Session S08: Advances in Qubit Measurement II

Asymmetric Sensing Dot for Scaleable Baseband Readout of Spin Qubits

High fidelity scalable readout is one of the key requirements for quantum computers with more than just a few qubits. Charge sensing dots are in this regard the most sensitive sensors for spin qubit readout. The most widespread readout technique is based on RF reflectometry, satisfying the requirement of high fidelity, but requires bulky, power-hungry components and is not well scalable. A more scalable alternative is to use transistors in close proximity to the qubit [1,2].
For best performance a high output swing of the sensor is desirable. We present experimental results in GaAs of an asymmetric sensing dot (ASD), improving the sensor response by a factor of 15 compared to conventional charge sensing dots. We perform charge sensing using a current biased ASD and observe a 2.36 mV swing in response to a (1,1)→(2,0) transition in a nearby double dot.
The improved voltage swing is due to a device design with a strongly decoupled drain reservoir from the sensor dot, mitigating negative feedback effects.

[1] M. J. Curry et al., APL 2015
[2] L. A. Tracy et al., APL 2016
  

A Reservoir Computing Approach to Quantum State Measurement

Quantum state measurement is an essential step in the probe or operation of any quantum system, and its optimization has accordingly been the focus of considerable ongoing research. Increases in the speed and fidelity of continuous measurements directly contribute to quantum information processing applications and the fundamental study of quantum systems. In this work, we propose a hardware-based reservoir computing system for quantum state measurement and discuss its performance when compared to conventional approaches. We theoretically analyze the readout of a superconducting circuit via direct coupling to a minimal reservoir computer and demonstrate how such a system provides a low latency approach to state measurement.   

Neural Network assisted Superconducting Qubit Readout

A significant error source in contemporary quantum processors is qubit-state readout. For a single qubit connected to a unique readout line, a linear matched filter is sufficient for high-fidelity readout. However, it is more resource efficient to frequency-multiplex multiple qubits on the same readout line. In this case, the single-qubit matched filters are no longer optimal. Rather, readout discrimination becomes a computationally intensive, multi-state classification problem. Here, we present a new approach to the readout problem based on neural networks. We discuss different types of neural network architectures and their readout discrimination performance compared to current readout methods when applied to experimental single and multi-qubit readout data.   

Single-Qubit Optimal Quantum Readout via Neural Networks

High fidelity readout is an essential part of quantum computing. Conventional approaches for superconducting circuit readout rely on linear models. We explore the benefits of employing alternative classification schemes based on neural networks to improve fidelity. Experimental results of qubit readout will be presented.   

Dispersive readout for Majorana qubits

We analyze the use of dispersive readout for qubits encoded in topological superconducting nanowires. Two models are considered: the Majorana transmon qubit and the Majorana box qubit. We model the interaction with each qubit and a readout resonator, and calculate the size of the qubit state-dependent dispersive shift of the resonator. We show that dispersive readoutu of Majorana qubits is protected against measurement-induced bit flips. Furthermore, we find that Majorana transmon qubits are well-suited to dispersive readout, producing dispersive shifts comparable to that of conventional superconducting transmons. Majorana box qubits produce more modest, but still potentially viable, dispersive shifts.   

Tracking non-Markovian quantum dynamics of a superconducting qubit with a recurrent neural network filter

Precise quantum control of superconducting qubits necessitates determining the time-dependent Hamiltonian of control pulses with high fidelity. While continuous state tracking has proved effective for determining qubit time-evolution in regimes with Markovian dynamics, fast control pulses used for native quantum gates and entanglement generation can result in non-Markovian transient dynamics. We use quantum state tracking with continuous weak measurement to experimentally investigate non-Markovianity in a transmon superconducting qubit coupled to a readout resonator. By weakly measuring the qubit state during a Rabi oscillation sequence on a timescale comparable to the cavity decay rate, we isolate dynamics that are difficult to describe with single-qubit trajectory theory. We train a recurrent neural network to reconstruct the quantum trajectories, motivated by such a network's demonstrated ability to learn long-time correlations in sequential data, and estimate parameters of the stochastic master equation.   

High-fidelity quantum state estimation via autoencoder tomography

We investigate the use of supervised machine learning, in the form of a denoising
autoencoder, to simultaneously remove experimental noise while encoding one- and two-qubit quantum state estimates into a minimum number of nodes within the latent layer of a neural network. We decode these latent representations into positive density matrices and compare them to similar estimates obtained via linear inversion and maximum likelihood estimation. Using a superconducting multiqubit chip we experimentally verify that the neural network estimates the quantum state with greater fidelity than either traditional method. Furthermore, we show that the network can be trained using only product states and still achieve high fidelity for entangled states. This simplification of the training overhead permits the network to aid experimental calibration, such as the diagnosis of multi-qubit crosstalk.   

