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Pan Gong

Publications and source records attributed to Pan Gong.

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On-chip generation of multi-qubit graph states with high-dimensional encoded single photons

Photonic multi-qubit entanglement is key to optical quantum information processing, particularly universal quantum computing. Yet multi-photon sources suffer from low emission efficiency, making single-photon high-dimensional encoding an appealing alternative. Here we propose an explicit and resource-efficient high-dimensional encoding approach to achieve the target multi-qubit quantum state. The technically challenging preparation of multi-photon quantum states is replaced by single-photon operations involving high-dimensional expansion, routing, and multi-layered quantum measurement. Besides, each photon in the resource multi-photon quantum state can be used to encode multiple qubits in a distributed manner, and a larger entangled state will be constructed. We demonstrate this approach using programmable photonic integrated circuits, where multi-qubit graph states--including the Greenberger-Horne-Zeilinger state and the cluster state--are generated and characterized. We additionally demonstrate the Grover search algorithm using the single-photon cluster state. Our findings unlock a novel route towards diverse entangled state generation with photons and advance large-scale and universal photonic quantum information processing.

quant-ph

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Metallic glasses have attracted considerable interest in recent years due to their unique combination of superb properties and processability. Predicting bulk metallic glass formers from known parameters remains a challenge and the search for new systems is still performed by trial and error. It has been speculated that some sort of "confusion" during crystallization of the crystalline phases competing with glass formation could play a key role. Here, we propose a heuristic descriptor quantifying confusion and demonstrate its validity by detailed experiments on two well-known glass forming alloy systems. With the insight provided by these results, we develop a robust model for predicting glass formation ability based on the spectral decomposition of geometrical and energetic features of crystalline phases calculated ab-initio in the AFLOW high throughput framework. Our findings indicate that the formation of metallic glass phases could be a much more common phenomenon than currently estimated, with more than 17% of binary alloy systems being potential glass formers. Our approach is capable of pinpointing favorable compositions, overcoming a major bottleneck hindering the discovery of new materials. Hence, it is demonstrated that smart descriptors, based solely on the energetics and structure of competing crystalline phases calculated from first-principles and available in online databases, others the sought-after key for accelerated discovery of novel metallic glasses.

cond-mat.mtrl-sci

Predicting Bulk Metallic Glass Forming Ability with the Thermodynamic Density of Competing Crystalline States

Despite two decades of studies, the formation of metallic glasses, very promising systems for industrial applications, still remains mostly unexplained. This lack of knowledge hinders the search for new systems, still performed with combinatorial trial and error. In the past, it was speculated that some sort of "confusion" during crystallization could play a key role during their formation. In this article, we propose a heuristic descriptor quantifying such confusion. It is based on the "thermodynamic density of competing crystalline states", parameterized from high-throughput ab-initio calculations. The existence of highly enthalpy-degenerate but geometrically-different phases frustrates the crystallization process and promotes glass formation. Two test beds are considered. A good and a bad glass-former, CuZr and NiZr, are experimentally characterized with high-throughput synthesis. The experimental results corroborate the capability of the heuristic descriptor in predicting glass forming ability through the compositional space. Our analysis is expected to deepen the understanding of the underlying mechanisms and to accelerate the discovery of novel metallic glasses.

cond-mat.mtrl-sci