- Materia-seminarios

SEMINAR de Grupo de Termodinámica y Computación Cuántica---Daniel Manzano
by Michalis Skotiniotis 29 Sep '23

29 Sep '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: 6 Octubre 2023 Hora: 13:00 pm Lugar: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Ponente: Daniel Manzano (Universidad de Granada) Titulo: An introduction to the Lindblad Master Equation I. Derivation from microscopic principles. Resumen: The theory of open quantum system is one of the most essential tools for the development of quantum technologies. Furthermore, the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) Master Equation plays a key role as it is the most general generator of Markovian dynamics in quantum systems. In this presentation, we present this equation together with its derivation from microscopic principles. The presentation tries to be as self-contained and straightforward as possible to be useful to readers with no previous knowledge of this field. Undergrad, master, and PhD students are encouraged to assist. Reference: https://pubs.aip.org/aip/adv/article/10/2/025106/1021638/A-short-introducti… El seminario es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan Nos vemos a todos allí DJM ________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: 6 October 2023 Time: 13:00 am Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum) Speaker: Daniel Manzano (University of Granada) Title: An introduction to the Lindblad Master Equation I. Derivation from microscopic principles. Abstract: An introduction to the Lindblad Master Equation I. Derivation from microscopic principles. Resumen: The theory of open quantum system is one of the most essential tools for the development of quantum technologies. Furthermore, the Lindblad (or Gorini-Kossakowski-Sudarshan-Lindblad) Master Equation plays a key role as it is the most general generator of Markovian dynamics in quantum systems. In this presentation, we present this equation together with its derivation from microscopic principles. The presentation tries to be as self-contained and straightforward as possible to be useful to readers with no previous knowledge of this field. Undergrad, master, and PhD students are encouraged to assist. Reference: https://pubs.aip.org/aip/adv/article/10/2/025106/1021638/A-short-introducti… The seminar is accessible to undergraduate students, whom we strongly encourage to attend. See you all there DJM. --------------------------------------------------------- Dr. Michalis Skotiniotis Grupo de Termodinámica y Informació Cuántica Departamento de Electromagnetismo y Física de la Materia Facultad de Ciencias Universidad de Granada Email: mskotiniotis(a)onsager.ugr.es ---------------------------------------------------------

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Quantum Thermodynamics and Computation Seminars: Joaquín J. Torres
by Michalis Skotiniotis 10 Jul '23

10 Jul '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: 10 Julio, 2023 Hora: 12:00 am Lugar: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Ponente: Joaquín J. Torres (Departaménto de Electromagnetísmo y Física de la Matéria) Titulo: A biological inspired quantum synapse paradigm Resumen: In this talk, I will review our recent research on the development of quantum neural networks, including biologically inspired dynamic qubits interactions. Our motivation was to build quantum versions of synaptic processes as they occur in real neural systems, that is, to build "quantum synapses" between qubits. In particular, I will report the results on the study of a minimal model of two interacting qubits with an activity-dependent dynamic interaction as in classical dynamic synapses that induce the so-called synaptic depression, that is, synapses that exhibit synaptic fatigue after a strong presynaptic stimulation. Our study shows that such time-dependent interaction induces Rabi oscillations whose frequency decreases when synaptic depression is introduced, thus excitations can be trapped for a long period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be maintained over time by introducing a small energy change between the qubits. We have also shown that long-time entanglement between the two qubits increases naturally in the presence of synaptic depression. Furthermore, I will describe the analysis of a plausible experimental setup of our two-qubit system that demonstrates that these results are robust and achievable experimentally in a laboratory, and I will illustrate preliminary results related to the extension of our system to larger quantum neural networks. Nos vemos a todos allí DJM ________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: 10 July, 2023 Time: 12:00 am Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum) Speaker: Joaquín J. Torres (Department of Electromagnetism and Condensed Matter) Title: A biological inspired quantum synapse paradigm Abstract: In this talk, I will review our recent research on the development of quantum neural networks, including biologically inspired dynamic qubits interactions. Our motivation was to build quantum versions of synaptic processes as they occur in real neural systems, that is, to build "quantum synapses" between qubits. In particular, I will report the results on the study of a minimal model of two interacting qubits with an activity-dependent dynamic interaction as in classical dynamic synapses that induce the so-called synaptic depression, that is, synapses that exhibit synaptic fatigue after a strong presynaptic stimulation. Our study shows that such time-dependent interaction induces Rabi oscillations whose frequency decreases when synaptic depression is introduced, thus excitations can be trapped for a long period of time. This creates a population imbalance between the qubits even though the Hamiltonian is Hermitian. This imbalance can be maintained over time by introducing a small energy change between the qubits. We have also shown that long-time entanglement between the two qubits increases naturally in the presence of synaptic depression. Furthermore, I will describe the analysis of a plausible experimental setup of our two-qubit system that demonstrates that these results are robust and achievable experimentally in a laboratory, and I will illustrate preliminary results related to the extension of our system to larger quantum neural networks. See you all there DJM.

