Observation of topological magnon insulator states in a superconducting circuit

  1. Weizhou Cai,
  2. Jiaxiu Han,
  3. Feng Mei,
  4. Yuan Xu,
  5. Yuwei Ma,
  6. Xuegang Li,
  7. Haiyan Wang,
  8. Yipu Song,
  9. Zheng-Yuan Xue,
  10. Zhang-qi Yin,
  11. Suotang Jia,
  12. and Luyan Sun
Searching topological states of matter in tunable artificial systems has recently become a rapidly growing field of research. Meanwhile, significant experimental progresses on observing
topological phenomena have been made in superconducting circuits. However, topological insulator states have not yet been reported in this system. Here, for the first time, we experimentally realize a spin version of the Su-Schrieffer-Heeger model and observe the topological magnon insulator states in a superconducting qubit chain, which manifest both topological invariants and topological edge states. Based on simply monitoring the time evolution of a singlequbit excitation in the chain, we demonstrate that the topological winding numbers and the topological magnon edge and soliton states can all be directly observed. Our work thus opens a new avenue to use controllable qubit chain system to explore novel topological states of matter and also offers exciting possibilities for topologically protected quantum information processing.

Quantum generative adversarial learning in a superconducting quantum circuit

  1. Ling Hu,
  2. Shu-Hao Wu,
  3. Weizhou Cai,
  4. Yuwei Ma,
  5. Xianghao Mu,
  6. Yuan Xu,
  7. Haiyan Wang,
  8. Yipu Song,
  9. Dong-Ling Deng,
  10. Chang-Ling Zou,
  11. and Luyan Sun
Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning—a subfield of artificial intelligence that is currently driving a revolution
in many aspects of modern society. It has shown splendid performance in a variety of challenging tasks such as image and video generations. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to possess the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum state generator can be trained to replicate the statistics of the quantum data output from a digital qubit channel simulator, with a high fidelity (98.8% on average) that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate-scale quantum devices.

Experimental demonstration of work fluctuations along a shortcut to adiabaticity with a superconducting Xmon qubit

  1. Zhenxing Zhang,
  2. Tenghui Wang,
  3. Liang Xiang,
  4. Zhilong Jia,
  5. Peng Duan,
  6. Weizhou Cai,
  7. Ze Zhan,
  8. Zhiwen Zong,
  9. Jianlan Wu,
  10. Luyan Sun,
  11. Yi Yin,
  12. and Guoping Guo
In a `shortcut-to-adiabaticity‘ (STA) protocol, the counter-diabatic Hamiltonian, which suppresses the non-adiabatic transition of a reference `adiabatic‘ trajectory, induces
a quantum uncertainty of the work cost in the framework of quantum thermodynamics. Following a theory derived recently [Funo et al 2017 Phys. Rev. Lett. 118 100602], we perform an experimental measurement of the STA work statistics in a high-quality superconducting Xmon qubit. Through the frozen-Hamiltonian and frozen-population techniques, we experimentally realize the two-point measurement of the work distribution for given initial eigenstates. Our experimental statistics verify (i) the conservation of the average STA work and (ii) the equality between the STA excess of work fluctuations and the quantum geometric tensor.