September 2024 - Materia-seminarios

Erratum. Seminar today!! Entanglement detection using Quantum Support Vector Machines
by Daniel Manzano 26 Sep '24

26 Sep '24

Reminder: The Quantum Thermodynamics and Quantum Computation Group invites you to assist to this talk. Date: Thursday 26/9/2024 Time: 11:00 am Place: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Speaker: Ana Martínez. University of Granada. Title: Entanglement detection using Quantum Support Vector Machines Abstract: The quantum separability problem addresses the detection of quantum entanglement, a fundamental feature of quantum mechanics, meaning that we want to determine if a certain quantum state is entangled or separable. This problem has been shown to be NP-hard (1). Our starting point will be the works from Refs (2) and (3), in which the problem is taken as a binary classification task that is approached with classical machine learning models. In this work, we propose the application of quantum machine learning, in particular quantum support vector machines (QSVM), to the quantum separability problem. By studying and optimizing various encoding circuits, the quantum SVM shows comparable discrimination power to other traditional classical kernels. (1) Gurvits L. Classical deterministic complexity of Edmonds’Problem and quantum entanglement (2) Casalé B, Di Molfetta G, Anthoine S, Kadri H. Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens. (3) Ureña J, Sojo A, Bermejo J, Manzano D. Entanglement detection with classical deep neural networks. For those of you unable to attend the seminar, we have set-up the following google meet link for the upcoming seminar. https://meet.google.com/csb-snng-bmu We would also like to bring to your attention the groups seminar page https://ic1.ugr.es/eventos/wp/qjc/ where you can find relevant material and information about past, present, and future seminars. See you all there DJM. --------------------------------------------------------------------------------- Daniel Manzano Quantum Thermodynamics and Quantum Computation Group University of Granada Facultad de Ciencias, Av. Fuentenueva s/n Granada 18071, Spain Phone: +34 958241000 Ext: 20569 https://ic1.ugr.es/members/dmanzano/

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Reminder: Entanglement detection using Quantum Support Vector Machines
by Daniel Manzano 26 Sep '24

26 Sep '24

Reminder: The Quantum Thermodynamics and Quantum Computation Group invites you to assist to this talk. Date: Thursday 27/9/2024 Time: 11:00 am Place: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Speaker: Ana Martínez. University of Granada. Title: Entanglement detection using Quantum Support Vector Machines Abstract: The quantum separability problem addresses the detection of quantum entanglement, a fundamental feature of quantum mechanics, meaning that we want to determine if a certain quantum state is entangled or separable. This problem has been shown to be NP-hard (1). Our starting point will be the works from Refs (2) and (3), in which the problem is taken as a binary classification task that is approached with classical machine learning models. In this work, we propose the application of quantum machine learning, in particular quantum support vector machines (QSVM), to the quantum separability problem. By studying and optimizing various encoding circuits, the quantum SVM shows comparable discrimination power to other traditional classical kernels. (1) Gurvits L. Classical deterministic complexity of Edmonds’Problem and quantum entanglement (2) Casalé B, Di Molfetta G, Anthoine S, Kadri H. Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens. (3) Ureña J, Sojo A, Bermejo J, Manzano D. Entanglement detection with classical deep neural networks. For those of you unable to attend the seminar, we have set-up the following google meet link for the upcoming seminar. https://meet.google.com/csb-snng-bmu We would also like to bring to your attention the groups seminar page https://ic1.ugr.es/eventos/wp/qjc/ where you can find relevant material and information about past, present, and future seminars. See you all there DJM. --------------------------------------------------------------------------------- Daniel Manzano Quantum Thermodynamics and Quantum Computation Group University of Granada Facultad de Ciencias, Av. Fuentenueva s/n Granada 18071, Spain Phone: +34 958241000 Ext: 20569 https://ic1.ugr.es/members/dmanzano/

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Seminar: Entanglement detection using Quantum Support Vector Machines
by Daniel Manzano 24 Sep '24

24 Sep '24

The Quantum Thermodynamics and Quantum Computation Group invites you to assist to this talk. Date: Thursday 27/9/2024 Time: 11:00 am Place: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Speaker: Ana Martínez. University of Granada. Title: Entanglement detection using Quantum Support Vector Machines Abstract: The quantum separability problem addresses the detection of quantum entanglement, a fundamental feature of quantum mechanics, meaning that we want to determine if a certain quantum state is entangled or separable. This problem has been shown to be NP-hard (1). Our starting point will be the works from Refs (2) and (3), in which the problem is taken as a binary classification task that is approached with classical machine learning models. In this work, we propose the application of quantum machine learning, in particular quantum support vector machines (QSVM), to the quantum separability problem. By studying and optimizing various encoding circuits, the quantum SVM shows comparable discrimination power to other traditional classical kernels. (1) Gurvits L. Classical deterministic complexity of Edmonds’Problem and quantum entanglement (2) Casalé B, Di Molfetta G, Anthoine S, Kadri H. Large-Scale Quantum Separability Through a Reproducible Machine Learning Lens. (3) Ureña J, Sojo A, Bermejo J, Manzano D. Entanglement detection with classical deep neural networks. For those of you unable to attend the seminar, we have set-up the following google meet link for the upcoming seminar. https://meet.google.com/csb-snng-bmu We would also like to bring to your attention the groups seminar page https://ic1.ugr.es/eventos/wp/qjc/ where you can find relevant material and information about past, present, and future seminars. See you all there DJM. --------------------------------------------------------------------------------- Daniel Manzano Quantum Thermodynamics and Quantum Computation Group University of Granada Facultad de Ciencias, Av. Fuentenueva s/n Granada 18071, Spain Phone: +34 958241000 Ext: 20569 https://ic1.ugr.es/members/dmanzano/

