applications for quantum chemistry and condensed matter physics.

quant-ph↗

arXiv2026

Efficient Operator Selection and Warm-Start Strategy for Excitations in Variational Quantum Eigensolvers

We present a novel approach for efficient preparation of electronic ground states, leveraging the optimizer ExcitationSolve [Jäger et al., Comm. Phys. (2025)] and established variational quantum eigensolver-based operator selection methods, such as Energy Sorting (ES). By combining these tools, we demonstrate a computationally efficient protocol that enables the construction of an approximate ground state from a unitary coupled cluster ansatz via a single sweep over the operator pool. Utilizing efficient classical pre-processing to select the majority of relevant operators, this approach reduces the computational complexity associated with traditional variational quantum eigensolver (VQE) optimization methods. We further show that second-order Epstein-Nesbet (EN2) perturbation theory emerges as the first-order Taylor expansion of our protocol in terms of a correlation measure, clarifying why our approach provides a more robust initial guess for the ground state in strongly correlated regimes. We also find that second-order Møller-Plesset perturbation (MP2) theory, which is widely used for unitary coupled cluster (UCC) initialization, performs worse than both EN2 and our protocol. Furthermore, we show that our method can be seamlessly integrated with one-variational-parameter couple exchange operators, thereby further reducing the number of required CNOT operations. Overall, we empirically observe a quadratic convergence speedup beyond state-of-the-art methods, advancing the preparation of high-fidelity electronic ground states - one of the cornerstones of meaningful electronic structure calculations in the noisy intermediate-scale quantum computing (NISQ) era, and a prerequisite for fault-tolerant quantum computing (FTQC) algorithms such as quantum phase estimation.

quant-ph↗

arXiv2026

Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing

Quantum annealing has the potential to provide practical quantum advantage for complex optimization tasks. Here, we present a systematic assessment of an integrated factorization-machine and quantum-annealing workflow (FM+QA) for a technologically relevant application: multi-objective Pareto optimization in metal up-cycling through alloy design. To address the non-convex nature of the Pareto front, we employ the recently proposed data-driven Tchebycheff scalarization (DDTS) scheme. Our results show that FM+QA extends the applicability of QUBO-based optimization to data-driven materials discovery problems with multiple competing objectives. In particular, we analyze the scaling behavior of the approach and compare quantum annealing with classical simulated annealing using both regular binary encoding and one-hot encoding. Finally, we provide a critical perspective on the problem sizes and encoding strategies for which quantum-annealing-based optimization may become practically beneficial in the near future.

cond-mat.mtrl-sci↗

arXiv2025

Electronic and optical properties of native point defects in CuInS$_2$ and CuGaS$_2$

We present a detailed study of common intrinsic defects in CuInS$_2$ and CuGaS$_2$ using the Heyd, Scuseria and Ernzerhof (HSE) hybrid functional scheme. The impact of the two HSE parameters, $α$ and $ω$ on the band gap and compliance with the generalized Koopmans' theorem is investigated. Using the formation energy formalism and calculated thermodynamic charge-transition levels, we assess the electronic properties of the defects and explore the connection of charge-transition levels with optical-transition levels. Calculated Franck-Condon shifts for emission highlight the importance of lattice relaxation for the attribution of defects to luminescence peaks. Our results show that once these effects are included, predictions become closer to photoluminescence measurements available in literature.

cond-mat.mtrl-sci↗

arXiv2025

Progress on Data-Driven, Multi-Objective Quantum Optimization

Here, we present two complementary approaches that advance quadratic unconstrained binary optimization (QUBO) toward practical use in data-driven materials design and other real-valued black-box optimization tasks. First, we introduce a simple yet powerful preprocessing scheme that, when applied to a machine-learned QUBO model, entirely removes system-level equality constraints by construction. This makes cumbersome soft-penalty terms obsolete, simplifies QUBO formulation, and substantially accelerates solution search. Second, we develop a multi-objective optimization strategy inspired by Tchebycheff scalarization that is compatible with non-convex objective landscapes and outperforms existing QUBO-based Pareto front methods. We demonstrate the effectiveness of both approaches using a simplified model of a multi-phase aluminum alloy design problem, highlighting significant gains in efficiency and solution quality. Together, these methods broaden the applicability of QUBO-based optimization and provide practical tools for data-driven materials discovery and beyond.

cond-mat.mtrl-sci↗

arXiv2023

Assessing the potential of perfect screw dislocations in SiC for solid-state quantum technologies

Although point defects in solids are one of the most promising physical systems to build functioning qubits, it remains challenging to position them in a deterministic array and to integrate them into large networks. By means of advanced ab initio calculations we show that undissociated screw dislocations in cubic 3C-SiC, and their associated strain fields, could be used to create a deterministic pattern of relevant point defects. Specifically, we present a detailed analysis of the formation energies and electronic structure of the divacancy in 3C-SiC when located in the vicinity of this type of dislocations. Our results show that the divacancy is strongly attracted towards specific and equivalent sites inside the core of the screw dislocations, and would form a one-dimensional arrays along them. Furthermore, we show that the same strain that attracts the divacancy allows the modulation of the position of its electronic states and of its charge transition levels. In the case of the neutral divacancy, we find that these modulations result in the loss of its potential as a qubit. However, these same modulations could transform defects with no potential as qubits when located in bulk, into promising defects when located inside the core of the screw dislocations. Since dislocations are still mostly perceived as harmful defects, our findings represent a technological leap as they show that dislocations can be used as active building blocks in future defect-based quantum computers.

cond-mat.mtrl-sci↗

arXiv2022

Survey Propagation: A Resource Allocation Solution for Large Wireless Networks

The ever-increasing number of nodes in current and future wireless communication networks brings unprecedented challenges for the allocation of the available communication resources. This is caused by the combinatorial nature of the resource allocation problems, which limits the performance of state-of-the-art techniques when the network size increases. In this paper, we take a new direction and investigate how methods from statistical physics can be used to address resource allocation problems in large networks. To this aim, we propose a novel model of the wireless network based on a type of disordered physical systems called spin glasses. We show that resource allocation problems have the same structure as the problem of finding specific configurations in spin glasses. Based on this parallel, we investigate the use of the Survey Propagation method from statistical physics in the solution of resource allocation problems in wireless networks. Through numerical simulations we show that the proposed statistical-physics-based resource allocation algorithm is a promising tool for the efficient allocation of communication resources in large wireless communications networks. Given a fixed number of resources, we are able to serve a larger number of nodes, compared to state-of-the-art reference schemes, without introducing more interference into the system

cs.IT↗

arXiv2021

Native defects in monolayer GaS and GaSe: electrical properties and thermodynamic stability

Structural, electronic and thermodynamic properties of native defects in GaS and GaSe monolayers are investigated by means of accurate ab-initio calculations. Based on their charge transition levels we assess the influence of the studied defects on the electrical properties of the monolayers. Specifically, we show that native defects do not behave as shallow dopants and their presence cannot account for the experimentally observed intrinsic doping. In addition, we predict that native defects are efficient compensation and recombination centers. Besides pointing out their detrimental nature, we also calculate the corresponding finite temperature formation energies and provide a window of growth conditions able to reduce the concentration of all relevant native defects.

cond-mat.mtrl-sci↗