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abstract
Warm dense matter (WDM) is state of matter characterized by temperatures of the order of 10,000 K and nuclear densities of magnitude significantly higher than those found in ordinary condensed matter. WDM is found in many fields of physics, chemistry, planetary sciences, and even industry: stellar and planetary science, laser-induced chemical processes in solids and on surfaces as well as plasma physics.
Nowadays, using intense lasers, WDM properties can be investigated in the laboratory, thus requiring attention to theoretical research for interpretation and understanding of the results. The theoretical description of the regime is complex, being the intermediate between condensed matter physics (i.e., quantum description) and plasma physics (classical thermodynamics). WDM is often described theoretically using finite-temperature Kohn-Sham (KS) density functional theory (DFT) calculations, with reasonably good agreement to experiments. These calculations in finite (non-zero) temperatures are costly due to the large number of fractionally occupied KS orbitals that are involved. The computational cost exhibits cubic scaling with temperature.
Stochastic density functional theory (sDFT), developed recently [1,2,3] appears to be a viable approach to WDM since it is “orbital-free” and yet fully KS. The sDFT approach uses the Fermi Dirac occupation operator to calculate the energy, and finds the electronic density and other expectation values by executing the trace in a stochastic way using random orbitals; in doing so, it skips over the step of calculating the KS orbitals. We further use the stochastic trace formula to calculate the conductivity based on Kubo-Greenwood formula. In the talk, convergence of the free energy will be discussed, as well as calculations of equations of states and statistical convergence of the conductivity when calculated based on stochastic thermal DFT. Transition to metallization in he-h systems was seen in temperature of ~60kK using the stochastic approach.
[1] R. Baer, D. Neuhauser, E. Rabani, Phys. Rev. Lett. 111, 106402 (2013)
[2] Y. Cytter, E. Rabani, D. Neuhauser, and R. Baer Phys. Rev. B 97, 115207 (2018)
[3] Yael Cytter, Eran Rabani, Daniel Neuhauser, Martin Preising, Ronald Redmer, and Roi Baer Phys. Rev. B 100, 195101 (2019)

A quarter of the proteome in every living cell is comprised of helical membrane proteins. Our understanding of how they fold and assemble in vivo remains extremely poor, despite relevance to many diseases. I will describe the surprising finding that, as long a protein remains unfolded, its transmembrane helices may dynamically flip across the membrane. The sequence determinants that regulate helix flipping rates suggest that dynamic helices are a wide-spread feature of unfolded membrane proteins. The implications of this discovery to membrane protein folding and quality controls will be discussed.

The presentation will review two prospective analytic epidemiologic cohort studies of aging in which all participants are organ donors. It will summarize associations of risk factors for common chronic neurologic conditions of aging with an emphasis on Alzheimer’s dementia. It will then summarize the relation of different pathologies and resilience markers assessed at autopsy. Together this will highlight the complexity of neurodegenerative diseases. Next, it will illustrate how multi-level brain omic data can be mined to identify novel therapeutic targets. Finally, it will summarize a strategy to move these targets to a high throughput personalized medicine pipeline for compound screening.

Quantum sensing and metrology exploit quantum aspects of individual and complex systems to measure a physical quantity.
Quantum sensing targets a broad spectrum of physical quantities, of both static and time-dependent types.
While the most important characteristic for static quantities is sensitivity, for time-dependent signals it is the resolution, i.e. the ability to resolve two different frequencies.
The decay time of the probe imposes a fundamental limit on the quantum sensing efficiency. While error correction methods can prolong this time it was not clear if such a procedure could be used
in a quantum sensing protocol. In this talk I will present a study of spectral resolution problems with quantum sensors, and the development of a new super-resolution method that relies on quantum features for which the limitation imposed by the finite decay time can be partially overcome by error correction.

Quantum theory has been incredibly successful at explaining known phenomena and making new predictions that have led to some of the most important scientific and technological breakthroughs in the past century. Quantum computers are arguably the boldest prediction of the theory, but the level of control required to build them is extremely challenging. The requirements for building universal fault tolerant quantum computers (i.e computers that can run any quantum algorithm with high accuracy) are far beyond current capabilities, but less powerful (intermediate) quantum machines are already available, with some accessible online. The minimal requirements for such intermediate machines to significantly outperform ordinary (classical) computers is currently an open area of research. One approach to study the capabilities of intermediate quantum machines, is to study how small subsystems become correlated (and entangled) during a computation. I will provide an overview of work in this direction with some surprising results on the possible role of quantum entanglement. These results provide new insights into quantum theory and quantum technology.