| Socio | BBVA |
| Título | Multiobjective variational quantum optimization for constrained problems: an application to cash handling |
| Autores | Pablo Díez-Valle, Jorge Luis-Hita, Senaida Hernández-Santana, Fernando Martínez-García, Álvaro Díaz-Fernández, Eva Andrés, Juan José García-Ripoll, Escolástico Sánchez-Martínez and Diego Porras |
| Revista | Quantum Science and Technology |
| Año | 2023 |
| Volumen | 8 |
| Número | 4 |
| Páginas | 45009 |
| DOI | 10.1088/2058-9565/ace474 |
| Enlace | https://dx.doi.org/10.1088/2058-9565/ace474 |
| Resumen Científico | Combinatorial optimization problems are ubiquitous in industry. In addition to finding a solution with minimum cost, problems of high relevance involve a number of constraints that the solution must satisfy. Variational quantum algorithms (VQAs) have emerged as promising candidates for solving these problems in the noisy intermediate-scale quantum stage. However, the constraints are often complex enough to make their efficient mapping to quantum hardware difficult or even infeasible. An alternative standard approach is to transform the optimization problem to include these constraints as penalty terms, but this method involves additional hyperparameters and does not ensure that the constraints are satisfied due to the existence of local minima. In this paper, we introduce a new method for solving combinatorial optimization problems with challenging constraints using VQAs. We propose the multi-objective variational constrained optimizer (MOVCO) to classically update the variational parameters by a multiobjective optimization performed by a genetic algorithm. This optimization allows the algorithm to progressively sample only states within the in-constraints space, while optimizing the energy of these states. We test our proposal on a real-world problem with great relevance in finance: the cash handling problem. We introduce a novel mathematical formulation for this problem, and compare the performance of MOVCO versus a penalty based optimization. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima that do not satisfy any of the mandatory constraints. |
| Socio | GMV |
| Título | Quantum Optimization Methods for Satellite Mission Planning |
| Autores | Antón Makarov, Carlos Pérez-Herradón, Giacomo Franceschetto, Márcio M. Taddei, Eneko Osaba, Paloma del Barrio Cabello, Esther Villar-Rodriguez and Izaskun Oregi |
| Revista | IEEE Access |
| Año | 2024 |
| Volumen | 12 |
| Páginas | 71808-71820 |
| DOI | 10.1109/ACCESS.2024.3402990 |
| Enlace | https://ieeexplore.ieee.org/document/10534762 |
| Resumen Científico | Satellite mission planning for Earth observation satellites is a combinatorial optimization problem that consists of selecting the optimal subset of imaging requests, subject to constraints, to be fulfilled during an orbit pass of a satellite. The ever-growing amount of satellites in orbit underscores the need to operate them efficiently, which requires solving many instances of the problem in short periods of time. However, current classical algorithms often fail to find the global optimum or take too long to execute. Here, we approach the problem from a quantum computing point of view, which offers a promising alternative that could lead to significant improvements in solution quality or execution speed in the future. To this end, we study a planning problem with a variety of intricate constraints and discuss methods to encode them for quantum computers. Additionally, we experimentally assess the performance of quantum annealing and the quantum approximate optimization algorithm on a realistic and diverse dataset. Our results identify key aspects like graph connectivity and constraint structure that influence the performance of the methods. We explore the limits of today’s quantum algorithms and hardware, providing bounds on the problems that can be currently solved successfully and showing how the solution degrades as the complexity grows. This work aims to serve as a baseline for further research in the field and establish realistic expectations on current quantum optimization capabilities. |
| Socio | GMV |
| Título | Satellite image classification with neural quantum kernels |
| Autores | Pablo Rodriguez-Grasa, Robert Farzan-Rodriguez, Gabriele Novelli, Yue Ban and Mikel Sanz |
| Revista | Machine Learning: Science and Technology, |
| Año | 2024 |
| Volumen | 6 |
| Páginas | 17 |
| DOI | 10.1088/2632-2153/ada86 |
| Enlace | https://iopscience.iop.org/article/10.1088/2632-2153/ada86c |
| Resumen Científico | A practical application of quantum machine learning in real-world scenarios in the short term remains elusive, despite significant theoretical efforts. Image classification, a common task for classical models, has been used to benchmark quantum algorithms with simple datasets, but only few studies have tackled complex real-data classification challenges. In this work, we address such a gap by focusing on the classification of satellite images, a task of particular interest to the earth observation (EO) industry. We first preprocess the selected intrincate dataset by reducing its dimensionality. Subsequently, we employ neural quantum kernels (NQKs)- embedding quantum kernels (EQKs) constructed from trained quantum neural networks (QNNs)- to classify images which include solar panels. We explore both 1-to-n and n-to-n NQKs. In the former, parameters from a single-qubit QNN’s training construct an n-qubit EQK achieving a mean test accuracy over 86% with three features. In the latter, we iteratively train an n-qubit QNN to ensure scalability, using the resultant architecture to directly form an n-qubit EQK. In this case, a test accuracy over 88% is obtained for three features and 8 qubits. Additionally, we show that the results are robust against a suboptimal training of the QNN. |
| Socio | GMV |
| Título | Bayesian Parameterized Quantum Circuit Optimization (BPQCO): a task and hardware-dependent approach |
| Autores | Alexander Benítez-Buenache, Queralt Portell-Montserrat |
| Revista | Quantum Machine Intelligence |
| Año | 2025 |
| Volumen | 7 |
| Páginas | 19 |
| DOI | 10.1007/s42484-025-00308-x |
| Enlace | https://link.springer.com/article/10.1007/s42484-025-00308-x |
| Resumen Científico | Variational quantum algorithms (VQAs) have emerged as a promising quantum alternative for solving optimization and machine learning problems using parameterized quantum circuits (PQCs). The design of these circuits influences the ability of the algorithm to efficiently explore the solution space and converge to more optimal solutions. Quantum architecture search (QAS) is therefore a highly relevant topic, in which the selection of an appropriate circuit topology, gate set, and parameterization scheme is determinant to achieve good performance. In addition, it is not only problem-dependent, but the quantum hardware used also has a significant impact on the results. Therefore, we present BPQCO, a Bayesian optimization-based strategy to search for optimal PQCs adapted to the problem to be solved and to the characteristics and limitations of the chosen quantum hardware. To this end, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems (a synthetic dataset and the well-known Iris dataset), focusing on the design of the circuit ansatz. In addition, we study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers. To mitigate the effect of noise, two alternative optimization strategies based on the characteristics of the quantum system are proposed. The results obtained confirm the relevance of the presented approach and allow its adoption in further work based on the use of PQCs. |