- Quantum Processors and Computing Systems
Design and implementation of quantum processors, quantum memory, and quantum computing systems.
- Quantum Algorithms
Fast quantum and quantum-classical hybrid algorithms.
- Simulations of Quantum Physics
Classical and quantum simulations for quantum physics.
The Alibaba Cloud Quantum Development Platform (AC-QDP) aims to utilize Alibaba’s massive computational resources to aid the development of quantum applications and quantum computers themselves. Currently,
AC-QDP uses Tai-Zhang, a classical simulator of quantum circuits, as its computational engine. The design and performance of Tai-Zhang are documented in arXiv:1805.01450 and arXiv:1907.11217. A demonstration of AC-QDP for assisting the design and testing of quantum algorithms is reported in arXiv:1909.02559. Further demonstrations and improvements will be reported in the near future.
- Tai Zhang
Tai Zhang is a classical simulator of quantum computation,or more generally, a tensor contractor. It leverages Alibaba's powerful distributed computing system for the simulation of intermediate size quantum circuits.Tai-Zhang is becoming the computation engine for Alibaba's tensor-based,quantum-inspired classical computation framework. It is part of a development toolset for both physical implementations and applications of quantum computation.
Yaoyun received his Bachelor's degree from Peking University and his Ph.D. from Princeton University,both in Computer Science. After a postdoctoral scholarship at Caltech's Institute for Quantum Information, he joined the faculty of University of Michigan, Ann Arbor, holding the positions of Assistant, Associate,and Full Professor of Electrical Engineering and Computer Science. He has made contributions to a diverse set of topics in theoretical quantum information science,including quantum computational complexity, classical simulation of quantum systems,and quantum cryptography. At Alibaba, he is building an interdisciplinary and international team to realize the revolutionary potentials of quantum computation.
Mario received his Ph.D. in Computer Science from the University of Chicago. He worked at Bell Laboratories and Princeton's Institute for Advanced Studies before becoming a Professor of Computer Science at Rutgers University. His research areas include computational complexity theory and quantum computing. He was awarded the Gödel Prize both in 2001 and 2005,for his work on probabilistic-ally checkable proofs and on the space complexity of approximating frequency moments in streamed data.
- Shilin Huang, Jianxin Chen, Youning Li, Bei Zeng. Quantum state tomography for generic pure states. arXiv:1711.10878
- Connor Paddock, Jianxin Chen. A Characterization of Antidegradable Qubit Channels.arXiv:1712.03399.
- Cupjin Huang, Michael Newman, Mario Szegedy. Explicit lower bounds on strong quantum simulation. arXiv:1804.10368.
- Jianxin Chen, Fang Zhang, Cupjin Huang, Michael Newman, Yaoyun Shi. Classical Simulation of Intermediate-Size Quantum Circuits. arXiv:1805.01450.
- Cupjin Huang, Michael Newman, Mario Szegedy. Explicit lower bounds on strong simulation of quantum circuits in terms of T-gate count. arXiv:1902.04764.
- Fang Zhang, Jianxin Chen. Optimizing T gates in Clifford+T circuit as rotations around Paulis. arXiv:1903.12456.
- Bannink, Tom; Buhrman, Harry; Gilyén, András; Szegedy, Mario. The Interaction Light Cone of the Discrete Bak-Sneppen, Contact and other local processes. arxiv:1903.12607
- Fang Zhang, Cupjin Huang, Michael Newman, Junjie Cai, Huanjun Yu, Zhengxiong Tian, Bo Yuan, Haihong Xu, Junyin Wu, Xun Gao, Jianxin Chen, Mario Szegedy, Yaoyun Shi. Alibaba cloud quantum development kit: Large-scale classical simulation of quantum circuits. arXiv:1907.11217
- Cupjin Huang, Mario Szegedy, Fang Zhang, Xun Gao, Jianxin Chen, Yaoyun Shi. Alibaba Cloud Quantum Development Platform: Applications to Quantum Algorithm Design. arXiv:1909.02559
- Bin-Bin Chen, Yuan Gao, Yi-Bin Guo, Yuzhi Liu, Hui-Hai Zhao, Hai-Jun Liao, Lei Wang, Tao Xiang, Wei Li, Z. Y. Xie. Automatic Differentiation for Second Renormalization of Tensor Networks. arXiv:1912.02780