Cross-resonance (CR) gate has proved to be a promising scheme for implementing fault-tolerant quantum computation with fixed-frequency qubits. In this work, we experimentally implementan entangling cross-resonance gate by using a microwave-only control in a tunable coupling superconducting circuit. The flux-controlled tunable coupler allows us to continuously vary adjacent qubit coupling from positive to negative values, and thus providing an extra degree of freedom to verify optimal condition for constructing the CR gate. Based on three-qubit CR Hamiltonian tomography, we systematically investigate the dependency of gate fidelities on spurious interaction components and present the first experimental approach to evaluate the perturbation impact arising from the spectator qubits. Our results reveal that the spectator qubits can lead to reductions in the CR gate fidelity relying on the particular frequency resonance poles and the induced ZZ interaction between the spectator and gate qubits, while an improvement in the gate fidelity to 98.5% can be achieved by optimally tuning the inter-qubit detuning and flux bias on the coupler. Our experiments uncover an optimal CR operation regime and provide a guiding principle to improve the CR gate fidelity by suppression of unwanted qubit interactions.
Using geometric phases to realize noise-resilient quantum computing is an important method to enhance the control fidelity. In this work, we experimentally realize a universal nonadiabaticgeometric quantum gate set in a superconducting qubit chain. We characterize the realized single- and two-qubit geometric gates with both quantum process tomography and randomized benchmarking methods. The measured average fidelities for the single-qubit rotation gates and two-qubit controlled-Z gate are 0.9977 and 0.977, respectively. Besides, we also experimentally demonstrate the noise-resilient feature of the realized single-qubit geometric gates by comparing their performance with the conventional dynamic gates with different types of errors in the control field. Thus, our experiment proves a way to achieve high-fidelity geometric quantum gates for robust quantum computation.
Searching topological states of matter in tunable artificial systems has recently become a rapidly growing field of research. Meanwhile, significant experimental progresses on observingtopological 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.
Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning—a subfield of artificial intelligence that is currently driving a revolutionin 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.