Session Z64: Experiments on Current Noisy Quantum Hardware II

Sub-Kelvin bias and gain circuits for improving charge sensing signal-to-noise in quantum dot qubits

Performance measurements for simple bias and current amplification circuits based on inexpensive, commercial (as well as some custom packaged discrete components) measured at <1 Kelvin are presented and discussed as a strategy for improving bandwidth and reducing noise in charge sensor current signals. Capacitive charge sensing using single electron transistors (SETs) relies on discrimination between high and low current levels that may differ by <10 pA. These currents are commonly sourced and measured by electronics located at room temperature outside of the cryogenic environment, resulting in current loops as large as 10 m in length. Our work seeks to both source and amplify the charge sensing current within the 4 K area of the cryogenic system, thus dramatically reducing the opportunity for parasitic noise source to degrade the charge sensing signal. SET measurements taken using resistive voltage dividers and single transistor gain stages located close to SETs will be presented and discussed.

    

A Cost-Effective Measurement System for Calibrated Characterization of Microwave Components at Cryogenic Temperatures

The high demand for a large number of microwave components in the quantum industry causes the necessity of verification and characterization of these components at low temperatures from 4 K to millikelvin. This work presents two measurement setups and the adaptation of standard VNA calibration for a cryogenic environment.

The first system is a 2-port measurement setup for precise microwave characterization of superconducting samples at a 4K environment. We show the measurement setup based on a commercial VNA, 4K small-scale cryostat, and control/switching circuit. A 2-port SOLR calibration method was employed and verified at 4K.

The second system is based on 4K large-scale cryostat and performs fast and scalable RF measurements for broadband cryogenic fully integrated multichannel input/output chains for quantum computing.

In particular, we focus on calibration techniques for the cryogenic environment, measurement setup verification, and thermalization of the devices under test. Finally, we present the results of full two-port calibrated cryogenic microwave measurements performed on flexible superconducting RF hardware and the measurement results of multichannel fully-integrated input/output chains for quantum computing.   

Superconducting Non-reciprocal Bandpass Filter Based on Spatio-Temporal Inductance Modulation

Passives such as ferrite circulators, isolators, and bandpass filters are used in superconducting quantum computers. However, ferrites are bulky and cannot be integrated into superconducting qubit chips. Recognition of the need for compact, non-magnetic non-reciprocal(NR) components has inspired research of NR devices based on parametric modulation using JJs and SQUIDs. To this end, we propose the concept of a NR-Band Pass Filter, (NR-BPF) that combines the functionality of a conventional BPF with an isolator based on the concept of Spatio-Temporal Inductance Modulation (STIM). The BPF follows a capacitively-coupled shunt resonator architecture which is implemented as parallel combination of finger capacitor and a 7-stacked SQUIDs that behave as flux-controlled nonlinear inductors. We analyzed the NR-BPF using Floquet scattering matrices to create frequency translational admittance matrices for time-modulated SQUIDs. Under the right modulation criteria of modulation frequency, phase, and depth, the signals entering the BPF in the forward direction experience little-to-no insertion loss while the signals in the backward direction are converted to intermodulation frequencies, thus realizing RF isolation. Post layout simulations of  NR-BPF tuned to 4.8 GHz and modulated at 700 MHz achieves <1 dB insertion loss from 4.4 GHz to 5.2 GHz and isolation greater than 20 dB from 4.7 GHz to 4.9 GHz. The DC flux bias of the SQUIDs can be leveraged to tune the center frequency from 3.5 GHz to 6 GHz.

    

Experiments on programmable arrays of Rydberg atoms

The startup Pasqal is developing a quantum computing platform made of controllable arrays of neutral atoms. We will present here the main characteristics of these devices and illustrate how applications ranging from classification tasks to simulation of quantum systems can be explored at the analog level (programming Hamiltonian sequences). We will also report on recent progress in scaling up the number of qubits. Based on extended atomic lifetime and optimized trap loading equalization procedure, we have been able to generate arrays with more than 300 atoms while maintaining high accuracy of defect-free realizations. These results are the first step towards neutral-atom quantum processors with more than a thousand particles.   

Characterizing states and measurements: principles and approaches

The problem of separately characterizing state preparation and measurement (SPAM) processes has not been frequently discussed in the literature. In this talk, I will first review the theoretical challenge behind SPAM characterization due to a gauge freedom, and then describe two different protocols that achieve this task. The first one can be understood as an effective propagation of state preparation noise from the target system to an ancillary qubit, whereas the second one utilizes measurements and post-selection to reduce the state preparation noise and can be interpreted as a form of algorithmic cooling. For the first method, I will present experimental and simulation data obtained from real quantum processors. For the second method, I will analyze its overhead through an upper bound on the expected number of runs to achieve a given error-reduction ratio.   

Single-shot Hamiltonian parameter estimation by real-time sequential Monte-Carlo method

Bayesian filter, also known as Kalman filter for linear system or particle filter for nonlinear system, allow optimal control in quantum and classical interface circuitry. While applying Bayesian filter to classical electronic applications have shown accurate estimation and controllability, little is experimentally known about efficiency of Bayesian filter in noisy quantum system where observation model is nonlinear, stochastic and discrete. Previous works using conventional Bayesian inference (Maximum a Posteriori) could suppress the noise of qubit by measurement-based feedback control. However, the conventional estimator needs sufficient statistics for accuracy, which requires a large mount of data obtained from projective measurements. We report the fast Hamiltonian estimation and feedback in a single projective measurement by the real-time particle filtering, also known as sequential Monte-Carlo method. Using a singlet-triplet semiconductor qubit under nuclear spin noise and charge noise, we show qubit frequency estimation per single-shot measurement improving the coherence time by more than two orders of magnitude. Moreover, we investigate the noise properties and discuss potential for improvement, limitation and universal state-space representation for various quantum computing platforms suffering from the fluctuating environment.

