Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session TT03: V: Advances in Qubit Readout and Control |
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Sponsoring Units: DQI Chair: Xuexin Xu, Forschungszentrum Jülich GmbH Room: Virtual Room 3 |
Tuesday, March 21, 2023 3:30PM - 3:42PM |
TT03.00001: Effective and Scalable Control of Quantum Processors – Insight and Perspective Benjamin Lienhard, Saeed A Khan, Robert Kosut, Hakan E Tureci, Herschel A Rabitz To achieve the dream of useful quantum computation, there are two main thrusts to make improvements: quantum system design and control. Now at the junction of realizing effective quantum error correction, the boundaries of quantum system control become an essential piece of information. Establishing the limits of efficient, scalable, and accurate quantum control can inform the efficacy and choice of different kinds of error correction. The effort of quantum system control, specifically in terms of measurements during calibration, must be sufficiently low to compensate for system parameter drift or fast enough to enable periodic re-calibration. The effectiveness of different control routines can be evaluated using theoretical and numerical studies. While theoretical models help inform on general structures of quantum control landscapes, the controllability, or the complexity of the computational effort, they are often insufficient to represent the real quantum system. Complete system characterization can lead to accurate numerical models representing the entire quantum system but are incredibly cumbersome. Model-free learning control, a laboratory-costly approach, represents the other extreme. These methods tend to be highly laborious and measurement-intensive as systems scale in size. Here, we discuss our insights and perspective on the limits and capabilities of quantum control, the role of theoretical studies, and the methods available for experimental quantum control from a measurement point of view. Finally, we explore the design of quantum control schemes that are as robust as possible to system uncertainties while remaining resource-efficient. |
Tuesday, March 21, 2023 3:42PM - 3:54PM |
TT03.00002: Analysis of Elzerman readout based on Bayesian inference methods Julia Berndtsson, Adam R Mills, Zhaoyi (Joy) Zheng, Jason R Petta
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Tuesday, March 21, 2023 3:54PM - 4:06PM |
TT03.00003: Qubit-state discrimination techniques for accurate quantum error correction Liangyu Chen, Benjamin Lienhard, Basudha Srivastava, Anton Frisk Kockum, Mats Granath, Per Delsing, Jonas Bylander, Giovanna Tancredi Quantum error correction codes rely on the rapid and high-fidelity qubit-state readout. Qubit-state readout is often among the most error-prone operations for superconducting quantum processors. For instance, qubit-state transitions during readout and noise added to the measurement signal can make readout signals cumbersome to classify. To address these challenges, we recently demonstrated a readout scheme composed of two techniques: a shelving technique to mitigate the error from state transitions and a two-tone readout signal to increase the readout-signal distinguishability. Within readout times of 140 ns, we achieve fidelities in excess of 99.5%. Building on this result, we first evaluate to what extent the readout technique is quantum-non-demolition (QND), an essential requirement for quantum error correction protocols. We find that the method is QND even if we excite the qubits to higher states during readout. Next, we investigate how to exploit the uncertainty in qubit-state discrimination. A feedforward neural network (FNN) classifier used to post-process the measurement result readily offers confidence information in the qubit-state assignment. We explore how to improve minimum weight matching decoders by incorporating this information in the weights of graph edges that correspond to stabilizer measurement errors. |
Tuesday, March 21, 2023 4:06PM - 4:18PM |
TT03.00004: Improve the Pauli coefficient measurement with Active Learning Jiaqi Ai We provide an improvement in the process of Active Learning as a concept from machine learning that labels a large amount of data with a small amount of learning material. In this approach, the method is implemented to speed up measuring the Pauli coefficient for the two-qubit gate. The aim of the implementation is to prove the speed up of the measuring process by reducing unwanted interactions. |
Tuesday, March 21, 2023 4:18PM - 4:30PM |
TT03.00005: Linear Filtering of Pulses for Cross-Talk Elimination in Frequency-Multiplexed Qubit Control Sushil Subramanian, Stefano Pellerano, Todor Mladenov Frequency multiplexed microwave driving of a large array of spin qubits is a potential solution to scalable control of a fault-tolerant quantum computer. Such control of spin qubits closely spaced in frequency using a common interconnect is susceptible to cross-talk due to the leakage of energy outside the bandwidth of the driven qubit. While pulse shaping can reduce leakage to adjacent victim qubits, general pulse shapes are typically not easily programmable to accommodate varying qubit frequencies, particularly when implemented within low-power, mostly-digital integrated cryogenic controllers. In this work, we show that the loss of fidelity due to X and Y rotations of a victim qubit is approximately proportional to the spectral density of the pulse shape, thereby motivating the use of frequency domain linear filtering to eliminate cross-talk. We then describe low-power, programmable, discrete-time pulse filtering techniques that eliminate leakage and thereby cross-talk at a victim qubit's Larmor Frequency. A digital implementation of the filter is described that is easily scalable by cascading multiple programmable filters to eliminate cross-talk for several qubits. Finally, using simulations we demonstrate that the cross-talk elimination technique restores fidelity to 99.9% for the victim qubit. |
