The confluence of fundamental symmetries (such as time reversal invariance) and spin-orbit coupling is known to produce electronic states in crystalline solids that are accurately described using the language of topology. We use the synthesis of epitaxial topological quantum materials [1] to raise interesting questions about the manifestations of bulk-boundary correspondence in two cases where topology is interfaced with ferromagnetism: quantized anomalous Hall insulators [2] and non-conserved spin currents in topological insulator/ferromagnet heterostructures [3]. Finally, we discuss the surprising and puzzling discovery of emergent superconductivity at the interface of ferromagnetism and topology [4].

This work is supported by the Penn State Two-Dimensional Crystal Consortium-Materials Innovation Platform (2DCC-MIP) under NSF Grant No. DMR-2039351.

1. Nitin Samarth, "Quantum materials discovery from a synthesis perspective," Nature Materials 16, 1068-1076 (2017).
2. M. Ferguson et al., “Direct visualization of electronic transport in a quantum anomalous Hall insulator,” Nature Materials 22, 1100-1105 (2023).
3. Y. Ou et al., “Spin Hall conductivity in Bi1-xSbx as an experimental test of bulk-boundary correspondence,” arXiv:2311.11933.
4. H. Yi et al., “Interface-induced superconductivity in magnetic topological insulators,” Science 383, 634-639 (2024).

Recent advances in deep learning are triggering a revolution across fields. In this talk, I will discuss how we can apply these techniques to tackle complex problems in cosmology and astrophysics. I will first review how we can improve our understanding of fundamental physics by constraining the value of the cosmological parameters that characterize the laws and constituents of the Universe with the highest accuracy. After reviewing the standard methods used to carry out this task, I will show how deep learning can vastly outperform it. I will then discuss how cosmological structures on small scales contain a wealth of information about the cosmos and how we can retrieve it, taking into account uncertainties from astrophysics phenomena such as feedback from supernovae and active galactic nuclei.

Quantum networks (QN) have experienced rapid advancements in both theoretical and experimental domains over the last decade, making it increasingly important to understand their large-scale features from the viewpoint of statistical physics. In this talk, I will discuss a fundamental question: How can entanglement be effectively and indirectly (e.g., through intermediate nodes) distributed between distant nodes in an imperfect QN, where the connections are subject to noise?

First, I will explain how this question is traditionally understood by mapping to a classical statistical theory---percolation. This mapping gives rise to a critical threshold, indicating the minimum entanglement required per link, that facilitates long-distance entanglement transmission in QN. However, recent investigations have revealed something intriguing: certain network topologies exhibit a lower threshold than what classical percolation predicts. This suggests a possible “quantum improvement” in QN. But does this improvement apply universally across different network topologies? And how does it impact the overall efficiency of QN?

To shed light on these questions, we introduce a new statistical theory, concurrence percolation theory (ConPT), which redefines percolation by transitioning from clusters to entanglement-weighted percolating paths. ConPT reveals substantially lower critical thresholds across general network topologies, along with distinct critical phenomena. This highlights the universal nature of the “quantum improvement” in large-scale QN. Moreover, I will demonstrate how to efficiently determine the ConPT threshold using path-counting algorithms, as well as how to implement the underlying quantum protocols of ConPT on IBM’s quantum computing platform, showcasing the practicability of ConPT.

Quantum computing has long promised to speed up certain tasks that are intractable on classical computers. However, quantum devices are inherently noisy, with coherence times limited by unwanted interactions between the quantum processor and its environment. Overcoming this limitation requires new techniques for recovering useful information from quantum computations despite the noise, as well as new algorithms with enhanced speed and resource efficiency. In this talk, I will describe recent advances on both these fronts. First, I will present a new class of adaptive, quantum-classical hybrid algorithms that outperform previous quantum simulation algorithms in terms of both speed and resources, bringing practical quantum computing on near-term devices closer to reality. Then I will present a new method for characterizing and mitigating correlated noise, a ubiquitous and especially challenging type of noise that evades most existing error mitigation strategies.

Abstract: When we think of a galaxy, we often imagine a spinning disk of stars and gas surrounding a glowing center. But galaxies are more than meets the eye, and the disks we see are just a small part of the whole. We now know galaxies to be surrounded by huge halos of gas that provide the material from which stars are formed. In this lecture, we will explore the scope of these nearly invisible halos, describe how we can detect them, and look at computer simulations that help reveal their lesser known properties and their influences, such as magnetic fields!

Abstract: Ultra Wide Bandgap Semiconductors (UWBS) have been identified as crucial materials for a new generation of power electronics that would enable the future electricity grid. The UWBS materials of AlN, cubic BN and diamond exhibit high carrier mobility and high thermal conductivity which would support high power electronics. Interfaces of different UWBS must encompass the different crystal structure (cubic vs wurtzite), the different chemical bonding (III-V vs group IV), and the interface electric field (polarization and piezoelectric effects).  This talk presents progress on doping of diamond, high current transport in undoped diamond, noise spectroscopy to characterize electrical defects, comparison of polarization and charge transfer for interface doping for transistors, and the challenge of electrical contacts. 

Speaker Bio: Robert Nemanich is Regents’ Professor in the Department of Physics at Arizona State University. He leads the DOE EFRC on ULTRA Materials for a Resilient Smart Electricity Grid.  His research is focused on growth, interfaces and phenomena of diamond and ULTRA materials.

