Three-dimensional integration technologies such as flip-chip bonding are a key prerequisite to realize large-scale superconducting quantum processors. Modular architectures, in whichcircuit elements are spread over multiple chips, can further improve scalability and performance by enabling the integration of elements with different substrates or fabrication processes, by increasing the fabrication yield of completed devices, and by physically separating the qubits onto distinct modules to avoid correlated errors mediated by a common substrate. We present a design for a multi-chip module comprising one carrier chip and four qubit modules. Measuring two of the qubits, we analyze the readout performance, finding a mean three-level state-assignment error of 9×10−3 in 200 ns. We calibrate single-qubit gates and measure a mean simultaneous randomized benchmarking error of 6×10−4, consistent with the coherence times of the qubits. Using a wiring-efficient static inter-module coupler featuring galvanic inter-chip transitions, we demonstrate a controlled-Z two-qubit gate in 100 ns with an error of 7×10−3 extracted from interleaved randomized benchmarking. Three-dimensional integration, as presented here, will continue to contribute to improving the performance of gates and readout as well as increasing the qubit count in future superconducting quantum processors.
The dominant contribution to the energy relaxation of state-of-the-art superconducting qubits is often attributed to their coupling to an ensemble of material defects which behave astwo-level systems. These defects have varying microscopic characteristics which result in a large range of observable defect properties such as resonant frequencies, coherence times and coupling rates to qubits g. Here, we investigate strategies to mitigate losses to the family of defects that strongly couple to qubits (g/2π≥ 0.5 MHz). Such strongly coupled defects are particularly detrimental to the coherence of qubits and to the fidelities of operations relying on frequency excursions, such as flux-activated two-qubit gates. To assess their impact, we perform swap spectroscopy on 92 frequency-tunable qubits and quantify the spectral density of these strongly coupled modes. We show that the frequency configuration of the defects is rearranged by warming up the sample to room temperature, whereas the total number of defects on a processor tends to remain constant. We then explore methods for fabricating qubits with a reduced number of strongly coupled defect modes by systematically measuring their spectral density for decreasing Josephson junction dimensions and for various surface cleaning methods. Our results provide insights into the properties of strongly coupled defect modes and show the benefits of minimizing Josephson junction dimensions to improve qubit properties.
Quantum computing crucially relies on the ability to efficiently characterize the quantum states output by quantum hardware. Conventional methods which probe these states through directmeasurements and classically computed correlations become computationally expensive when increasing the system size. Quantum neural networks tailored to recognize specific features of quantum states by combining unitary operations, measurements and feedforward promise to require fewer measurements and to tolerate errors. Here, we realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological (SPT) phases of a spin model characterized by a non-zero string order parameter. We benchmark the performance of the QCNN based on approximate ground states of a family of cluster-Ising Hamiltonians which we prepare using a hardware-efficient, low-depth state preparation circuit. We find that, despite being composed of finite-fidelity gates itself, the QCNN recognizes the topological phase with higher fidelity than direct measurements of the string order parameter for the prepared states.