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SEMINAR: Paula García-Molina (Instituto de Física Fundamental - CSIC)
by Michalis Skotiniotis 23 Sep '24

23 Sep '24

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: Viernes 27/9/2024 Hora: 11:00 am Lugar: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Ponente: Paula García-Molina (Instituto de Física Fundamental - CSIC) Titulo: Pseudospectral method for solving PDEs using Matrix Product States (arXiv:2409.02916) Resumen: This research focuses on solving time-dependent partial differential equations (PDEs), in particular the time-dependent Schrödinger equation, using matrix product states (MPS). We propose an extension of Hermite Distributed Approximating Functionals (HDAF) to MPS, a highly accurate pseudospectral method for approximating functions of derivatives. Integrating HDAF into an MPS finite precision algebra, we test four types of quantum-inspired algorithms for time evolution: explicit Runge-Kutta methods, Crank-Nicolson method, explicitly restarted Arnoli iteration and split-step. The benchmark problem is the expansion of a particle in a quantum quench, characterized by a rapid increase in space requirements, where HDAF surpasses traditional finite difference methods in accuracy with a comparable cost. Moreover, the efficient HDAF approximation to the free propagator avoids the need for Fourier transforms in split-step methods, significantly enhancing their performance with an improved balance in cost and accuracy. Both approaches exhibit similar error scaling and run times compared to FFT vector methods; however, MPS offer an exponential advantage in memory, overcoming vector limitations to enable larger discretizations and expansions. Finally, the MPS HDAF split-step method successfully reproduces the physical behavior of a particle expansion in a double-well potential, demonstrating viability for actual research scenarios. El seminario es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan. Para aquellos que no puedan asistir, también hemos configurado una reunión de Google donde el seminario se transmitirá en vivo. https://meet.google.com/csb-snng-bmu También os recordamos a todos la página web del Seminario de Grupos, https://ic1.ugr.es/eventos/wp/qjc/ donde se publicarán anuncios pasados, presentes y futuros. Nos vemos a todos allí DJM ________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: Friday 27/9/2024 Time: 11:00 am Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum) Speaker: Paula García-Molina (Instituto de Física Fundamental - CSIC) Title: Pseudospectral method for solving PDEs using Matrix Product States (arXiv:2409.02916) Abstract: This research focuses on solving time-dependent partial differential equations (PDEs), in particular the time-dependent Schrödinger equation, using matrix product states (MPS). We propose an extension of Hermite Distributed Approximating Functionals (HDAF) to MPS, a highly accurate pseudospectral method for approximating functions of derivatives. Integrating HDAF into an MPS finite precision algebra, we test four types of quantum-inspired algorithms for time evolution: explicit Runge-Kutta methods, Crank-Nicolson method, explicitly restarted Arnoli iteration and split-step. The benchmark problem is the expansion of a particle in a quantum quench, characterized by a rapid increase in space requirements, where HDAF surpasses traditional finite difference methods in accuracy with a comparable cost. Moreover, the efficient HDAF approximation to the free propagator avoids the need for Fourier transforms in split-step methods, significantly enhancing their performance with an improved balance in cost and accuracy. Both approaches exhibit similar error scaling and run times compared to FFT vector methods; however, MPS offer an exponential advantage in memory, overcoming vector limitations to enable larger discretizations and expansions. Finally, the MPS HDAF split-step method successfully reproduces the physical behavior of a particle expansion in a double-well potential, demonstrating viability for actual research scenarios. The seminar is accessible to undergraduate students, whom we strongly encourage to attend. For those of you unable to attend the seminar, we have set-up the following google meet link for the upcoming seminar. https://meet.google.com/csb-snng-bmu We would also like to bring to your attention the groups seminar page https://ic1.ugr.es/eventos/wp/qjc/ where you can find relevant material and information about past, present, and future seminars. See you all there DJM. --------------------------------------------------------- Dr. Michalis Skotiniotis Grupo de Termodinámica y Información Cuántica. Departamento de Electromagnetismo y Física de la Materia. Instituto Carlos I de Física Teórica y Computacional. Facultad de Ciencias, Av. Fuentenueva s/n Universidad de Granada Granada, 18071 Spain Email: mskotiniotis(a)onsager.ugr.es ---------------------------------------------------------

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