Quantum State Reconstruction in a Noisy Environment via Deep Learning

• URL: http://arxiv.org/abs/2309.11949v1
• Date: Thu, 21 Sep 2023 10:03:30 GMT
• Title: Quantum State Reconstruction in a Noisy Environment via Deep Learning
• Authors: Angela Rosy Morgillo, Stefano Mangini, Marco Piastra and Chiara Macchiavello
• Abstract summary: 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.
• Score: 0.9012198585960443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum noise is currently limiting efficient quantum information processing and computation. In this work, we consider the tasks of reconstructing and classifying quantum states corrupted by the action of an unknown noisy channel using classical feedforward neural networks. By framing reconstruction as a regression problem, we show how such an approach can be used to recover with fidelities exceeding 99% the noiseless density matrices of quantum states of up to three qubits undergoing noisy evolution, and we test its performance with both single-qubit (bit-flip, phase-flip, depolarising, and amplitude damping) and two-qubit quantum channels (correlated amplitude damping). Moreover, 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.

Related papers

• Classical Verification of Quantum Learning Advantages with Noises [0.27930367518472443]
  We propose an efficient classical error rectification algorithm to reconstruct the noise-free results given by the quantum Fourier sampling circuit. We also prove that a classical client with access to the random example oracle can verify the agnostic parity learning results from the noisy quantum prover.
  arXiv  Detail & Related papers  (2024-11-14T06:14:39Z)
• Method for noise-induced regularization in quantum neural networks [0.0]
  We show that noise levels in quantum hardware can be effectively tuned to enhance the ability of quantum neural networks to generalize data. As an example, we consider a medical regression task, where, by tuning the noise level in the circuit, we improved the mean squared error loss by 8%.
  arXiv  Detail & Related papers  (2024-10-25T18:29:42Z)
• 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)
• 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)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics [0.0]
  We address a learning-based quantum error mitigation method, which utilizes deep neural network applied at the postprocessing stage. We concentrate on the simulation of Trotterized dynamics of 2D spin lattice in the regime of high noise, when expectation values of bounded traceless observables are strongly suppressed.
  arXiv  Detail & Related papers  (2023-10-20T09:53:05Z)
• Some aspects of noise in binary classification with quantum circuits [2.2311710049695446]
  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.
  arXiv  Detail & Related papers  (2022-11-11T22:06:17Z)
• Suppressing Amplitude Damping in Trapped Ions: Discrete Weak Measurements for a Non-unitary Probabilistic Noise Filter [62.997667081978825]
  We introduce a low-overhead protocol to reverse this degradation. We present two trapped-ion schemes for the implementation of a non-unitary probabilistic filter against amplitude damping noise. This filter can be understood as a protocol for single-copy quasi-distillation.
  arXiv  Detail & Related papers  (2022-09-06T18:18:41Z)
• 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)
• 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.