Chen, Hongxiang | Engineering Papers

DOE OSTI2022

Topological Crystalline Insulator Candidate ErAsS with Hourglass Fermion and Magnetic-Tuned Topological Phase Transition

Topological crystalline insulators (TCIs) with hourglass fermion surface state have attracted a lot of attention and are further enriched by crystalline symmetries and magnetic order. Here, the emergence of hourglass fermion surface state and exotic phases in the newly discovered, air-stable ErAsS single crystals are shown. In the paramagnetic phase, ErAsS is expected to be a TCI with hourglass fermion surface state protected by the nonsymmorphic symmetry. Dirac-cone-like bands and nearly linear dispersions in large energy range are experimentally observed, consistent well with theoretical calculations. Below T N ≈ 3.27 K, ErAsS enters a collinear antiferromagnetic state, which is a trivial insulator breaking the time-reversal symmetry. An intermediate incommensurate magnetic state appears in a narrow temperature range (3.27–3.65 K), exhibiting an abrupt change in magnetic coupling. Overall, the results reveal that ErAsS is an experimentally available TCI candidate and provide a unique platform to understand the formation of hourglass fermion surface state and explore magnetic-tuned topological phase transitions.

36 MATERIALS SCIENCE↗

DOE OSTI2020

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.

Broughton, Michael↗