Talks

We define a map from an arbitrary quantum circuit to a local Hamiltonian whose ground state encodes the quantum computation. All previous maps relied on the Feynman-Kitaev construction, which introduces an ancillary ‘clock register’ to track the computational steps. Our construction, on the other hand, relies on injective tensor networks with associated parent Hamiltonians, avoiding the introduction of a clock register. This comes at the cost of the ground state containing only a noisy version of the quantum computation, with independent stochastic noise. We can remedy this - making our construction robust - by using quantum fault tolerance. In addition to the stochastic noise, we show that any state with energy density exponentially small in the circuit depth encodes a noisy version of the quantum computation with adversarial noise. We also show that any ‘combinatorial state’ with energy density polynomially small in depth encodes the quantum computation with adversarial noise. This serves as evidence that any state with energy density polynomially small in depth has a similar property. As an application, we give a new proof of the QMA-completeness of the local Hamiltonian problem (with logarithmic locality) and show that contracting injective tensor networks to additive error is BQP- hard. We also discuss the implication of our construction to the quantum PCP conjecture, combining with an observation that QMA verification can be done in logarithmic depth. Based on joint work with Anurag Anshu and Nikolas P. Breuckmann. (https://arxiv.org/abs/2309.16475)

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It is well known that the study and observation of core collapse Supernovae (SN) provide powerful tools to probe possible scenarios of physics beyond the Standard Model (SM). After reviewing the basics beyond the mechanism of a SN explosion and how these observations can constraint models of new physics, I will focus on two novel ideas to exploit the already available data from past SN and the observation of a future, long due, galactic one. Firstly. I will discuss constraints, from the cooling on the SN, on the interactions of SM particles with an hypothetical dark sector, leading to bounds on the mediators of such interactions competitive with collider ones. Then, I will turn on the constrains on the emission of axion-like particles (ALPs) that can convert into photons in an external magnetic field, leading to a gamma-ray signal. I will discuss the possibility that ALPs can convert to gamma-rays in the stellar magnetic fields of the progenitor stars. Applying this concept to gamma-ray data from SN1987A leads to the strongest constraints on axion-like particles for masses within a few orders of magnitude of 10^-5 eV. The implications for a future galactic blue supergiant supernova will be discussed.

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In this talk, I will present a novel framework for modeling the weak lensing source galaxy redshift distribution and galaxy intrinsic alignment via a shared luminosity function. In the context of LSST Year 1 and Year 10 cosmic shear analysis, I demonstrate the significant impact of the luminosity function on source galaxy redshift distributions and intrinsic alignment contamination. I establish the influence of Schechter luminosity function parameters on the redshift distribution of a magnitude-limited sample and show the effects of marginalizing over these parameters in intrinsic alignment modeling. I forecast how this joint modeling approach affects cosmological parameter constraints, finding that it yields constraints comparable to standard analyses while mitigating potential biases from incorrectly fixed luminosity function parameters. I highlight the specific impact of the luminosity function shape on the cosmic shear data vector and discuss the method's potential for modeling generic selection functions and extending to a 3x2pt analysis with galaxy bias incorporation. While focused on LSST cosmic shear, this framework is broadly applicable to weak lensing surveys.

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