Characterization and tomography of a hidden qubit

In circuit-based quantum computing it is typically assumed that the available gate set consists of single qubit gates acting on each individual qubit as well as an entangling gate between pairs of qubits. In some physical architectures, not all qubits may be addressable, but some may be hidden and only connected to another control qubit. In this case, no single qubit operations can be applied to the hidden qubit and its state cannot be measured directly, but entangling gates with the control qubit can be carried out. Tomography of the combined two-qubit system is still possible on the combined two-qubit system whenever an iSWAP-type interaction in combination with a controlled-phase gate and single qubit operations on the control qubit is available. In our experiment we use transmon-type superconducting qubits along with parametric tunable-coupler gates to realize both types of two-qubit interactions. We further discuss the tune-up process required to completely characterize the gate set used for tomography and we evaluate the resulting fidelities.   

Quantum Rifling: Protecting a Qubit from Measurement Back-action

Quantum mechanics postulates that measuring the qubit's wave function results in its collapse, with the recorded discrete outcome designating the particular eigenstate the qubit collapsed into. We show this picture breaks down when the qubit is strongly driven during measurement. More specifically, for a fast evolving qubit the measurement returns the time-averaged expectation value of the measurement operator, erasing information about the initial state of the qubit, while completely suppressing the measurement back-action. We call this regime `quantum rifling', as the fast spinning of the Bloch vector protects it from deflection into either of its two eigenstates. We study this phenomenon with two superconducting qubits coupled to the same probe field and demonstrate that quantum rifling allows us to measure either one of the two qubits on demand while protecting the state of the other from measurement back-action. Our results allow for the implementation of selective read out multiplexing of several qubits, contributing to efficient scaling up of quantum processors for future quantum technologies.   

Rapid gate-based read-out of spins in silicon using an on-chip resonator

Over the past decade tremendous progress has been made on spin qubits based on electron spins in silicon quantum dots. As with any qubit implementation, a critical requirement is the ability to read out the qubit rapidly, with high fidelity, and in a scalable manner. Much attention has been focused on improving single-electron transistors embedded in radio-frequency reflectometry circuits as charge detectors to detect, in combination with a spin-to-charge conversion scheme, electron spin states. While these are the most sensitive detectors to date, they come with additional resources that take up valuable space near the quantum dots (gate electrodes, electron reservoirs), which makes scaling up to two-dimensional spin qubit arrays difficult. More efficiently, read-out can be performed utilizing gates that are already in place for defining quantum dots by connecting those gates to a resonant circuit. This promising method of gate-based sensing has been developed for quantum dots with off-chip resonators, and only very recently achieved the sensitivity necessary for single-shot read-out of spins in silicon [1]. In this talk, I will describe the use of an on-chip superconducting microwave resonator to improve the sensitivity, aided by its high quality factor and impedance. Using Pauli Spin Blockade as the spin-to-charge conversion scheme, we demonstrate the gate-based read-out of a two-electron spin state in a single shot with an average fidelity of 98% in only 6 microseconds [2]. Furthermore, our latest work towards long-distance spin-spin coupling will be presented.
[1] Hu, Nat. Nano. 14, 735 (2019), and references therein.
[2] Zheng et al, Nat. Nano. 14, 742 (2019)   

Improving Multilevel Qudit Readout Fidelity During Relaxation Events via Hidden Markov Models

Quantum state determination with high fidelity is a requirement for quantum computation. However, qubit relaxation imposes a limiting constraint on readout fidelity. Longer readout measurement times increase the distinguishability between the qudit states, however state decay during a long readout pulse can obfuscate the measurement and result in misclassification of qudit state. To overcome this constraint, we demonstrate high-fidelity multi-state readout by detecting qubit relaxation with Hidden Markov Models on LLNL’s Quantum Design and Integration Testbed (QuDIT). The ability of Hidden Markov Models to account for relaxation and thermal excitation processes allows for longer readout times, extraction of transition probabilities, and higher readout fidelity.   

Continuous Indirect detection of stabilizers for quantum error correction

Measuring high-weight operators is an important problem in quantum computation. The conventional procedure to measure a high-weight operator involves multiple pairwise unitary operations, which may require a large number of quantum gates. We provide an alternative method to passively detect the value of an operator. This approach involves joint interactions between the system and continuously-monitored ancillary qubits. The continuous measurement outcomes of the monitor qubits reveal information about the values of the stabilizer generators. This information can be retrieved using an estimator, which is driven by the measurement outcomes. We also show that there is a more efficient way to read out the outcomes directly from the time average of the signals. The interaction Hamiltonian can use only two-local operators, based on techniques similar to perturbative gadgets. We apply this indirect detection scheme to the four-qubit Bacon-Shor code, where the two stabilizers are indirectly monitored using four ancillary qubits. Since it is an error-detecting code, we show that non-Markovian errors, e.g., the Hamiltonian 1/f noise, can be suppressed by the indirect detection. This detection scheme could be implemented in near-term experimental systems and operate in real time.   

Diagnosing Errors in Quantum Gates Using Continuous Measurements

We use continuous weak measurements to track a quantum gate operation as it develops in time, which allows us to identify the full time-dependent dynamics of any systematic errors. We account for measurement backaction such that it has no bearing on the error estimate. This diagnostic method is tested on imperfect single- and two-qubit gates, and is shown to accurately extract known time-dependent error pulses. This offers significant advantages over traditional quantum process tomography, as it provides the ability to identify the origin and nature of any deviations from the desired evolution, which can give insight into how to modify the gate pulse.