Talks

The gauge color code is a quantum error-correcting code with local syndrome measurements that, remarkably, admits a universal transversal gate set without the need for resource-intensive magic state distillation. A result of recent interest, proposed by Bombin, shows that the subsystem structure of the gauge color code admits an error-correction protocol that achieves tolerance to noisy measurements without the need for repeated measurements, so called single-shot error correction. Here, we demonstrate the promise of single-shot error correction by designing a two-part decoder and investigate its performance. We simulate fault-tolerant error correction with the gauge color code by repeatedly applying our proposed error-correction protocol to deal with errors that occur continuously to the underlying physical qubits of the code over the duration that quantum information is stored. We estimate a sustainable error rate, i.e. the threshold for the long time limit, of ~0.31% for a phenomenological noise model using a simple decoding algorithm.

We analyse different classical formulations of General Relativity in the Batalin (Frad-
kin) Vilkovisky framework with boundary, as a first step in the program of CMR [1] quantisation. Success and failure in satisfying the axioms will allow us to discriminate among the different descriptions, suggesting that some are more suitable than others in view of perturbative quantisation. Based on a joint work with A. Cattaneo [2, 3] we will present the details of the application of the BV-BFV formalism to the Einstein-Hilbert and Palatini-Holst formulations of General Relativity. We show that the two descriptions are no longer equivalent from this point of view, and we discuss possible interpretations of this result.

[1] A. S. Cattaneo, P. Mnëv, N. Reshetikhin, Classical BV theories on manifolds with boundary, Comm. Math. Phys. 332, 2, 535-603 (2014); A.S. Cattaneo, P. Mnëv, N. Reshetikhin, Perturbative quantum gauge theories on manifolds with boundary, arXiv:1507.01221
[2] A. S. Cattaneo, M. Schiavina, BV-BFV analysis of General Relativity. Part I: Einstein Hilbert action, arXiv:1509.05762 (2015).
[3] A. S. Cattaneo, M. Schiavina, BV-BFV analysis of General Relativity. Part II: Palatini Holst action, in preparation; A. S. Cattaneo, M. Schiavina, On time, in preparation.

We study the representation theory of truncated shifted Yangians. These algebras arise as quantizations of slices to Schubert varieties in the affine Grassmannian. We will describe the combinatorics of their highest weights, which is encoded in Nakajima's monomial crystal. We also prove Hikita's conjecture in this context.

Plasma-filled magnetospheres can extract energy from a spinning black hole and provide the power source for a variety of observed astrophysical phenomena. These magnetospheres are described by the highly nonlinear equations of force-free electrodynamics. Typically these equations can only be solved numerically, but they become amenable to analytic solution in the extremal limit when the black hole achieves maximal angular momentum and an infinite-dimensional conformal symmetry emerges in the high-redshift region near its horizon. This constitutes an example of critical behavior in astronomy. We use the near-horizon scaling symmetry of the extreme Kerr metric to determine universal properties of physics near rapidly spinning black holes.

In this talk I address the problem of simultaneously inferring unknown quantum states and unknown quantum measurements from empirical data. This task goes beyond state tomography because we are not assuming anything about the measurement devices. I am going to talk about the time and sample complexity of the inference of states and measurements, and I am going to talk about the robustness of the minimal Hilbert space dimension. Moreover, I will describe a simple heuristic algorithm (alternating optimization) to fit states and measurements to empirical data. For this algorithm the dataset does not need to be quantum. Hence, the proposed algorithm enables us to interpret general datasets from a quantum perspective. By analyzing movie ratings, we demonstrate the power of quantum models in the context of item recommendation which is a key discipline in machine learning. We observe that quantum models can compete with state-of-the-art algorithms for item recommendation. Based on joint work with Aram Harrow. Relevant preprints: arXiv:1412.7437 and arXiv:1510.02800.