Josephson parametric amplifiers have become a critical tool in superconducting device physics due to their high gain and quantum-limited noise. Traveling wave parametric amplifiers (TWPAs) promise similar noise performance while allowing for significant increases in both bandwidth and dynamic range. We present a TWPA device based on an LC-ladder transmission line of Josephson junctions and parallel plate capacitors using low-loss amorphous silicon dielectric. Crucially, we have inserted $\lambda/4$ resonators at regular intervals along the transmission line in order to maintain the phase matching condition between pump, signal, and idler and increase gain. We achieve an average gain of 12\,dB across a 4\,GHz span, along with an average saturation power of -92\,dBm with noise approaching the quantum limit.

Traveling wave parametric amplifier with Josephson junctions using minimal resonator phase matching

We characterize a novel Josephson parametric amplifier based on a flux-tunable quarter-wavelength resonator. The fundamental resonance frequency is ~1GHz, but we use higher modes of the resonator for our measurements. An on-chip tuning line allows for magnetic flux pumping of the amplifier. We investigate and compare degenerate parametric amplification, involving a single mode, and nondegenerate parametric amplification, using a pair of modes. We show that we reach quantum-limited noise performance in both cases, and we show that the added noise can be less than 0.5 added photons in the case of low gain.

Characterization of a multimode coplanar waveguide parametric amplifier

Abstract Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics. We further articulate a two-step scheme to improve the reconstruction accuracy while significantly reducing the computational load. Through synthetic and real-world 2-simplicial complexes, we validate the framework by showing that all the connections can be faithfully identified and the full topology of the 2-simplicial complexes can be inferred. The effects of noisy data or stochastic disturbance are studied, demonstrating the robustness of the proposed framework. 

/pdf/full-reconstruction-of-simplicial-complexes-from-binary-2q257o5s.pdf

Full reconstruction of simplicial complexes from binary contagion and Ising data

High-fidelity and rapid readout of a qubit state is key to quantum computing and communication, and it is a prerequisite for quantum error correction. We present a readout scheme for superconducting qubits that combines two microwave techniques: applying a shelving technique to the qubit that effectively increases the energy-relaxation time, and a two-tone excitation of the readout resonator to distinguish among qubit populations in higher energy levels. Using a machine-learning algorithm to post-process the two-tone measurement results further improves the qubit-state assignment fidelity. We perform single-shot frequency-multiplexed qubit readout, with a 140ns readout time, and demonstrate 99.5% assignment fidelity for two-state readout and 96.9% for three-state readout - without using a quantum-limited amplifier. 

https://www.nature.com/articles/s41534-023-00689-6.pdf

Author Correction: Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier

We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in which the active nonlinear element is implemented using an array of rf-SQUIDs. The device is matched to the 50 Ω environment with a Klopfenstein-taper impedance transformer and achieves a bandwidth of 250–300 MHz with input saturation powers up to −95 dBm at 20 dB gain. A 54-qubit Sycamore processor was used to benchmark these devices, providing a calibration for readout power, an estimation of amplifier added noise, and a platform for comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. We find that the high power rf-SQUID array design has no adverse effect on system noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on amplifier added noise at 1.6 times the quantum limit. Finally, amplifiers with this design show no degradation in readout fidelity due to gain compression, which can occur in multi-tone multiplexed readout with traditional JPAs.

/pdf/readout-of-a-quantum-processor-with-high-dynamic-range-1hi0wlx2.pdf

Readout of a quantum processor with high dynamic range Josephson parametric amplifiers

The rapid progress towards scalable quantum processors demands amplifiers with large bandwidths and high saturation powers. For this purpose, we present a broadband flux-pumped Josephson parametric amplifier integrated with an on-chip coplanar waveguide impedance transformer. Our device can be fabricated with simple and straightforward photo-lithography. This device experimentally achieves an operational bandwidth over 600 MHz with a gain above 15 dB, and a high saturation power with quantum-limited noise performance. In addition, the center frequency of this device can be tuned over several hundred megahertz, which in turn broadens the effective operational bandwidth to around 1 GHz.

Broadband flux-pumped Josephson parametric amplifier with an on-chip coplanar waveguide impedance transformer

Measurement of qubits plays a key role in quantum computation. In superconducting multiqubit quantum processors, a multiplexed readout scheme is widely used. In such a scheme, measurement of a qubit state may be influenced by the state of neighboring qubits, due to various crosstalk effects, which will degrade the readout fidelity. To reduce the impact of crosstalk, we model the digital signal processing system used in measurements as a shallow neural network and train it to become a state discriminator. Applying our method to a six-qubit superconducting quantum chip, we see an overall improved readout performance compared with a contemporary qubit-state discriminator. The readout crosstalk is decreased by more than $80\mathrm{%}$. The training and optimization process of the neural network consumes only about 10 s.

Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination

Predicting network dynamics based on data, a problem with broad applications, has been studied extensively in the past, but most existing approaches assume that the complete set of historical data from the whole network is available. This requirement presents a great challenge in applications, especially for large, distributed networks in the real world, where data collection is accomplished by many clients in a parallel fashion. Often, each client only has the time series data from a partial set of nodes, and the client has access to only partial time stamps of the whole set of time series data and the partial structure of the network. Due to privacy concerns or license-related issues, the data collected by different clients cannot be shared. Accurately predicting the network dynamics while protecting the privacy of different parties is a critical problem in modern times. Here, we propose a solution based on federated graph neural networks (FGNNs) that enables the training of a global dynamic model for all parties without data sharing. We validate the working of our FGNN framework through two types of simulations to predict a variety of network dynamics (four discrete and three continuous dynamics). As a signiﬁcant real-world application, we demonstrate successful prediction of state-wise inﬂuenza spreading in the USA. Our FGNN scheme represents a general framework to predict diverse network dynamics through collaborative fusing of the data from different parties without disclosing their privacy.

Machine learning prediction of network dynamics with privacy protection

/pdf/author-correction-full-reconstruction-of-simplicial-1n4le5mw.pdf

Hai-Feng Zhang

Papers

Broadband flux-pumped Josephson parametric amplifier with an on-chip coplanar waveguide impedance transformer

Mitigating Crosstalk-Induced Qubit Readout Error with Shallow-Neural-Network Discrimination

Machine learning prediction of network dynamics with privacy protection

Author Correction: Full reconstruction of simplicial complexes from binary contagion and Ising data

Influence maximization in social networks with privacy protection