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Quantum Thermodynamics and Computation Seminars: Juani Bermejo Vega
by Michalis Skotiniotis 15 Jun '23

15 Jun '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario, una continuación sobre el tema de la computación cuántica para el aprendizaje automático y la ciencia de datos, por Juani Bermejo Vega Seminario 3 Fecha: 16 Junio, 2023 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: Juani Bermejo Vega (Grupo de Termodinamica y Informació Cuántica) Titulo: Quantum computing for machine learning and data science Resumen: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. El mini curso es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan Nos vemos a todos allí DJM ______________________________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar, continuing on the topic of Quantum computing for machine learning and data science, by Juani Bermejo Vega Seminar 3 Date: 16 Junio, 2023 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: Juani Bermejo Vega (Quantum Thermodynamics and Computation group) Title: Quantum computing for machine learning and data science Abstract: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. This mini course is accessible to undergraduate students, whom we strongly encourage to attend. See you all there DJM.

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Thermodynamics and Quantum Computation Seminars: Quantum Computing for Machine Learning and Data Science
by Michalis Skotiniotis 11 Jun '23

11 Jun '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: 2 Junio, 2023 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: Juani Bermejo Vega (Grupo de Termodinamica y Informació Cuántica) Titulo: Quantum computing for machine learning and data science Resumen: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. El mini curso es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan Nos vemos a todos allí DJM ______________________________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: 2 Junio, 2023 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: Juani Bermejo Vega (Quantum Thermodynamics and Computation group) Title: Quantum computing for machine learning and data science Abstract: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. This mini course is accessible to undergraduate students, whom we strongly encourage to attend. See you all there DJM.

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Thermodynamics and Quantum Computation Seminars: Quantum Computing for Machine Learning and Data Science
by Michalis Skotiniotis 22 May '23

22 May '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario Fecha: 26 Mayo, 2023 Hora: 12:00 am Lugar: Laboratorio de Fïsica Computacional, Dept de Electromagnetismo y Física de al Materia, Planta Baja, Facultad de Ciencias (junto al péndulo gigante) Ponente: Juani Bermejo Vega (Grupo de Termodinamica y Informació Cuántica) Titulo: Quantum computing for machine learning and data science Resumen: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. El mini curso es accesible para los estudiantes de grado, a los que recomendamos encarecidamente que asistan Nos vemos a todos allí DJM ______________________________________________________________________________________________________ Dear all The Quantum Thermodynamics and Computation group cordially invites you to the following seminar Date: 26 Mayo, 2023 Time: 12:00 am Location: Computational Physics Laboratory, Department of Electromagnetism and Condensed Mater, Ground Floor, Faculty of Science (next to the giant pendulum) Speaker: Juani Bermejo Vega (Quantum Thermodynamics and Computation group) Title: Quantum computing for machine learning and data science Abstract: Quantum computing is an emerging research field at the intersection of physics, computer science and applied mathematics. Quantum computers are devices that exploit coherent quantum phenomena to speed-up calculations. Quantum machine learning is a subfield of quantum computing that investigates the application of quantum computers algorithms for machine learning problems. This course will give an elementary introduction to quantum machine learning, and review a few examples of quantum algorithms for classification and clustering problems. In our first meeting, we will give a general introduction, and review quantum algorithms for solving linear systems of equations, Grover search and simulating sparse Hamiltonian dynamics. This mini course is accessible to undergraduate students, whom we strongly encourage to attend. See you all there DJM.

