Neural network based time-resolved state tomography of superconducting qubits

• URL: http://arxiv.org/abs/2312.07958v2
• Date: Fri, 15 Mar 2024 10:40:23 GMT
• Title: Neural network based time-resolved state tomography of superconducting qubits
• Authors: Ziyang You, Jiheng Duan, Wenhui Huang, Libo Zhang, Song Liu, Youpeng Zhong, Hou Ian,
• Abstract summary: We introduce a time-resolved neural network capable of full-state tomography for individual qubits. This scalable approach, with a dedicated module per qubit, mitigated readout error by an order of magnitude under low signal-to-noise ratios.
• Score: 9.775471166288503
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Superconducting qubits have emerged as a premier platform for large-scale quantum computation, yet the fidelity of state readout is often hindered by random noise and crosstalk, especially in multi-qubit systems. While neural networks trained on labeled data have shown promise in reducing crosstalk effects during readout, their current capabilities are limited to binary discrimination of joint-qubit states due to architectural constraints. Here we introduce a time-resolved modulated neural network capable of full-state tomography for individual qubits, enabling detailed time-resolved measurements like Rabi oscillations. This scalable approach, with a dedicated module per qubit, mitigated readout error by an order of magnitude under low signal-to-noise ratios and substantially reduced variance in Rabi oscillation measurements. This advancement bolsters quantum state discrimination with neural networks, and propels the development of next-generation quantum processors with enhanced performance and scalability.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Low-latency machine learning FPGA accelerator for multi-qubit-state discrimination [1.6773398825542363]
  Measuring a qubit state is a fundamental yet error-prone operation in quantum computing. Here, we utilize an integrated approach to deploy neural networks onto field-programmable gate arrays (FPGA) We demonstrate that implementing a fully connected neural network accelerator for multi-qubit readout is advantageous.
  arXiv  Detail & Related papers  (2024-07-04T11:34:43Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Machine Learning based Discrimination for Excited State Promoted Readout [0.0]
  A technique known as excited state promoted (ESP) readout was proposed to reduce this effect. In this work, we use readout data from five-qubit IBMQ devices to measure the effectiveness of using deep neural networks.
  arXiv  Detail & Related papers  (2022-10-16T16:09:46Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Monitoring fast superconducting qubit dynamics using a neural network [0.0]
  Weak measurements of a superconducting qubit produce noisy voltage signals that are weakly correlated with the qubit state. Traditional methods require slow qubit dynamics and substantial prior information in the form of calibration experiments. Here, we experimentally demonstrate an alternative method for accurately tracking rapidly driven superconducting qubit trajectories.
  arXiv  Detail & Related papers  (2021-08-26T20:25:31Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Anti-crosstalk high-fidelity state discrimination for superconducting qubits [3.886248147345244]
  Current methods for classifying states of single qubit in a superconducting multi-qubit system produce fidelities lower than expected due to the existence of crosstalk. We make the digital signal processing (DSP) system used in measurement into a shallow neural network and train it to be an optimal classifier to reduce the impact of crosstalk.
  arXiv  Detail & Related papers  (2021-03-16T10:39:42Z)
• Scalable Neural Decoder for Topological Surface Codes [0.0]
  We present a neural network based decoder for a family of stabilizer codes subject to noise and syndrome measurement errors. The key innovation is to autodecode error syndromes on small scales by shifting a preprocessing window over the underlying code. We show that such a preprocessing step allows to effectively reduce the error rate by up to two orders of magnitude in practical applications.
  arXiv  Detail & Related papers  (2021-01-18T19:02:09Z)

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.