Séminaires Quantiques IVADO Automne 2025

IVADO propose une nouvelle série de séminaires qui débute à l’automne 2025.

Ces séminaires quantiques IVADO ont pour objectif de stimuler la collaboration entre les communautés de recherche en physique quantique et en intelligence artificielle. Ils s’inscrivent dans le cadre de l’Alliance en Algorithmique Quantique dont IVADO est membre.

Ces séminaires auront généralement lieu les vendredis peu après midi.

Ils se tiennent en présence et un lunch est offert.

On demande aux participantes et participants de s’inscrire et d’indiquer leur choix de repas.

Nick Ormrod (Perimeter Institute)

Résumé :

I introduce a modern perspective on decoherence informed by quantum causal modelling, situate it in its historical context, and show how it resolves two long-standing problems in traditional approaches.

Decoherence is often told as a story about states, focused on the suppression of off-diagonal terms in a density matrix through correlation with an environment. Yet this sits uneasily with a key observation already in Zurek (1981): the unitary dynamics alone determine the preferred basis. Recent advances in quantum causal modelling enable a genuinely dynamics-first account: define decoherence directly in terms of causal influence, formalized as noncommutation relations that specify which generators can affect which observables. On this view, diagonal density matrices, correlations, and other state-level features are mere symptoms of decoherence, while decoherence itself is a property of the unitary dynamics.

A major payoff of this causal account is that, rather than designating one piece of the universe as the “system” and the rest as the “environment,” one can treat decoherence more democratically, allowing different systems to serve as environments for each other. In turn, this democratic perspective yields a unique consistent set of histories for any subset of unitarily interacting subsystems, and thus addressing the critique of consistent histories by Dowker and Kent (1994) by separating physically meaningful histories from uninformative ones.

Lieu : Campus MIL, A.5502.1

Matthias Christandl (Institut for Matematiske Fag, University of Copenhaguen)

Résumé : Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide range of biological phenomena from how cells send and receive signals to how pharmaceutical compounds can be used to treat diseases. Quantitative and predictive free energy calculations require computational models that accurately capture both the varied and intricate electronic interactions between molecules as well as the entropic contributions from motions of these molecules and their aqueous environment. However, accurate quantum-mechanical energies and forces can only be obtained for small atomistic models, not for large biomacromolecules. Here, we demonstrate how to consistently link accurate quantum-mechanical data obtained for substructures to the overall potential energy of biomolecular complexes by machine learning in an integrated algorithm. We do so using a two-fold quantum embedding strategy where the innermost quantum cores are treated at a very high level of accuracy. We demonstrate the viability of this approach for the molecular recognition of a ruthenium-based anticancer drug by its protein target, applying traditional quantum chemical methods. As such methods scale unfavorable with system size, we analyze requirements for quantum computers to provide highly accurate energies that impact the resulting free energies. Once the requirements are met, our computational pipeline FreeQuantum is able to make efficient use of the quantum computed energies, thereby enabling quantum computing enhanced modeling of biochemical processes. This approach combines the exponential speedups of quantum computers for simulating interacting electrons with modern classical simulation techniques that incorporate machine learning to model large molecules.

Lieu : Campus MIL, A.5502.1

Mio Murao (School of Science, University of Tokyo)

Résumé : à venir

Lieu : Campus MIL, A.5502.1

Stepan Fomichev (Xanadu)

Résumé : Truly convincing killer applications of quantum computers — ones that combine usefulness and feasibility — are still missing from the quantum ecosystem. In this talk, I will describe the guiding principles that the algorithms team at Xanadu is employing in the search for such applications. I will then review our progress, with a specific focus on applications in quantum chemistry and materials science. The talk will conclude with an overview of fruitful directions and open questions we believe are key in the search for killer applications.

Lieu : Campus MIL, A.5502.1

Sergey Bravyi (IBM T.J. Watson Research Center)

Résumé : We consider the problem of simulating dynamics of classical nonlinear dissipative systems with N degrees of freedom. To make the problem tractable for quantum computers, we add a weak Gaussian noise to the equation of motion and to the initial state. Our main result is an end-to-end quantum algorithm for simulating the noisy dynamics of nonlinear systems satisfying certain sparsity and divergence-free conditions. For any constant nonzero noise rate, the quantum runtime scales polynomially with log(N), evolution time, inverse error tolerance, and the relative strength of nonlinearity and dissipation. Our main technical tool is the Kolmogorov partial differential equation describing time evolution of scalar functions of solutions averaged over noise.

Lieu : Campus MIL, A.1502.1

Conférencier à venir

Résumé : à venir

Lieu : Campus MIL, A.5502.1

David Gosset (Institute for Quantum Computing, U. of Waterloo)

Résumé : à venir

Lieu : à venir