Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley, Kevin J. Satzinger, Frank Arute, Kunal Arya, Juan Atalaya, Joseph C. Bardin, Rami Barends, Sergio Boixo, Mic… 展开作者
Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley, Kevin J. Satzinger, Frank Arute, Kunal Arya, Juan Atalaya, Joseph C. Bardin, Rami Barends, Sergio Boixo, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett, Nicholas Bushnell, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Sean Demura, Andrew Dunsworth, Daniel Eppens, Austin Fowler, Brooks Foxen, Craig Gidney, Marissa Giustina, Rob Graff, Steve Habegger, Alan Ho, Sabrina Hong, Trent Huang, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody Jones, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Seon Kim, Paul V. Klimov, Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Pavel Laptev, Mike Lindmark, Martin Leib, Orion Martin, John M. Martinis, Jarrod R. McClean, Matt McEwen, Anthony Megrant, Xiao Mi, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Charles Neill, Florian Neukart, Murphy Yuezhen Niu, Thomas E. O'Brien, Bryan O'Gorman, Eric Ostby, Andre Petukhov, Harald Putterman, Chris Quintana, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Andrea Skolik, Vadim Smelyanskiy, Doug Strain, Michael Streif, Marco Szalay, Amit Vainsencher, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Leo Zhou, Hartmut Neven, Dave Bacon, Erik Lucero, Edward Farhi, Ryan Babbush
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA). Like past QAOA experiments, we study performance for problems defined on the (planar) connectivity graph of our hardware; however, we also apply the QAOA to the Sherrington-Kirkpatrick model and MaxCut, both high dimensional graph problems for which the QAOA requires significant compilation. Experimental scans of the QAOA energy landscape show good agreement with theory across even the largest instances studied (23 qubits) and we are able to perform variational optimization successfully. For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth. For problems requiring compilation, performance decreases with problem size but still provides an advantage over random guessing for circuits involving several thousand gates. This behavior highlights the challenge of using near-term quantum computers to optimize problems on graphs differing from hardware connectivity. As these graphs are more representative of real world instances, our results advocate for more emphasis on such problems in the developing tradition of using the QAOA as a holistic, device-level benchmark of quantum processors.
Realizing leakage elimination operator-based adiabatic speedup on a superconducting quantum processor
College of Physics and Optoelectronic Engineering, Ocean University of China（中国海洋大学物理与光电工程学院）；China Mobile (Suzhou) Software Technology Co., Ltd（中国移动（苏州）软件技术有限公司）；School of Sciences, Qingdao University of Technology（青岛理工大学理学院）
Cryogenic Multiplexing Control Chip for Superconducting Quantum Processor
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Beijing Innovation Center for Future Chips, Tsinghua University（清华大学未来芯片创新中心）
Random Party Distillation on a Superconducting Processor
University of Toronto（多伦多大学）；National University of Singapore（新加坡国立大学）；Quantum Bridge Technologies Inc.（量子桥技术有限公司）；Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子技术中心）
Qubit error bursts in superconducting quantum processors of Quantum Inspire: quasiparticle pumping and anomalous time dependence
QuTech and Kavli Institute of Nanoscience, Delft University of Technology（代尔夫特理工大学量子纳米科学卡弗里研究所和QuTech）；Netherlands Organisation for Applied Scientific Research (TNO)（荷兰应用科学研究组织（TNO））；Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology（代尔夫特理工大学电气、数学与计算机科学学院）
Experimental Quantum End-to-End Learning on a Superconducting Processor
机器学习可以由量子计算机提供大量动力，因为它具有巨大的希尔伯特空间和固有的量子并行性。为了追求具有噪声的中等规模量子设备的机器学习的量子优势，提出了可以以端到端的方式设计学习模型，即，通过直接可操纵的控制脉冲来参数化量子测量，而无需电路设计和编译。这种无门模型是硬件友好的，可以充分利用有限的量子资源。在这里，我们报告了在超导处理器上实现量子端到端机器学习的第一个实验。在MNIST（Mixed National Institute of Standards and Technology）数据库中，经过训练的模型可以对两个手写数字（通过两个量子位）实现98%的识别准确率，对四个数字（通过三个量子位）实现89%的识别准确率。实验结果展示了量子端到端学习的巨大潜力，当有更多的量子比特可用时，它可以解决复杂的现实世界任务。
Wave--particle transition and quantum Zeno effect in which-way experiments with a superconducting quantum processor
RIKEN Center for Quantum Computing (RQC)（理化学研究所量子计算中心）；Department of Physics, University of Michigan（密歇根大学物理系）；Department of Applied Physics, Graduate School of Engineering, The University of Tokyo（东京大学工学研究科应用物理系）
Error-Corrected Memory and Logic on a Heavy-Hex Superconducting-Qubit Processor
The University of Sydney（悉尼大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Sungkyunkwan University（成均馆大学）；IBM Quantum, T. J. Watson Research Center（IBM量子T.J.沃森研究中心）；IBM Denmark（IBM丹麦）
Learning Lindblad Dynamics of a Superconducting Quantum Processor
University of Copenhagen（哥本哈根大学）；Niels Bohr Institute, University of Copenhagen（尼尔斯·玻尔研究所，哥本哈根大学）；Center for the Mathematics of Quantum Theory, MATH Department, University of Copenhagen（哥本哈根大学数学系量子理论数学中心）；Chalmers University of Technology（查尔姆斯理工大学）
