April 2024 - NumPI - Mailing list — Dipartimento di Matematica, UniPI

Next week's seminar on quantum Computing : Today at 14:00 Lindsay Bassman Oftelie
by Gianna M. Del Corso 22 Apr '24

22 Apr '24

Dear all, this message is a reminder for today's quantum series seminar. Title: Dynamic Cooling of Qubits on Contemporary Quantum Computers Speaker: Lindsay Bassman Oftelie (NEST-CNR) Venue: Sala Polifunzionale, Ed. C, Polo Fibonacci (Dip. Informatica) Time: Monday, 22/04/2024, 14:00 Abstract: Quantum computers require qubits to be initialized in a pure (i.e., cold) state for successful computation. Dynamic cooling offers a route to effectively lower qubit temperatures beyond what is possible with direct, physical cooling techniques. It works by cooling a subset of qubits, at the expense of heating others, by applying certain logic gates to the entire system. While it was initially dismissed as impractical for the high-temperature NMR-based quantum computers available at the time of its inception, we show how dynamic cooling is substantially more effective and efficient on the low-temperature quantum computers available today. In this talk, we will examine how optimal dynamic cooling scales with total system size, in terms of the minimal achievable final temperature, the work cost, and the complexity of the associated quantum circuits. We will observe the effect of hardware noise on cooling and we will propose a sub-optimal dynamic cooling scheme with fixed (low) complexity to improve the feasibility of implementation on noisy quantum hardware. Here the link for the online attendance https://teams.microsoft.com/l/meetup-join/19%3ameeting_YzgwYmY4NDMtZWUwZi00… <https://teams.microsoft.com/l/meetup-join/19:meeting_YzgwYmY4NDMtZWUwZi00Mz…> See you there. Gianna Del Corso @@@@@@@@@@@@@@@@@ Gianna M. Del Corso, PhD Dipartimento di Informatica, Università di Pisa Largo Pontecorvo, 3 56127 Pisa, Italy ph. +39-050-2213118 @@@@@@@@@@@@@@@@@

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Next week's seminar on quantum Computing : Lindasy Bassman Oftelie
by Gianna M. Del Corso 16 Apr '24

16 Apr '24

Dear all, As part of the CN1-SPOKE 10 on Quantum Computing, we are organizing a weekly seminar series. Next week’s seminar is: Title: Dynamic Cooling of Qubits on Contemporary Quantum Computers Speaker: Lindsay Bassman Oftelie (NEST-CNR) Venue: Sala Polifunzionale, Ed. C, Polo Fibonacci (Dip. Informatica) Time: Monday, 22/04/2024, 14:00 Abstract: Quantum computers require qubits to be initialized in a pure (i.e., cold) state for successful computation. Dynamic cooling offers a route to effectively lower qubit temperatures beyond what is possible with direct, physical cooling techniques. It works by cooling a subset of qubits, at the expense of heating others, by applying certain logic gates to the entire system. While it was initially dismissed as impractical for the high-temperature NMR-based quantum computers available at the time of its inception, we show how dynamic cooling is substantially more effective and efficient on the low-temperature quantum computers available today. In this talk, we will examine how optimal dynamic cooling scales with total system size, in terms of the minimal achievable final temperature, the work cost, and the complexity of the associated quantum circuits. We will observe the effect of hardware noise on cooling and we will propose a sub-optimal dynamic cooling scheme with fixed (low) complexity to improve the feasibility of implementation on noisy quantum hardware. Everyone is welcome! Please, note the change of venue and time. ======================================================================= @@@@@@@@@@@@@@@@@ Gianna M. Del Corso, PhD Dipartimento di Informatica, Università di Pisa Largo Pontecorvo, 3 56127 Pisa, Italy ph. +39-050-2213118 @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@ Gianna M. Del Corso, PhD Dipartimento di Informatica, Università di Pisa Largo Pontecorvo, 3 56127 Pisa, Italy ph. +39-050-2213118 @@@@@@@@@@@@@@@@@

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[Indico] Ivan Bioli, Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations, 11/04/2024, 16:00 Europe/Rome
by fabio.durastante＠unipi.it 10 Apr '24

