Noise-resistant adaptive Hamiltonian learning

• URL: http://arxiv.org/abs/2501.08017v1
• Date: Tue, 14 Jan 2025 11:12:59 GMT
• Title: Noise-resistant adaptive Hamiltonian learning
• Authors: Wenxuan Wang,
• Abstract summary: An adaptive Hamiltonian learning (AHL) model for data analysis and quantum state simulation is proposed to overcome problems such as low efficiency. A noise-resistant quantum neural network (RQNN) based on AHL is developed, which improves the noise robustness of the quantum neural network.
• Score: 30.632260870411177
• License:
• Abstract: Mitigating and reducing noise influence is crucial for obtaining precise experimental results from noisy intermediate-scale quantum (NISQ) devices. In this work, an adaptive Hamiltonian learning (AHL) model for data analysis and quantum state simulation is proposed to overcome problems such as low efficiency and the noise influence of quantum machine learning algorithms. First, an adaptive parameterized quantum circuit with noise resistant ability is constructed by decomposing the unitary operators that include penalty Hamiltonian in the topological quantum system. Then, a noise-resistant quantum neural network (RQNN) based on AHL is developed, which improves the noise robustness of the quantum neural network by updating iterative parameters. Finally, the experiments on Paddle Quantum demonstrate that RQNN can simulate the mathematical function and get accurate classification results on NISQ devices. Compared with the quantum neural network, RQNN ensures high accuracy with the same non-linear discrete data classification under the impact of amplitude damping noise, with an accuracy of 98.00 $\%$. It provides new possibilities for solving practical issues on NISQ devices and also benefits in the resolution of increasingly complicated problems, which will expand the range of potential applications for quantum machine learning models in the future.

Related papers

• Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation [4.2435928520499635]
  In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution. We conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanal Neural Network (QuanNN), and Quantum Transfer Learning (QTL) We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture.
  arXiv  Detail & Related papers  (2025-01-24T11:23:26Z)
• Physics-inspired Machine Learning for Quantum Error Mitigation [15.243176527806126]
  We introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for Machine Learning for Quantum Error Mitigation (ML-QEM) NNAS incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. For deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors.
  arXiv  Detail & Related papers  (2025-01-08T15:07:48Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks [0.4779196219827508]
  Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems. The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
  arXiv  Detail & Related papers  (2024-03-10T15:35:41Z)
• Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis [0.0]
  In quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. Noisy Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a significant challenge for the execution of complex algorithms or quantum machine learning. This paper provides a comprehensive analysis of the impact of noise on QNNs, examining the Mottonen state preparation algorithm under various noise models.
  arXiv  Detail & Related papers  (2023-11-23T15:22:22Z)
• Variational Denoising for Variational Quantum Eigensolver [0.28675177318965045]
  The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems. VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE.
  arXiv  Detail & Related papers  (2023-04-02T14:56:15Z)
• 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)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Entangling Quantum Generative Adversarial Networks [53.25397072813582]
  We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
  arXiv  Detail & Related papers  (2021-04-30T20:38:41Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.