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Next week's seminars on quantum Computing : Monday 6 and Friday 10
by Gianna M. Del Corso 02 May '24

02 May '24

Dear All, As part of the CN1-SPOKE 10 on Quantum Computing, we are organizing a weekly seminar series. This week, exceptionally we will have two seminars: Title: The effect of phase noise in superconducting qubit control Speaker: Agata Barsotti (Università di Pisa) Venue: room 250, first floor Ed. C, Polo Fibonacci (Physics Dept.) Time: Monday, 06/05/2024, 17:00 At the end of the same week there is: Title: Certified randomness from single-particle entanglement Speaker: Sonia Mazzucchi (Università di Trento) Venue: Sala Gerace, Ed. C Polo Fibonacci (Computer Science Dept.) Time: Friday, 10/05/2024, 16:00 The abstracts of both the seminars are attached. Everyone is welcome! 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 : 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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Next seminar on Quantum Computing : Simone Montangero Monday 25 h.16:00
by Gianna M. Del Corso 21 Mar '24

21 Mar '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: Platforms and methods for quantum computing Speaker: Simone Montangero (University of Padova) Venue: Sala Gerace, Dipartimento di Informatica Time: Monday, 25/03/2024, 16:00 Abstract: We present an overview of the current status of the quantum computing landscape, presenting some fo the most developed hardware platforms together with a selection of recent results and methods currently driving the technological development of this potentially disruptive technology. Everyone is welcome! PS. Note that the seminar is at 4:00PM and not at 5Pm as usual ============================================================================== @@@@@@@@@@@@@@@@@ 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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Seminario CN1-SPOKE 10 (Quantum Computing) today
by Gianna M. Del Corso 11 Mar '24

11 Mar '24

Dear All, As part of the CN1-SPOKE 10 on Quantum Computing, we are organizing a weekly seminar series. This week’s seminar is: Title: Hybrid Variational Algorithms for Classical and Quantum Complex Systems Speaker: Elisa Ercolessi (University of Bologna) Venue: Sala Galilei, room 131 ground floor, Bldg C (Dipartimento di Fisica) Time: Monday, 11/03/2024, 17:00 Abstract: Quantum Computing is seen as a potential breakthrough for the study of hard classical problems as well as for quantum many body systems. However, we are in the era of NISQ devices and still far away from fault-tolerant machines. This leads us to consider the possibility of hybrid classical-quantum protocols of variational type: they exploit quantum resources to efficiently prepare states that depend on a suitable chosen set of variational parameters, which can then be determined by means of optimization algorithms to be run on a classical computer. The choice of such classical optimizer schemes is to be guided by compatibility requirements with respect to current available quantum platforms. To evaluate the feasibility of such an approach, we present different applications of the Quantum Approximate Optimization Algorithm to both classical and quantum systems implemented by means of emulations of the full protocol on a classical hardware as well as a case study that has been run on a real Rydberg atom quantum machine. Everyone is welcome! Gianna @@@@@@@@@@@@@@@@@ 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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seminar by Puya Latafat on Tue 27
by Giancarlo Bigi 26 Feb '24

26 Feb '24

You are very welcome to attend the seminar below Best wishes Giancarlo ============ _/Time/_: Tuesday 27 February, 11:00 am _/Place/_: Sala Seminari Est, Dipartimento di Informatica /_Speaker_/: Puya Latafat, KU Leuven /_Title_: / Convergence of operator splitting methods for nonmonotone inclusions /_Abstract_/ Despite their popularity, convergence results of splitting methods have been largely limited to the convex/monotone setting. We present convergence results for several first-order methods, including the proximal point method, forward-backward-forward splitting, and Douglas-Rachford splitting, within a nonmonotone setting. To this end, we focus on a problem class characterized by an oblique weak Minty condition, which captures non-trivial structures as we demonstrate with examples. Moreover, we introduce the concept of semimonotonicity and provide sufficient conditions for the global convergence of splitting techniques for the sum of two semimonotone operators. Illustrative examples demonstrate the wide range of problems our theory is able to cover.

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26/02/2024 - Seminario CN1-SPOKE 10 (Quantum Computing)
by Paola Boito 23 Feb '24

23 Feb '24

Dear all, I am forwarding this seminar announcement on quantum computing, which may be of interest to some of you. Best regards, Paola Boito ============================================================================== Dear All, As part of the CN1-SPOKE 10 on Quantum Computing, we are organizing a* weekly* seminar series. This week’s seminar is: Title: *Quantum Software Engineering* Speakers: *Giuseppe Bisicchia and Antonio Brogi * Venue:* Sala Gerace, Dipartimento di Informatica* Time:* Monday, 26/02/2024, 17:00 * Everyone is welcome! ==============================================================================

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