Talks

We show that there are two distinct obstacles to an efficient
classical simulation of a general quantum circuit. The first obstacle, which
has been well-studied, is the difficulty of efficiently estimating the
probability associated with a particular measurement outcome. We show that
this alone does not determine whether a quantum circuit can be efficiently
simulated. The second obstacle is that, in general, there can be an
exponential number of `relevant' outcomes that are needed for an accurate
simulation, and so efficient simulation may not be possible even in
situations where the probabilities of individual outcomes can be efficiently
estimated. We show that these two obstacles are distinct, overcoming the
former obstacle being necessary but not sufficient for simulability whilst
overcoming the pair is jointly sufficient for simulability. Specifically, we
prove that a family of quantum circuits is efficiently simulable if it
satisfies two properties: one related to the efficiency of Born rule
probability estimation, and the other related to the sparsity of the outcome
distribution. We then prove a pair of hardness results (using standard
complexity assumptions and a variant of a commonly-used average case
hardness conjecture), where we identify families of quantum circuits that
satisfy one property but not the other, and for which efficient simulation
is not possible. To prove our results, we consider a notion of simulation of
quantum circuits that we call epsilon-simulation. This notion is less
stringent than exact sampling and is now in common use in quantum hardness
proofs.

Most machine learning algorithms involve optimizing a single set of parameters to decrease a single cost function. In adversarial machine learning, two or more "players" each adapt their own parameters to decrease their own cost, in competition with the other players. In some adversarial machine learning algorithms, the algorithm designer contrives this competition between two machine learning models in order to produce a beneficial side effect. For example, the generative adversarial networks framework involves a contrived conflict between a generator network and a discriminator network that results in the generator learning to produce realistic data samples. In other contexts, adversarial machine learning models a real conflict, for example, between spam detectors and spammers. In general, moving machine learning from optimization and a single cost to game theory and multiple costs has led to new insights in many application areas.

I will review recent and ongoing developments in path integral optimization and path integral complexity in 2d CFTs. After, I will discuss the connection with geometric approach to circuit complexity and point out several open problems.

Interacting electrons in high magnetic fields exhibit rich physical phenomena including the gapped fractional quantum Hall effects and the gapless states. The composite Fermi liquids (CFLs) are gapless states that can occur at even denominator Landau level fillings. Due to the celebrated work of Halperin, Lee and Read (94), the CFLs were understood as Fermi liquids of composite fermions, which are bound states of electron and electromagnetic flux quanta. However, at 1/2 filling, it is not obvious why the HLR description is consistent with the particle hole symmetry. Motivated by this, recently Son (15) proposed an alternative description for CFLs at 1/2, according to which the composite fermions are instead emergent Dirac fermions. Importantly, Son’s theory predicts a Pi Berry curvature singularity at the composite Fermi sea center. In the first part of this talk [2,3], I will present our numerical work about detecting this Z2 Berry phase at 1/2 filling. In the second part [1], I will present how and why Dirac fermions can emerge at all the other filling fractions (1/2m and 1-1/2m when m is integer) even without the particle hole symmetry.

[1] arXiv 1808.07529. JW.

[2] arXiv 1711.07864. Geraedts, JW, Rezayi, Haldane.

[3] arXiv 1710.09729. JW, Geraedts, Rezayi, Haldane.

Anomaly cancellation conditions place strong constraints on many physical theories. In the traditional framework, local and global anomalies are detected by computing an eta invariant of a certain Dirac operator on a mapping torus. Recent research has uncovered the existence of finer anomaly cancellation conditions, not visible in these traditional settings. I will review the traditional and refined anomalies, and apply them to various symmetries of interest in particle physics. Examples include the (various global forms of) the Standard Model, GUT's, and discrete gauge symmetries of physical interest such as proton triality and a certain Z4 symmetry in the (MS)SM.

The Twin Higgs model is an attractive solution to the little Hierarchy problem with top partners that are neutral under SM gauge charges. The framework is consistent with the null result of LHC colored top partner searches while offering many alternative discovery channels. Depending on model details, the phenomenology looks very different: either spectacular long-lived particle signals at colliders, or a plethora of unusual cosmological and astrophysical signatures via the existence of a predictive hidden sector. I will examine the latter possibility, and describe how the asymmetrically reheated Mirror Twin Higgs provides a predictive framework for a highly motivated and highly non-trivial interacting dark sector, with correlated signals in the CMB, Large Scale Structure, and direct detection searches, as well as higgs precision measurements at colliders. This provides a vivid example of the collider-cosmology complementarity, and motivates a variety of new astrophysical searches that are motivated by the hierarchy problem.