Calibration of Flux Crosstalk in Large-Scale Flux-Tunable Superconducting Quantum Circuits

Dai, Xianzhe; Tennant, Daniel; Trappen, Robbyn; Martinez, Antonio; Melanson, Denis; Yurtalan, Muhammet Ali; Tang, Yongchao; Новиков, Сергей Петрович; Grover, Jeffrey A.; Disseler, Steven M.; Basham, James I.; Das, Rabindra Nath; Kim, D.K.; Melville, Alexander; Niedzielski, Bethany; Weber, Steven; Yoder, Jonilyn; Lidar, Daniel A.; Lupaşcu, Adrian

doi:10.1103/prxquantum.2.040313

Cited by 23 publications

(18 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This would particularly be advantageous where a variety of experiments can be carried out but it is not clear which of them are more informative for the learning task. For example, a query efficient method is desired for learning cross-talk on a superconducting quantum device [80,81].…”

Section: Discussionmentioning

confidence: 99%

Active Learning of Quantum System Hamiltonians yields Query Advantage

Dutt¹,

Pednault²,

Wu³

et al. 2021

Preprint

View full text Add to dashboard Cite

Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers. Through queries to the quantum system, this procedure seeks to obtain the parameters of a given Hamiltonian model and description of noise sources. Standard techniques for Hamiltonian learning require careful design of queries and O( −2 ) queries in achieving learning error due to the standard quantum limit. With the goal of efficiently and accurately estimating the Hamiltonian parameters within learning error through minimal queries, we introduce an active learner that is given an initial set of training examples and the ability to interactively query the quantum system to generate new training data. We formally specify and experimentally assess the performance of this Hamiltonian active learning (HAL) algorithm for learning the six parameters of a two-qubit cross-resonance Hamiltonian on four different superconducting IBM Quantum devices. Compared with standard techniques for the same problem and a specified learning error, HAL achieves up to a 99.8% reduction in queries required, and a 99.1% reduction over the comparable non-adaptive learning algorithm. Moreover, with access to prior information on a subset of Hamiltonian parameters and given the ability to select queries with linearly (or exponentially) longer system interaction times during learning, HAL can exceed the standard quantum limit and achieve Heisenberg (or super-Heisenberg) limited convergence rates during learning.

show abstract

Section: Discussionmentioning

confidence: 99%

Active Learning of Quantum System Hamiltonians yields Query Advantage

Dutt¹,

Pednault²,

Wu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Crosstalk is a major factor that impedes the development of scalable quantum information processing architectures. Among the miscellaneous crosstalk phenomena including measurement crosstalk [1,2], signal crosstalk [3][4][5][6][7] and quantum-mechanical crosstalk (spurious coupling) [8][9][10][11], the microwave signal crosstalk is notorious for superconducting qubits which usually operate at 4-8 GHz. Various technologies have been developed to improve isolation between circuit components to reduce such crosstalk [12][13][14][15][16].…”

mentioning

confidence: 99%

Canceling microwave crosstalk with fixed-frequency qubits

Nuerbolati

Han

Chu

et al. 2022

Applied Physics Letters

View full text Add to dashboard Cite

Scalable quantum information processing requires that modular gate operations can be executed in parallel. The presence of crosstalk decreases the individual addressability, causing erroneous results during simultaneous operations. For superconducting qubits which operate in the microwave regime, electromagnetic isolation is often limited due to design constraints, leading to signal crosstalk that can deteriorate the quality of simultaneous gate operations. Here, we propose and demonstrate a method based on the alternative-current Stark effect for calibrating the microwave signal crosstalk. The method is suitable for processors based on fixed-frequency qubits, which are known for high coherence and simple control. The optimal compensation parameters can be reliably identified from a well-defined interference pattern. We implement the method on an array of seven superconducting qubits and show its effectiveness in removing the majority of crosstalk errors.

show abstract

“…Flux crosstalk to non-primary loops is suppressed to a mean of 0.5% and maximum of 3.4% between any antenna-loop pair via an iterative calibration procedure based on Refs. [25,29] (see Supplementary Information). The on-chip circuit includes a fourth flux qubit that is not needed and so was far detuned from the other transitions.…”

mentioning

confidence: 99%

Demonstration of tunable three-body interactions between superconducting qubits

Menke¹,

Banner²,

Bergamaschi³

et al. 2022

Preprint

View full text Add to dashboard Cite

Nonpairwise multi-qubit interactions present a useful resource for quantum information processors. Their implementation would facilitate more efficient quantum simulations of molecules and combinatorial optimization problems, and they could simplify error suppression and error correction schemes. Here we present a superconducting circuit architecture in which a coupling module mediates 2-local and 3-local interactions between three flux qubits by design. The system Hamiltonian is estimated via multi-qubit pulse sequences that implement Ramsey-type interferometry between all neighboring excitation manifolds in the system. The 3-local interaction is coherently tunable over several MHz via the coupler flux biases and can be turned off, which is important for applications in quantum annealing, analog quantum simulation, and gate-model quantum computation.

show abstract

Calibration of Flux Crosstalk in Large-Scale Flux-Tunable Superconducting Quantum Circuits

Cited by 23 publications

References 47 publications

Active Learning of Quantum System Hamiltonians yields Query Advantage

Active Learning of Quantum System Hamiltonians yields Query Advantage

Canceling microwave crosstalk with fixed-frequency qubits

Demonstration of tunable three-body interactions between superconducting qubits

Contact Info

Product

Resources

About