Search Talks

While the standard $\Lambda$CDM model provides an astonishing fit to cosmological data, there is a 4-6 $\sigma$ tension in the Hubble constant, $H_0$, between the value inferred from early-Universe observables, Planck CMB anisotropy spectra, and the value determined by local measurements from SH0ES collaboration using Type 1a supernovae calibrated with Cepheid variables. As an effort to solve this tension, we develop a method of searching for data-driven solutions to the Hubble tension given cosmological dataset, based on the standard Fisher bias formalism. Taking as proof of principle the case of a time-varying electron mass, and focusing first on Planck CMB data, we demonstrate a modified recombination can solve the Hubble tension and lower $S_8$ to better match weak lensing measurements. Once baryonic acoustic oscillation and uncalibrated supernovae data are included, it is not possible to fully solve the tension with perturbative modifications to recombination. The method we develop in this work can be a useful tool to search for many other possible extensions to the $\Lambda$CDM model, and is expected to inspire a model-building effort from the cosmology and particle physics community.

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In this work, drawing inspiration from the type of noise present in real hardware, we study the output distribution of random quantum circuits under practical non-unital noise sources with constant noise rates. We show that even in the presence of unital sources like the depolarizing channel, the distribution, under the combined noise
channel, never resembles a maximally entropic distribution at any depth. To show this, we prove that the output distribution of such circuits never anticoncentrates — meaning it is never too "flat" — regardless of the depth of the circuit. This is in stark contrast to the behavior of noiseless random quantum circuits or those with only unital noise, both
of which anticoncentrate at sufficiently large depths. As consequences, our results have interesting algorithmic implications on both the hardness and easiness of noisy random circuit sampling, since anticoncentration is a critical property exploited by both state-of-the-art classical hardness and easiness results. This talk is based on arXiv:2306.16659.

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