10 Apr '24

Title: Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations, Speaker(s): Ivan Bioli, Università degli Studi di Pavia, Date and time: 11 Apr 2024, 16:00 (Europe/Rome), Lecture series: Seminar on Numerical Analysis, Venue: Dipartimento di Matematica (Aula Seminari). You can access the full event here: https://events.dm.unipi.it/e/268 Abstract -------- We focus on the development and analysis of Riemannian optimization methods for solving multiterm linear matrix equations, when the associated linear operator is symmetric positive definite. Assuming low-rank approximability of the solution, we employ first- and second-order Riemannian optimization algorithms on low-rank matrix manifolds. To enhance competitiveness with existing solvers on realistic problems, significant efforts are dedicated to developing efficient Riemannian preconditioners, which are interpreted as changing the Riemannian metric. By alternating between fixed-rank Riemannian optimization and rank updates, we devise a rank-adaptive algorithm that does not require fixing the rank a priori. In numerical experiments, we illustrate the effectiveness of the developed Riemannian preconditioners, comparing our approach with established solvers such as Conjugate Gradient with truncation. Results indicate that the Riemannian optimization approach, in both its fixed-rank and rank-adaptive variants, is competitive with other solvers and can avoid excessive growth of ranks in intermediate iterations. References [Bou23] Nicolas Boumal. An Introduction to Optimization on Smooth Manifolds. 1st ed. Cambridge University Press, Mar. 16, 2023. [KSV16] Daniel Kressner, Michael Steinlechner, and Bart Vandereycken. “Preconditioned Low-rank Riemannian Optimization for Linear Systems with Tensor Product Structure”. In: SIAM Journal on Scientific Computing 38.4 (Jan. 2016), A2018–A2044. [VV10] Bart Vandereycken and Stefan Vandewalle. “A Riemannian Optimization Ap proach for Computing Low-Rank Solutions of Lyapunov Equations”. In: SIAM Journal on Matrix Analysis and Applications 31.5 (Jan. 2010), pp. 2553–2579. -- Indico :: Email Notifier https://events.dm.unipi.it/e/268

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Next week's seminar on quantum Computing : Leonardo Banchi
by Gianna M. Del Corso 05 Apr '24

05 Apr '24

Dear all, As part of the CN1-SPOKE 10 on Quantum Computing, we are organizing a weekly seminar series. Next week’s seminar is: Title: Statistical Complexity of Quantum Learning Speaker: Leonardo Banchi (Università degli Studi di Firenze) Venue: Room 250, Ed. C, Polo Fibonacci (Physics Department, first floor) Time: Monday, 08/04/2024, 17:00 Abstract: Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via quantum computing. As in classical learning, quantum learning problems involve settings in which the mechanism generating the data is unknown, and the main goal of a learning algorithm is to ensure satisfactory accuracy levels when only given access to data and, possibly, side information such as expert knowledge. I will introduce the complexity of quantum learning using information-theoretic techniques by focusing on data complexity, copy complexity, and model complexity. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. For example, in a quantum system, unlike in classical machine learning, it is generally not possible to evaluate the training loss simultaneously on multiple hypotheses using the same quantum data. To make the seminar approachable by different research communities, I will provide extensive background material on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, we highlight the differences between quantum and classical learning by addressing both supervised and unsupervised learning, and discuss potential advantages. Reference: arXiv:2309.11617 - Adv. Quant. Tech 2024 - in press Everyone is welcome! @@@@@@@@@@@@@@@@@ Gianna M. Del Corso, PhD Dipartimento di Informatica, Università di Pisa Largo Pontecorvo, 3 56127 Pisa, Italy ph. +39-050-2213118 @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@@@@@ Gianna M. Del Corso, PhD Dipartimento di Informatica, Università di Pisa Largo Pontecorvo, 3 56127 Pisa, Italy ph. +39-050-2213118 @@@@@@@@@@@@@@@@@

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[Indico] Ivan Bioli, Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations, 11/04/2024, 16:00 Europe/Rome
by fabio.durastante＠unipi.it 04 Apr '24

04 Apr '24

Title: Preconditioned Low-Rank Riemannian Optimization for Multiterm Linear Matrix Equations, Speaker(s): Ivan Bioli, École Polytechnique Fédérale de Lausanne, Date and time: 11 Apr 2024, 16:00 (Europe/Rome), Lecture series: Seminar on Numerical Analysis, Venue: Dipartimento di Matematica (Aula Seminari). You can access the full event here: https://events.dm.unipi.it/e/268 Abstract -------- We focus on the development and analysis of Riemannian optimization methods for solving multiterm linear matrix equations, when the associated linear operator is symmetric positive definite. Assuming low-rank approximability of the solution, we employ first- and second-order Riemannian optimization algorithms on low-rank matrix manifolds. To enhance competitiveness with existing solvers on realistic problems, significant efforts are dedicated to developing efficient Riemannian preconditioners, which are interpreted as changing the Riemannian metric. By alternating between fixed-rank Riemannian optimization and rank updates, we devise a rank-adaptive algorithm that does not require fixing the rank a priori. In numerical experiments, we illustrate the effectiveness of the developed Riemannian preconditioners, comparing our approach with established solvers such as Conjugate Gradient with truncation. Results indicate that the Riemannian optimization approach, in both its fixed-rank and rank-adaptive variants, is competitive with other solvers and can avoid excessive growth of ranks in intermediate iterations. References [Bou23] Nicolas Boumal. An Introduction to Optimization on Smooth Manifolds. 1st ed. Cambridge University Press, Mar. 16, 2023. [KSV16] Daniel Kressner, Michael Steinlechner, and Bart Vandereycken. “Preconditioned Low-rank Riemannian Optimization for Linear Systems with Tensor Product Structure”. In: SIAM Journal on Scientific Computing 38.4 (Jan. 2016), A2018–A2044. [VV10] Bart Vandereycken and Stefan Vandewalle. “A Riemannian Optimization Ap proach for Computing Low-Rank Solutions of Lyapunov Equations”. In: SIAM Journal on Matrix Analysis and Applications 31.5 (Jan. 2010), pp. 2553–2579. -- Indico :: Email Notifier https://events.dm.unipi.it/e/268

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