Aplicación

@article{nokey,

title = {Nuclear shell-model simulation in digital quantum computers},

author = {Pérez-Obiol, A. and Romero, A. M. and Menéndez, J. and Rios, A. and García-Sáez, A. and Juliá-Díaz, B. },

url = {https://www.nature.com/articles/s41598-023-39263-7},

doi = {doi.org/10.1038/s41598-023-39263-7},

year  = {2023},

date = {2023-07-29},

urldate = {2023-02-07},

journal = {Scientific Reports},

volume = {13},

abstract = {The nuclear shell model is one of the prime many-body methods to study the structure of atomic nuclei, but it is hampered by an exponential scaling on the basis size as the number of particles increases. We present a shell-model quantum circuit design strategy to find nuclear ground states that circumvents this limitation by exploiting an adaptive variational quantum eigensolver algorithm. Our circuit implementation is in excellent agreement with classical shell-model simulations for a dozen of light and medium-mass nuclei, including neon and calcium isotopes. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions. Our strategy also addresses explicitly energy measurements and the required number of circuits to perform them. Our simulated circuits approach the benchmark results exponentially with a polynomial scaling in quantum resources for each nucleus and configuration space. Our work paves the way for quantum computing shell-model studies across the nuclear chart.},

keywords = {algorithms, quantic, quantum computing, simulations},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Multi-dimensional Fourier series with quantum circuits},

author = {Casas, B. and Cervera-Lierta, A.},

url = {https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.062612

Preprint version: https://arxiv.org/abs/2302.03389



},

doi = {10.1103/PhysRevA.107.062612},

year  = {2023},

date = {2023-06-29},

urldate = {2023-06-29},

journal = {Physical Review A},

volume = {107},

issue = {5},

pages = {15},

abstract = {Quantum machine learning is the field that aims to integrate machine learning with quantum computation. In recent years, the field has emerged as an active research area with the potential to bring new insights to classical machine learning problems. One of the challenges in the field is to explore the expressibility of parametrized quantum circuits and their ability to be universal function approximators, as classical neural networks are. Recent works have shown that, with a quantum supervised learning model, we can fit any one-dimensional Fourier series, proving their universality. However, models for multidimensional functions have not been explored in the same level of detail. In this work, we study the expressibility of various types of circuit Ansätze that generate multidimensional Fourier series. We found that, for some Ansätze, the degrees of freedom required for fitting such functions grow faster than the available degrees in the Hilbert space generated by the circuits. For example, single-qudit models have limited power to represent arbitrary multidimensional Fourier series. Despite this, we show that we can enlarge the Hilbert space of the circuit by using more qudits or higher local dimensions to meet the degrees of freedom requirements, thus ensuring the universality of the models.},

keywords = {algorithms, quantic, quantumcircuits, quantumsimulation},

pubstate = {published},

tppubtype = {article}

}