Tuesday, March 21, 2023 4:30PM - 4:42PM |
TT03.00006: FPGA module synchronization for QubiC Yilun Xu, Gang Huang, Neelay Fruitwala, Ravi K Naik, Kasra Nowrouzi, David I Santiago, Irfan Siddiqi As quantum bit (qubit) grows in count, the radio frequency (RF) control system becomes a limiting factor to large scale extensibility. To tackle this challenge and keep pace with rapidly evolving classical control requirements, we developed a synchronization protocol among multiple FPGA modules for distributed control systems. The pulse synchronization was presented on our customized FPGA-based RF control system (QubiC) with newly developed low-cost DAC boards. The synchronization functionality was demonstrated by two-qubit experiments on superconducting quantum information processors. |
Tuesday, March 21, 2023 4:42PM - 4:54PM |
TT03.00007: Quantum Crosstalk Mitigating Optimized Single Qubit Gates Kevin Fernando, Yasuo Oda, Gregory Quiroz As we enter the NISQ era, quantum computation is still held back by noise experienced on hardware which is often difficult to mitigate. A particularly important and complex type of noise is crosstalk. In this work, we construct optimized control sequences for two-qubits that suppress crosstalk noise for given gates and crosstalk coupling strength. We utilize the Filter Function Formalism (FFF) to convert a problem of control into one of finding optimized filters. This allows us to analytically derive favorable initial conditions, which are used in the FGRAFS code to find optimal filters using gradient ascent. We obtain simultaneous single qubit control gates on two qubits of 1e-9 infidelity after O(100) iterations of the optimizer. Additionally, we find clear bandwidth control conditions that must be satisfied in order to achieve high fidelity gates for both high pass and bandpass filters. |
Tuesday, March 21, 2023 4:54PM - 5:06PM |
TT03.00008: Discovering Dynamical Error-Correcting Gate with Geometric Formalism and Machine Learning Bikun Li, Edwin Barnes In modern quantum information technology, how to protect and manipulate quantum information has been a key question in realizing reliable quantum algorithms and simulations. It is imperative to implement quantum operation at the bottom level with higher accuracy. There are plenty of work focused on quantum control and gate design in the past decades. However, designing a proper quantum gate with the practical device constraint while achieving certain goals is a challenging task. In this work, we try to combine geometric formalism for quantum control and physics-informed neural network. We show that the approach of machine learning is capable of discovering and optimizing dynamical error-correcting gates with practical constraints. |
Tuesday, March 21, 2023 5:06PM - 5:18PM |
TT03.00009: Theory of multi-dimensional quantum capacitance and its application to spin and charge discrimination in quantum-dot arrays Andrea Secchi, Filippo Troiani Quantum states of a few-particle system capacitively coupled to a metal gate can be discriminated by measuring the quantum capacitance (QC), which can be identified with the second derivative of the system energy with respect to the gate voltage. If more than one gates are capacitively coupled with the system (e.g., in a quantum-dot array), the theory must be generalized to account for the dependence of the energy on all the applied gate voltages. With this aim, we have introduced the concept of QC matrix. The matrix formalism allows us to determine the dependence of the QC on both the working point and direction of the voltage oscillations in the parameter space, and to identify the optimal combination of gate voltages that maximizes the outcome of a QC measurement. From the application to a quantum-dot array, we predict novel measurable features that are specific of the multi-gate approach, such as QC plateaus and voltage-tunable heights of the QC peaks. We show how such features depend on the quantum tunneling processes involved in the transitions between different charge stability regions. Altogether, our work provides a procedure for optimizing the discrimination between states with different particle numbers and/or total spins, which can be realized experimentally. |
Tuesday, March 21, 2023 5:18PM - 5:30PM |
TT03.00010: QuantumGEP: Gene Expression Programming for Quantum Computing Gonzalo Alvarez, Ryan Bennink, Stephan Irle, Jacek Jakowski We are designing and implementing QuantumGEP [1]: a scalable artificial intelligence software generator to construct quantum circuits that output the ground state of a Hamiltonian when applied to an initial state. QuantumGEP relies on gene expression programming (GEP) [2] and this talk focuses on advanced features, including automatically defined functions, multiple genes, and the chromosome. [1] The software is at https://github.com/g1257/evendim; [2] Ferreira, C., Gene Expression Programming, Mathematical Modeling by an Artificial Intelligence, 2nd Ed. Springer-Verlag, Berlin, Heidelberg, 2006. |
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