Abstract: Low-dimensional (e.g., atomically thin) continues to gain prominence in applications ranging from electronics to photonics and energy conversion systems. Critical to efficiently developing these systems is the understanding of the fundamental processes related to the dynamics of charge carriers, phonons, and other excitations (i.e. excitons, polaritons). Understanding the principles that govern these excitations will enable the fabrication of optoelectronic and photonic devices with novel and enhanced functionalities. While significant studies of nanomaterials, optical, and electrical transport properties are often made, identifying the mechanisms and timescales governing the interactions between electrons, phonons, and other excitations can be extremely challenging. I will discuss how excited carriers in low-dimensional systems undergo energy relaxation through various dynamical processes that occur over different time scales. Various physical mechanisms such as electron-phonon interactions, phonon-phonon interactions, and carrier recombination are involved in these processes. The electron‒phonon scattering processes are essential to understanding and controlling the energy and charge flow in electronic and energy conversion devices. For this study, we used time-resolved pump-probe spectroscopy with subpicosecond resolution to observe charge carrier dynamics in bilayer graphene.

Bio: Dr. Ioannis Chatzakis earned his Ph.D. in Physics from Kansas State University in Dec. 2009. After completing his coursework, he moved to Columbia University to work on the optical and electronic properties of carbonic materials under the supervision of Prof. Tony F. Heinz. He also holds an M.Sc. degree in Physical Chemistry (Applied Molecular Spectroscopy), and a B.Sc. degree in Electrical Engineering. Before joining TTU, he was an American Society for Engineering Education (ASEE) research fellow residing at the U.S. Naval Research Laboratory (NRL) in Washington, DC. Prior to the NRL appointment, he trained as a postdoctoral researcher at Iowa State University/Ames Laboratory, Stanford University, and the University of Southern California (USC). He is a member of the American Physical Society, Materials Research Society, and Optical Society of America, and he serves the community as a referee in several scientific journals.

The discovery of van der Waals (vdW) materials with intrinsic magnetic order in 2017 has given rise to new avenues for the study of emergent phenomena in two dimensions. In particular, monolayer CrI3 was found to be ferromagnet. Other vdW transition metal halides were later found to have different magnetic properties. How many vdW magnetic materials exist in nature? What are their properties? How do these properties change with the number of layers? A conservative estimate for the number of candidate vdW materials (including monolayers, bilayers and trilayers) exceeds ~106. A recent study showed that artificial intelligence (AI) can be harnessed to discover new vdW Heisenberg ferromagnets based on Cr2Ge2Te6 [1,2]. In this talk, we will harness AI to efficiently explore the large chemical space of vdW transition metal halides and to guide the discovery of magnetic vdW materials with desirable spin properties.That is, we investigate crystal structures based on monolayer Cr2I6 of the form A2X6, which are studied using density functional theory (DFT) calculations and AI. Magnetic properties, such as the magnetic moment, are determined. The formation energy is also calculated and used as a proxy for the chemical stability. We show that AI combined with DFT can provide a computationally efficient means to predict the thermodynamic and magnetic properties of vdW materials [3]. This study paves the way for the rapid discovery of chemically stable magnetic vdW materials with applications in spintronics and data storage.

[1] T. D. Rhone, et al., “Data-driven Studies of Magnetic Two-dimensional Materials,” Scientific Reports 10, 15795 (2020).

[2] Y. Xie, et al., “Data-Driven Studies of the Magnetic Anisotropy of Two-DimensionalMagnetic Materials,” J. Phys. Chem. Lett., 12, 50, 12048–12054 (2021).

[3] T. D. Rhone et al., “Artificial Intelligence Guided Studies of van der Waals Magnets,” Adv. Theory Simulations, 6, 2300019 (2023).

This research was primarily supported by the NSF CAREER, under award number DMR-2044842.

Abstract: Analog and RF circuits have been traditionally designed using continuous-time operation in voltage domain. With the scaling down of transistors and move to the FinFET technology, this is no longer possible without ruining the performance and power consumption. On the other hand, the low supply voltage and sheer switching speed of transistors favor the newly developed time-domain operation where the signal information is contained not in a voltage level but in a time transition timestamp. This talk will give an overview of such recent advancements in the main areas of a communication channel: 1) frequency synthesizer exploiting all-digital PLLs using digital-to-time converters (DTC) and charge-sharing locking techniques; 2) digital transmitters exploiting switched-mode power-amplifier stage even at mm-wave; 3) discrete-time receivers manipulating the signal as charge packets that undergo extensive charge-sharing for filtering and decimation.

Biography R. Bogdan Staszewski received B.Sc. (summa cum laude), M.Sc. and PhD from University of Texas at Dallas, USA, in 1991, 1992 and 2002, respectively. From 1991 to 1995 he was with Alcatel in Richardson, Texas. He joined Texas Instruments in Dallas, Texas in 1995. In 1999 he co-started a Digital RF Processor (DRP) group in TI with a mission to invent new digitally intensive approaches to traditional RF functions. Dr. Staszewski served as a CTO of the DRP group between 2007 and 2009. In July 2009 he joined Delft University of Technology in the Netherlands where he is currently a part-time Full Professor. Since Sept. 2014 he has been a Full Professor at University College Dublin (UCD) in Ireland. He has co-authored seven books, 11 book chapters, and over 160 journal and 220 conference publications, and holds 210 issued US patents. His research interests include nanoscale CMOS architectures and circuits for frequency synthesizers, transmitters and receivers, as well as quantum computers. He is a co-founder of a startup company Equal1 Labs aiming at building the first practical CMOS quantum computer. He is an IEEE Fellow and a recipient of IEEE Circuits and Systems Industrial Pioneer Award (https://ieee-cas.org/society-achievement-award-recipients-list).