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Seminar: Why Quantum?
by Michalis Skotiniotis 08 May '23

08 May '23

El grupo de Termodinámica y Computación Cuántica les invita al siguiente seminario. Ponente: Yelena Guryanova. IQOQI Vienna. Título: Why quantum? Resumen: When we look at the results of experiments in the lab, why is it that they all seem to obey the postulates of quantum mechanics? Could there be some underlying principle, (or a set of principles) that singles out quantum theory as an ’island in theory space’? Is quantum theory special because it obeys special relativity? Or perhaps because we need to send a non-trivial amount of information to communicate? Could one devise an ‘almost quantum’ theory that satisfies similar requirements? In this talk we’ll look at information-theoretic research, historic and present-day, to see what progress has been made into understanding why the world seems to be quantum. Fecha y hora: Miercoles 10 de Mayo 2023, a las 11:00-12:00. Lugar: Facultad de ciencias. Departamento de Electromagnetismo y Física Materia. Laboratorio de Física Computacional. Michalis Skotiniotis

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Thermodynamics and Quantum Computation Seminars: Quantum Statistical Inference
by Michalis Skotiniotis 02 May '23

02 May '23

Hola tod@s. Este Viernes tenemos la segunda parte de nuestra serie sobre Inferencía estatística: la parte concerniente el uso de phenomenas cuànticas. Como la última vez el seminario será impartido por Michalis Skotiniotis (titulo y resumen a continuación) a las 12:00 en el Laboratorio de Física Computacional situada en el departamento de Electromagnetismo y Física Materia (planta baja circa de péndulo gigante). La charla será accesible para todos, por lo que se recomienda encarecidamente a los estudiantes de pregrado que se unan. La información, así como un enlace a las notas cubiertas en ambas conferencias, se pueden encontrar aquí <https://ic1.ugr.es/eventos/wp/qjc/>. Nos vemos a todos allí DJM —————————————————————————————————————————————————————————————————————————————— Dear all This Friday we have the second part of our series on Statistical Inference: the part concerning the use of quantum phenomena. As last time, the seminar will be given by Michalis Skotiniotis (title and summary below) at 12:00 in the Computational Physics Laboratory located in the Electromagnetism and Matter Physics department (ground floor around giant pendulum). The talk will be accessible to everyone, so undergraduate students are strongly encouraged to join. The information, as well as a link to the notes covered in both conferences, can be found here <https://ic1.ugr.es/eventos/wp/qjc/>. See you all there DJM. —————————————————————————————————————————————————————————————————————————————— Title: Quantum Statistical Inference—Hypothesis Testing and Parameter Estimation Speaker: Michalis Skotiniotis Abstract: In this second lecture we will revisit the Hypothesis testing and parameter estimation scenarios discussed in the previous lecture, except we shall be invoking quantum theory. In the case of quantum Hypothesis testing I shall review Holevo's optimality theorem which establishes the conditions for achieving the maximum probability of success in a hypothesis testing scenario involving quantum states and measurements. We shall also cast these conditions in terms of semi-definite programs and apply them to obtain the optimal measurement (aka the Helstrom measurement) for the case of binary hypothesis testing. We will then consider quantum parameter estimation, review the quantum version of the Cramér-Rao bound and the Quantum Fisher Information (QFI). These concepts will be applied to the simple case of interferometry where we will encounter the Standard and Heisenberg limits in estimation precision.

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Fwd: [qipc.spain] QTYR 2023 Workshop: Quantum Technologies for Young Researchers (Madrid, 4th-7th July 2023)
by Michalis Skotiniotis 27 Apr '23

27 Apr '23

FYI > Begin forwarded message: > > From: PYSQT organization <pysqt.organization(a)gmail.com> > Subject: [qipc.spain] QTYR 2023 Workshop: Quantum Technologies for Young Researchers (Madrid, 4th-7th July 2023) > Date: 27 April 2023 at 12:12:38 CEST > To: qipc.spain(a)listas.csic.es > Reply-To: PYSQT organization <pysqt.organization(a)gmail.com> > > Dear Colleagues, > We are excited to invite you to the Quantum Technologies for Young Researchers (QTYR) workshop taking place in Madrid (Spain) from 4th-7th July. The event is organized by the PhD and Young Scientists in Quantum Technologies Network (PYSQT) and falls under the umbrella of the UIMP Master's degree in Quantum Technologies participated by CSIC and various universities. > This workshop is targeted to scientists in the early stages of their career working in different branches of Quantum Science. The program will include keynote speeches, contributed talks and poster presentations, as well as ample opportunities for networking and collaboration. > We have assembled a distinguished group of speakers who will cover a wide range of topics, including quantum computing, quantum information, quantum sensing, and quantum hardware, among others. The confirmed speakers for the keynote sessions are: > - Alba Cervera Lierta > - Mari Carmen Bañuls > - Jorge Casanova > - Alicia Gómez > In addition to the keynote and contributed sessions, the program includes a panel discussion on the bridges between academia and companies in quantum technologies, as well as a quantum computing hands-on tutorial. > We encourage you to register and submit your abstract at https://www.benasque.org/2023qtyr/ <https://urldefense.com/v3/__https://www.benasque.org/2023qtyr/__;!!D9dNQwwG…>. This event promises to be a great opportunity for young researchers to share their work and expand their knowledge of the field. > Deadline for registration: July 1st > Deadline for abstract submission: May 22nd > Webpage: https://www.benasque.org/2023qtyr/ <https://urldefense.com/v3/__https://www.benasque.org/2023qtyr/__;!!D9dNQwwG…> > Info: pysqt.organization(a)gmail.com <mailto:pysqt.organization@gmail.com> > Share this email with your colleagues who may be interested in coming. We look forward to seeing you! > Best regards, > QTYR workshop organization

