Physics

Gravitational-wave observatories have established a new field of transient astronomy. The most recent LIGO-Virgo-KAGRA catalog, GWTC-3, identified 90 merging binaries, which range from a double neutron star with a total mass of 2.7 at 40 Mpc (GW170817) to a double black hole with a total mass of 150 at 5.3 Gpc (GW190521). These observations have many connections to nuclear astrophysics. They are revealing the remnants of stellar evolution and supernovae in merging binary systems, they are constraining event rates and astrophysical environments for heavy-element nucleosynthesis, and they are illuminating the dense matter dynamics inside the cores of merging neutron stars. Here, I will describe the imprint of dense matter on gravitational waves, the implications of existing observations for nuclear physics, and some prospects for the coming years including the science potential of proposed next-generation observatories like Cosmic Explorer.

---

Zoom link https://pitp.zoom.us/j/99015121355?pwd=NStOc2srbEJXdW9aSTJJbDk4RWZhdz09

Isometric tensor networks (isoTNS) form a subclass of tensor network states that have an additional isometric condition, which implies that they can be efficiently prepared with a linear-depth quantum circuit. In this work, we introduce a procedure to construct isoTNS encoding of certain 2D classical partition functions. By continuously tuning a parameter in the isoTNS, the many-body ground state undergoes quantum phase transitions, exhibiting distinct 2D topological order. We illustrate this by constructing an isoTNS path with bond dimension $D = 2$ interpolating between distinct symmetry-enriched topological (SET) phases. At the transition point, the isoTNS wavefunction is related to a gapless point in the classical six-vertex model. Furthermore, the critical wavefunction supports a power-law correlation along one spatial direction while remains long-range ordered in the other spatial direction. We provide an exact linear-depth parametrized local quantum circuit that realizes the path. The above features can therefore be efficiently realized on a programmable quantum device. In the second part of my talk, I will show how to discover efficiently measurable order parameters for quantum phases using model-independent training of quantum circuit classifiers. The possibility of the efficient realization of phase transition path is useful for benchmarking quantum phase recognition methods in higher than one dimension.

---

Zoom link https://pitp.zoom.us/j/93183360141?pwd=RVdYeUxUbE1aZ1dUbzRSL3lBb0lHZz09

In this presentation, we review historical and modern results on the linear response of dynamical systems to external perturbations and the celebrated relationship between fluctuations and dissipation expressed by the Fluctuation-Dissipation Theorem (FDT). The conceptual derivation of FDT originated from the definition of the equilibrium state and Onsager’s regression hypothesis. Over time the fluctuation-dissipation relation was vividly investigated for systems away from equilibrium, frequently exhibiting wild fluctuations of measured parameters. In this talk, we recall the major formulations of the FDT, such as those proposed by Langevin, Onsager, and Kubo [1]. We discuss as well the importance of fluctuations in a wide class of growth phenomena. We point out challenges and opportunities where the relationship breaks down and cannot be adapted.[1] F. A. Oliveira, R. M. S. Ferreira, L. C. Lapas and M.H. Vainstein, Review article Anomalous Diffusion: A Basic Mechanism for the Evolution of Inhomogeneous Systems Frontiers in Physics, 7, (18) (2019).  

Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the transfer of theoretical and algorithmic ideas. We provide a simplifying perspective onto convolutions through tensor networks (TNs) which allow reasoning about the underlying tensor multiplications by drawing diagrams, and manipulating them to perform function transformations and sub-tensor access. We demonstrate this expressive power by deriving the diagrams of various autodiff operations and popular approximations of second-order information with full hyper-parameter support, batching, channel groups, and generalization to arbitrary convolution dimensions. Further, we provide convolution-specific transformations based on the connectivity pattern which allow to re-wire and simplify diagrams before evaluation. Finally, we probe computational performance, relying on established machinery for efficient TN contraction. Our TN implementation speeds up a recently-proposed KFAC variant up to 4.5x and enables new hardware-efficient tensor dropout for approximate backpropagation.

---

Zoom link https://pitp.zoom.us/j/99090845943?pwd=NHBNVTNnbDNSOGNSVzNGS21xcllFdz09

Abstract TBA

---

Zoom link https://pitp.zoom.us/j/98123499206?pwd=QWlHcFBJWE8zRTlyT1A5WUVsQS9NUT09