    

Deep Adaptive Design for Characterisation of Superconducting Qubits

Modern superconducting quantum computers are severely limited not by the number of qubits but by the high error and noise characteristics. The detailed system characterization required to understand the underlying error sources is an arduous process and impractical with increasing chip size. Typical textbook characterisation routines do not scale efficiently to large multi-qubit chips, requiring the development of advanced techniques based on statistical and information theoretic foundations. We present a Bayesian Experiment Design process that adaptively recommends the most optimal experiments at every step to maximise the expected information gain about the system, thus enabling optimal identification of arbitrary system model parameters. The cost of calculating expensive Bayesian posteriors is amortised by the use of Reinforcement Learning assisted Deep Adaptive Design techniques. A high-fidelity differentiable digital twin that models the open quantum dynamics, complete electronic control stack and noise & transfer functions for various superconducting quantum devices lies at the heart of this closed loop adaptive calibration and characterisation process. The practical usability of this Bayesian Experiment Design method is demonstrated on a multi-qubit superconducting transmon chip.   

Autonomous characterization of multi-transmon circuit QED processors

Operating a modern circuit QED processor requires knowledge of the physical characteristics of a large number of interconnected qubits and resonators. These characteristics are initially learned through an extensive sequence of low-level measurements (e.g., spectroscopy). Such measurements must be repeated for every new device due to fabrication non-uniformity and design variation. We demonstrate autonomous device characterization starting from minimal prior knowledge such as device layout and design parameters (e.g., qubit and resonator frequencies and coupling strengths). Examples include qubit-resonator matching, finding qubit sweetspots, and identifying the linear regime for readout. This approach accelerates the transition to graph-based calibration of quantum operations (qubit gates and readout). During the characterization process, a digital twin is constructed based on the learned device parameters. This interactable model can also be used to simulate the low-level characterization of devices with perturbed parameters.   

Noise spectroscopy of a Josephson parametric oscillator

Bistable processes are among the most frequent physical phenomena observed in nature.  Josephson parametric oscillators (JPO) based on nonlinear superconducting resonators exhibit bistability where the dynamics of these systems can be qualitatively changed depending on the accessible parameters. When the parametric pump strength of the JPO gradually increases, crossing a threshold, the output field of the JPO settles into oscillating states with a well-defined phase of either 0 or π. In this talk, we present the experimental results on the noise spectroscopy of a JPO using a microwave homodyne interferometric measurement scheme where we characterize the phase and amplitude noise by continuously tracking the oscillator output dynamics. We observe the interstate switching of the oscillating output state between the two stable states of the JPO, which contributes to 1/f² characteristics in the phase noise spectrum and can be suppressed with the increase in the pump strength.    

Non-Markovian transient spectroscopy in cavity QED

We theoretically analyze measurements of the transient field leaving a cavity as a tool for studying non-Markovian dynamics in cavity quantum electrodynamics (QED) [1]. Combined with a dynamical decoupling pulse sequence, transient spectroscopy can be used to recover spectral features that may be obscured in the stationary cavity transmission spectrum due to inhomogeneous broadening. The formalism introduced here can be leveraged to perform in situ noise spectroscopy, revealing a robust signature of quantum noise arising from non-commuting observables, a purely quantum effect.

[1] Z. McIntyre and W. A. Coish, arXiv:2206.02073

Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing

We propose a quantum annealing protocol that more effectively probes the dynamics of a single qubit on D-Wave's quantum annealing hardware. This protocol uses D-Wave's h-gain schedule functionality, which allows the rapid quenching of the longitudinal magnetic field at arbitrary points during the anneal. This features enables us to distinguish between open and closed system dynamics as well as the presence or absence of longitudinal magnetic field noise. We show that both thermal and magnetic field fluctuations are key sources of noise that need to be included in an open quantum system model to reproduce the output statistics of the hardware.   

Relaxational Quantum Eigensolver: Optimization and Tuning

In the last several years the Variational Quantum Eigensolver (VQE) has garnered significant interest due to its ability to efficiently prepare approximate ground states of quantum problems. However, current implementations of VQE must race against proliferating gate error, which limits its usefulness for problems requiring high circuit depths. Our work draws on ideas from bath engineering, open quantum systems, and variational algorithms to develop an algorithm exhibiting continuous, approximate error correction, which we call the Relaxational Quantum Eigensolver (RQE). In RQE we instantiate a second register of auxiliary "shadow" qubits and weakly couple them to the primary system in Trotterized evolution, allowing us to engineer an approximate zero-temperature bath by periodically resetting them during the algorithm's runtime. Balancing the infinite temperature bath of random gate error, RQE returns distributions with an average energy equal to a constant fraction of the ground state, even at infinite circuit depth. Using extensive simulations, we have demonstrated that RQE is able to successfully find approximate near-ground steady states in the face of proliferating error, for circuits with over 450 layers and 24 total qubits. This talk focuses on optimizing and fine tuning the algorithm's parameters in order to improve overall performance and reduce the impact of error on the ground state approximation.