Fast Bayesian tomography of a two-qubit gate set in silicon

• URL: http://arxiv.org/abs/2107.14473v1
• Date: Fri, 30 Jul 2021 07:56:04 GMT
• Title: Fast Bayesian tomography of a two-qubit gate set in silicon
• Authors: T. J. Evans, W. Huang, J. Yoneda, R. Harper, T. Tanttu, K. W. Chan, F. E. Hudson, K. M. Itoh, A. Saraiva, C. H. Yang, A. S. Dzurak, and S. D. Bartlett
• Abstract summary: We introduce a Bayesian approach to self-consistent process tomography, called fast Bayesian tomography (FBT) We experimentally demonstrate its performance in characterising a two-qubit gate set on a silicon-based spin qubit device.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benchmarking and characterising quantum states and logic gates is essential in the development of devices for quantum computing. We introduce a Bayesian approach to self-consistent process tomography, called fast Bayesian tomography (FBT), and experimentally demonstrate its performance in characterising a two-qubit gate set on a silicon-based spin qubit device. FBT is built on an adaptive self-consistent linearisation that is robust to model approximation errors. Our method offers several advantages over other self-consistent tomographic methods. Most notably, FBT can leverage prior information from randomised benchmarking (or other characterisation measurements), and can be performed in real time, providing continuously updated estimates of full process matrices while data is acquired.

Related papers

• PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture [46.266960248570086]
  This study tackles the quadratic complexity of the self-attention mechanism by introducing a complexity local attention mechanism for effective feature aggregation. We also introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel. We show that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.
  arXiv  Detail & Related papers  (2024-08-10T10:16:03Z)
• AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer [54.713778961605115]
  Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community. We propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer.
  arXiv  Detail & Related papers  (2024-07-17T18:38:48Z)
• Learning to rank quantum circuits for hardware-optimized performance enhancement [0.0]
  We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits. We compare our method to two common approaches: random layout selection and a publicly available baseline called Mapomatic. Our best model leads to a $1.8times$ reduction in selection error when compared to the baseline approach and a $3.2times$ reduction when compared to random selection.
  arXiv  Detail & Related papers  (2024-04-09T18:00:01Z)
• DB-LLM: Accurate Dual-Binarization for Efficient LLMs [83.70686728471547]
  Large language models (LLMs) have significantly advanced the field of natural language processing. Existing ultra-low-bit quantization always causes severe accuracy drops. We propose a novel Dual-Binarization method for LLMs, namely DB-LLM.
  arXiv  Detail & Related papers  (2024-02-19T09:04:30Z)
• Characterizing non-Markovian Quantum Process by Fast Bayesian Tomography [0.0]
  characterization of non-Markovian error poses a challenge to current quantum process tomography techniques. Fast Bayesian Tomography (FBT) is a self-consistent gate set tomography protocol that can be bootstrapped from earlier characterization knowledge. We introduce two experimental protocols for FBT to diagnose the non-Markovian behavior of two-qubit systems on silicon quantum dots.
  arXiv  Detail & Related papers  (2023-07-23T23:10:18Z)
• Read Pointer Meters in complex environments based on a Human-like Alignment and Recognition Algorithm [16.823681016882315]
  We propose a human-like alignment and recognition algorithm to overcome these problems. A Spatial Transformed Module(STM) is proposed to obtain the front view of images in a self-autonomous way. A Value Acquisition Module(VAM) is proposed to infer accurate meter values by an end-to-end trained framework.
  arXiv  Detail & Related papers  (2023-02-28T05:37:04Z)
• 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)
• Dispersive qubit readout with machine learning [0.08399688944263842]
  Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. A recently introduced measurement protocol uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9%. We use machine learning-based classification algorithms to extract information from this critical dynamics.
  arXiv  Detail & Related papers  (2021-12-10T04:25:43Z)
• Performance of teleportation-based error correction circuits for bosonic codes with noisy measurements [58.720142291102135]
  We analyze the error-correction capabilities of rotation-symmetric codes using a teleportation-based error-correction circuit. We find that with the currently achievable measurement efficiencies in microwave optics, bosonic rotation codes undergo a substantial decrease in their break-even potential.
  arXiv  Detail & Related papers  (2021-08-02T16:12:13Z)
• Randomized linear gate set tomography [0.0]
  We introduce randomized linear gate set tomography, an easy-to-implement gate set tomography procedure. We demonstrate the performance of our scheme through simulated examples and experiments done on the IBM Quantum Experience Platform.
  arXiv  Detail & Related papers  (2020-10-23T08:46:11Z)
• Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
  We study two different methods to prepare excited states on a quantum computer. We benchmark these techniques on emulated and real quantum devices. These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
  arXiv  Detail & Related papers  (2020-09-28T17:21:25Z)

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.