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.

Perfect remote quantum state transfer in a superconducting qubit chain with parametrically tunable couplings

  1. X. Li,
  2. Y. Ma,
  3. J. Han,
  4. Tao Chen,
  5. Y. Xu,
  6. W. Cai,
  7. H. Wang,
  8. Y. P. Song,
  9. Zheng-Yuan Xue,
  10. Zhang-qi Yin,
  11. and Luyan Sun
Faithfully transferring quantum state is essential for quantum information processing. Here, we demonstrate a fast (in 84~ns) and high-fidelity (99.2%) quantum state transfer in a
chain of four superconducting qubits with nearest-neighbor coupling. This transfer relies on full control of the effective couplings between neighboring qubits, which is realized only by parametrically modulating the qubits without increasing circuit complexity. Once the couplings between qubits fulfill specific ratio, a perfect quantum state transfer can be achieved in a single step, therefore robust to noise and accumulation of experimental errors. This quantum state transfer can be extended to a larger qubit chain and thus adds a desirable tool for future quantum information processing. The demonstrated flexibility of the coupling tunability is suitable for quantum simulation of many-body physics which requires different configurations of qubit couplings.

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.

Tuning coupling between superconducting resonators with collective qubits

  1. Qi-Ming Chen,
  2. Re-Bing Wu,
  3. Luyan Sun,
  4. and Yu-xi Liu
By coupling multiple artificial atoms simultaneously to two superconducting resonators, we construct a quantum switch that controls the resonator-resonator coupling strength from zero
to a large value proportional to the number of qubits. This process is implemented by switching the qubits among different \emph{subradiant states}, where the microwave photons decayed from different qubits interfere destructively so that the coupling strength keeps stable against environmental noise. Based on a two-step control scheme, the coupling strength can be switched at the \emph{nanosecond} scale while the qubits are maintained at the coherent optimal point. We also use the quantum switch to connect multiple resonators with a programmable network topology, and demonstrate its potential applications in quantum simulation and scalable quantum information storage and processing.