Descubriendo el potencial de la computación cuántica

@article{nokey,

title = {Efficient quantum algorithms to find substructures on finite algebras},

author = {Hernández Caceres, J. M. and Fernández Rúa, I. and Fernández-Combarro Álvarez, E. },

url = {https://www.rintonpress.com/xxqic23/qic-23-1516/1275-1290.pdf},

doi = {doi.org/10.26421/QIC23.15-16-2},

isbn = {1533-7146},

year  = {2024},

date = {2024-12-16},

journal = {Quantum Information and Computation },

volume = {23},

number = {15},

abstract = {When classifying a collection of finite algebras (for instance, in the computational classification of finite semifields), an important task is the determination of substructures such as the right, middle, and left nuclei, the nucleus, and the center. Finding these structures may become computationally expensive when there is no additional information about the algebra properties. In this paper, we introduce quantum algorithms than solve this task efficiently, by formulating it as an instance of the Hidden Subgroup Problem (HSP) {over Abelian groups}. We give detailed constructions of the quantum circuits involved in the process and prove that the overall (quantum) complexity of our algorithm is polynomial in the dimension of the algebra, while a similar approach with classical computers would require an exponential number of queries to the HSP function},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Functional quantum abstract detecting systems},

author = {Lugilde, G. and Combarro, E.F. and Rúa, I.F.},

url = {https://link.springer.com/content/pdf/10.1007/s11128-024-04273-5.pdf},

doi = {doi.org/10.1007/s11128-024-04273-5},

isbn = {1570-0755},

year  = {2024},

date = {2024-02-28},

journal = {Quantum Information Processing},

volume = {23},

number = {82},

abstract = {Quantum abstract detecting systems (QADS) provide a common framework to address detection problems in quantum computers. A particular QADS family, that of combinatorial QADS, has been proved to be useful for decision problems on eigenvalues or phase estimation methods. In this paper, we consider functional QADS, which not only have interesting theoretical properties (intrinsic detection ability, relation to the QFT), but also yield improved decision and phase estimation methods, as compared to combinatorial QADS. A first insight into the comparison with other phase estimation methods also shows promising results.},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines},

author = {Schenk,M. and Combarro, E.F. and Grossi, M. and Kain, V. and Shing-Brice, K. and Popa, M.M. and Vallecorsa, S.},

url = {https://iopscience.iop.org/article/10.1088/2058-9565/ad261b},

doi = {10.1088/2058-9565/ad261b},

isbn = {2058-9565},

year  = {2024},

date = {2024-02-21},

urldate = {2024-02-21},

journal = {Quantum Science and Technology},

volume = {9},

abstract = {Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-learning with the restriction, however, to discrete state-action space environments. In this paper, the FERL approach is extended to multi-dimensional continuous state-action space environments to open the doors for a broader range of real-world applications. First, free energy-based Q-learning is studied for discrete action spaces, but continuous state spaces and the impact of experience replay on sample efficiency is assessed. In a second step, a hybrid actor-critic (A-C) scheme for continuous state-action spaces is developed based on the deep deterministic policy gradient algorithm combining a classical actor network with a QBM-based critic. The results obtained with quantum annealing (QA), both simulated and with D-Wave QA hardware, are discussed, and the performance is compared to classical reinforcement learning methods. The environments used throughout represent existing particle accelerator beam lines at the European Organisation for Nuclear Research. Among others, the hybrid A-C agent is evaluated on the actual electron beam line of the Advanced Wakefield Experiment (AWAKE).},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum circuits for computing Hamming distance requiring fewer T gates},

author = {Orts, F. and Ortega, G. and Combarro, E.F. and Rúa, I.F. and Garzón, E.M. },

url = {https://link.springer.com/article/10.1007/s11227-024-05916-1},

doi = {doi.org/10.1007/s11227-024-05916-1},

isbn = {0920-8542},

year  = {2024},

date = {2024-02-16},

urldate = {2024-02-16},

journal = {The Journal of Supercomputing },

volume = {80},

pages = {12527–12542},

abstract = {The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes.},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum algorithms to compute the neighbour list of N-body simulations},

author = {Combarro, E.F. and Rúa, I.F. and Ortega, O.G. and Puertas, A.M. and Garzón, E.M. },

url = {https://link.springer.com/article/10.1007/s11128-023-04245-1},

doi = {doi.org/10.1007/s11128-023-04245-1},

isbn = {1570-0755},

year  = {2024},

date = {2024-02-13},

journal = {Quantum Information Processing},

volume = {23},

number = {61},

abstract = {One of the strategies to reduce the complexity of N-body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Solving the Kemeny ranking aggregation problem with quantum optimization algorithms},

author = {Combarro, E.F. and Pérez-Fernández, R. and Ranilla, J. and De Baets, B. },

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/mma.9489},

doi = {doi.org/10.1002/mma.9489},

isbn = {0170-4214},

year  = {2023},

date = {2023-07-13},

journal = {Mathematical Methods in the Applied Sciences},

volume = {46},

issue = {16},

abstract = {The aim of a ranking aggregation problem is to combine several rankings into a single one that best represents them. A common method for solving this problem is due to Kemeny and selects as the aggregated ranking the one that minimizes the sum of the Kendall distances to the rankings to be aggregated. Unfortunately, the identification of the said ranking—called the Kemeny ranking—is known to be a computationally expensive task. In this paper, we study different ways of computing the Kemeny ranking with quantum optimization algorithms, and in particular, we provide some alternative formulations for the search for the Kemeny ranking as an optimization problem. To the best of our knowledge, this is the first time that this problem is addressed with quantum techniques. We propose four different ways of formulating the problem, one novel to this work. Two different quantum optimization algorithms—Quantum Approximate Optimization Algorithm and Quantum Adiabatic Computing—are used to evaluate each of the different formulations. The experimental results show that the choice of the formulation plays a big role on the performance of the quantum optimization algorithms.},

keywords = {UNIOVI},

pubstate = {published},

tppubtype = {article}

}