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Thermodynamics and Quantum Computation Seminars: Classical Statistical Inference
by Michalis Skotiniotis 17 Apr '23

17 Apr '23

Hola tod@s. Este Viernes tenemos la primera reunión de los seminarios del Grupo de Termodinámica y Computació Cuántica. Este primer seminario será impartido por Michalis Skotiniotis (titulo y resumen a continuación) a las 12:00 en el Laboratorio de Física Computacional situada en el departamento de Electromagnetismo y Física Materia (planta baja circa de péndulo gigante). La charla será accesible para todos, por lo que se recomienda encarecidamente a los estudiantes de pregrado que se unan. Para facilitar la comunicación, también hemos revivido la pagina web del club de revistas cuánticas que se puede encontrar aquí <https://ic1.ugr.es/eventos/wp/qjc/>. Marque la pagina web como favorito, ya que lo usaremos para cargar anuncios de seminarios, así como material de audio, visual, y textual para los seminarios. Nos vemos a todos allí DJM —————————————————————————————————————————————————————————————————————————————— Dear all. This Friday we will have thee first seminar of the Quantum Thermodynamics and Computation Group. The seminar will be given by Michalis Skotiniotis (title and abstract below) at 12:00 in the Computational Physics laboratory in the Department of Electromagnetism and Material Physics (ground floor next to the giant pendulum). The level of the seminar will be such so as to be accessible to all, particularly students of the undergrad degree interested in the topic whom we strongly encourage to attend. To facilitate future communication, we have also revived the webpage of the quantum journal club which can be found here <https://ic1.ugr.es/eventos/wp/qjc/>. Please bookmark this webpage as we will use it to announce future seminars, as well as upload audio, visual, and textual material pertaining to the seminars. See you all there DJM. —————————————————————————————————————————————————————————————————————————————— Title: Classical Statistical Inference—Hypothesis Testing and Parameter Estimation Speaker: Michalis Skotiniotis Abstract: Statistical inference is an important branch of decision theory and deals with taking optimal decisions in the face of uncertainty. For instance, the decision of whether a new drug combats a given disease or not is taken based on samples taken from medical trials and forms the primary example of classical hypothesis testing. On the other hand, determining the precision in the estimation of a certain physical constant—such as the value of the gravitational constant g—is an example of parameter estimation. In this talk we will go through both these tasks from the point of view of statistical decision theory. In the case of hypothesis testing we will derive the optimal decision rule for minimizing the probability of erroneously identifying the likely hypothesis, as well as several variants of symmetric and asymmetric hypothesis testing (the famous Neyman-Pearson theorem). For parameter estimation we will derive the celebrated Cramér-Rao theorem which sets a lower bound on the precision of any unbiased estimator, and we will connect this to the traditional error propagation formula we all know and love (or hate as the case may be).

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Recordatorio: Seminarios de computación cuántica y Tgemroduynamics
by Michalis Skotiniotis 30 Mar '23

30 Mar '23

Estimado/a Un gentil recordatorio de la primera reunión de los seminarios del grupo de Computación Cuántica y Termodinámica que tendrá lugar mañana a las 12:00 en el Laboratorio de Computación del grupo de Electromagnetismo y Física Materia (planta baja justo al lado del péndulo grande). Nos vemos todos allí. Dani Juani Michalis

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