Some aspects of noise in binary classification with quantum circuits

• URL: http://arxiv.org/abs/2211.06492v2
• Date: Mon, 8 May 2023 17:54:38 GMT
• Title: Some aspects of noise in binary classification with quantum circuits
• Authors: Yonghoon Lee and Doga Murat Kurkcuoglu and Gabriel Nathan Perdue
• Abstract summary: We study the effects of a restricted single-qubit noise model inspired by real quantum hardware on the performance of binary classification using quantum circuits. We show that noise in the data can work as a regularizer, implying potential benefits from the noise in certain cases for machine learning problems.
• Score: 2.2311710049695446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We formally study the effects of a restricted single-qubit noise model inspired by real quantum hardware, and corruption in quantum training data, on the performance of binary classification using quantum circuits. We find that, under the assumptions made in our noise model, that the measurement of a qubit is affected only by the noises on that qubit even in the presence of entanglement. Furthermore, when fitting a binary classifier using a quantum dataset for training, we show that noise in the data can work as a regularizer, implying potential benefits from the noise in certain cases for machine learning problems.

Related papers

• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Quantum State Reconstruction in a Noisy Environment via Deep Learning [0.9012198585960443]
  We consider the tasks of reconstructing and classifying quantum states corrupted by an unknown noisy channel. We show how such an approach can be used to recover with fidelities exceeding 99%. We also consider the task of distinguishing between different quantum noisy channels, and show how a neural network-based classifier is able to solve such a classification problem with perfect accuracy.
  arXiv  Detail & Related papers  (2023-09-21T10:03:30Z)
• Taking advantage of noise in quantum reservoir computing [0.0]
  We show that quantum noise can be used to improve the performance of quantum reservoir computing. Our results shed new light into the physical mechanisms underlying quantum devices.
  arXiv  Detail & Related papers  (2023-01-17T11:22:02Z)
• Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise [68.1992787416233]
  We show that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples.
  arXiv  Detail & Related papers  (2022-11-02T05:17:04Z)
• Evaluating the Resilience of Variational Quantum Algorithms to Leakage Noise [6.467585493563487]
  Leakage noise is a damaging source of error that error correction approaches cannot handle. The impact of this noise on the performance of variational quantum algorithms (VQAs) is yet unknown.
  arXiv  Detail & Related papers  (2022-08-10T14:50:14Z)
• Noise effects on purity and quantum entanglement in terms of physical implementability [27.426057220671336]
  Quantum decoherence due to imperfect manipulation of quantum devices is a key issue in the noisy intermediate-scale quantum (NISQ) era. Standard analyses in quantum information and quantum computation use error rates to parameterize quantum noise channels. We propose to characterize the decoherence effect of a noise channel by the physical implementability of its inverse.
  arXiv  Detail & Related papers  (2022-07-04T13:35:17Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Impact of quantum noise on the training of quantum Generative Adversarial Networks [0.0]
  We conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process.
  arXiv  Detail & Related papers  (2022-03-02T10:35:34Z)
• Learning Noise via Dynamical Decoupling of Entangled Qubits [49.38020717064383]
  Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom. We develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates.
  arXiv  Detail & Related papers  (2022-01-26T20:22:38Z)
• A deep learning model for noise prediction on near-term quantum devices [137.6408511310322]
  We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
  arXiv  Detail & Related papers  (2020-05-21T17:47:29Z)
• Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
  Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived. This is the first quantum protocol that can be used against the most general adversaries.
  arXiv  Detail & Related papers  (2020-03-20T17:56:14Z)

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.