Improved Tomographic Estimates by Specialised Neural Networks

• URL: http://arxiv.org/abs/2211.11655v2
• Date: Tue, 27 Jun 2023 09:15:05 GMT
• Title: Improved Tomographic Estimates by Specialised Neural Networks
• Authors: Massimiliano Guarneri, Ilaria Gianani, Marco Barbieri and Andrea Chiuri
• Abstract summary: We show that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. We demonstrate that a stable and reliable operation is achievable by training the network only with simulated data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Characterization of quantum objects, being them states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real-life components. To this end, Machine Learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here we show that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. We applied our technique to quantum process tomography for the characterization of several quantum channels. We demonstrate that a stable and reliable operation is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.

Related papers

• Disentanglement process in dephasing channel with machine learning [0.0]
  We employ a machine-learning approach to investigate the disentanglement process in two-qubit systems in the presence of dephasing noise. Specialized ANN algorithms, tailored for classifying states and entanglement, demonstrate excellent performance using only a subset of tomographic features.
  arXiv  Detail & Related papers  (2024-10-28T20:18:04Z)
• Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection [3.2927352068925444]
  deepfake technologies pose challenges to privacy, security, and information integrity. This paper introduces a Quantum-Trained Convolutional Neural Network framework designed to enhance the detection of deepfake audio.
  arXiv  Detail & Related papers  (2024-10-11T20:52:10Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• MARS: Meta-Learning as Score Matching in the Function Space [79.73213540203389]
  We present a novel approach to extracting inductive biases from a set of related datasets. We use functional Bayesian neural network inference, which views the prior as a process and performs inference in the function space. Our approach can seamlessly acquire and represent complex prior knowledge by metalearning the score function of the data-generating process.
  arXiv  Detail & Related papers  (2022-10-24T15:14:26Z)
• Neural network enhanced measurement efficiency for molecular groundstates [63.36515347329037]
  We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians. We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
  arXiv  Detail & Related papers  (2022-06-30T17:45:05Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Variational learning for quantum artificial neural networks [0.0]
  We first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons.
  arXiv  Detail & Related papers  (2021-03-03T16:10:15Z)
• Mixed State Entanglement Classification using Artificial Neural Networks [0.0]
  Separable Neural Network Quantum States employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. We extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems.
  arXiv  Detail & Related papers  (2021-02-11T14:59:24Z)

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.