Automated extraction of capacitive coupling for quantum dot systems

• URL: http://arxiv.org/abs/2301.08654v2
• Date: Thu, 25 May 2023 14:56:05 GMT
• Title: Automated extraction of capacitive coupling for quantum dot systems
• Authors: Joshua Ziegler, Florian Luthi, Mick Ramsey, Felix Borjans, Guoji Zheng, Justyna P. Zwolak
• Abstract summary: Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. Near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices.
• Score: 0.06775401033588706
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning to a regime suitable for qubit operations.

Related papers

• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Autonomous Bootstrapping of Quantum Dot Devices [26.47874938214435]
  We propose a bootstrapping algorithm for initializing a depletion mode QD device in preparation for subsequent phases of tuning. We demonstrate that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global state in under 8 minutes with a success rate of 96 %.
  arXiv  Detail & Related papers  (2024-07-29T14:47:46Z)
• Data needs and challenges for quantum dot devices automation [0.8768081687253924]
  Gate-defined quantum dots are a promising system for realizing scalable, coupled qubit systems. However, present-day quantum dot devices suffer from imperfections that must be accounted for. We aim to provide guidance and inspiration to researchers invested in automation efforts.
  arXiv  Detail & Related papers  (2023-12-21T22:27:32Z)
• QDA$^2$: A principled approach to automatically annotating charge stability diagrams [1.2437226707039448]
  Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing. Large configuration spaces and inherent noise make tuning of QD devices a nontrivial task. QD auto-annotator is a classical algorithm for automatic interpretation and labeling of experimentally acquired data.
  arXiv  Detail & Related papers  (2023-12-18T13:52:18Z)
• Quantum process tomography of continuous-variable gates using coherent states [49.299443295581064]
  We demonstrate the use of coherent-state quantum process tomography (csQPT) for a bosonic-mode superconducting circuit. We show results for this method by characterizing a logical quantum gate constructed using displacement and SNAP operations on an encoded qubit.
  arXiv  Detail & Related papers  (2023-03-02T18:08:08Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Potential and limitations of quantum extreme learning machines [55.41644538483948]
  We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements. Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
  arXiv  Detail & Related papers  (2022-10-03T09:32:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Colloquium: Advances in automation of quantum dot devices control [0.0]
  Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques.
  arXiv  Detail & Related papers  (2021-12-17T07:41:47Z)
• Analytical and experimental study of center line miscalibrations in M\o lmer-S\o rensen gates [51.93099889384597]
  We study a systematic perturbative expansion in miscalibrated parameters of the Molmer-Sorensen entangling gate. We compute the gate evolution operator which allows us to obtain relevant key properties. We verify the predictions from our model by benchmarking them against measurements in a trapped-ion quantum processor.
  arXiv  Detail & Related papers  (2021-12-10T10:56:16Z)
• Strong parametric dispersive shifts in a statically decoupled multi-qubit cavity QED system [0.4915375958667782]
  Cavity quantum electrodynamics (QED) with in-situ tunable interactions is important for developing novel systems for quantum simulation and computing. Here, we couple two transmon qubits to a lumped-element cavity through a shared dc-SQUID. We show that by parametrically driving the SQUID with an oscillating flux it is possible to independently tune the interactions between either of the qubits and the cavity dynamically.
  arXiv  Detail & Related papers  (2021-03-16T18:46:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.