Estimating Quantum Mutual Information Through a Quantum Neural Network

• URL: http://arxiv.org/abs/2306.14566v2
• Date: Sun, 11 Feb 2024 07:12:21 GMT
• Title: Estimating Quantum Mutual Information Through a Quantum Neural Network
• Authors: Myeongjin Shin, Junseo Lee, Kabgyun Jeong
• Abstract summary: We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) QMINE estimates von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.
• Score: 0.8988769052522807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. The QMINE proposed here basically utilizes a technique of quantum neural networks (QNNs), to minimize a loss function that determines the von Neumann entropy, and thus quantum mutual information, which is believed more powerful to process quantum datasets than conventional neural networks due to quantum superposition and entanglement. To create a precise loss function, we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum analog of the classical Donsker-Varadhan representation. By exploiting a parameter shift rule on parameterized quantum circuits, we can efficiently implement and optimize the QNN and estimate the quantum entropies using the QMINE technique. Furthermore, numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.

Related papers

• Measurement-based quantum machine learning [0.0]
  A quantum neural network (QNN) is an object that extends the notion of a classical neural network to quantum models for quantum data. We propose a universal QNN in this framework which we call the multiple-triangle ansatz (MuTA)
  arXiv  Detail & Related papers  (2024-05-14T05:17:01Z)
• Randomness-enhanced expressivity of quantum neural networks [7.7991930692137466]
  We propose a novel approach to enhance the expressivity of QNNs by incorporating randomness into quantum circuits. We prove that our approach can accurately approximate arbitrary target operators using Uhlmann's theorem for majorization. We find the expressivity of QNNs is enhanced by introducing randomness for multiple learning tasks, which could have broad application in quantum machine learning.
  arXiv  Detail & Related papers  (2023-08-09T07:17:13Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Quantum Semantic Communications for Resource-Efficient Quantum Networking [52.3355619190963]
  This letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations. The proposed framework achieves approximately 50-75% reduction in quantum communication resources needed, while achieving a higher quantum semantic fidelity.
  arXiv  Detail & Related papers  (2022-05-05T03:49:19Z)
• QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks [71.14713348443465]
  We introduce a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
  arXiv  Detail & Related papers  (2021-10-06T14:44:51Z)
• Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases [2.1465372441653354]
  Quantum neural networks tailored to recognize specific features of quantum states by combining unitary operations, measurements and feedforward promise to require fewer measurements and to tolerate errors. We realize a quantum convolutional neural network (QCNN) on a 7-qubit superconducting quantum processor to identify symmetry-protected topological phases of a spin model characterized by a non-zero string order parameter. We find that, despite being composed of finite-fidelity gates itself, the QCNN recognizes the topological phase with higher fidelity than direct measurements of the string order parameter for the prepared states.
  arXiv  Detail & Related papers  (2021-09-13T12:32:57Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)
• Neural Error Mitigation of Near-Term Quantum Simulations [0.0]
  We introduce $textitneural error mitigation$, a novel method that uses neural networks to improve estimates of ground states and ground-state observables. Our results show that neural error mitigation improves the numerical and experimental VQE computation to yield low-energy errors. Our method is a promising strategy for extending the reach of near-term quantum computers to solve complex quantum simulation problems.
  arXiv  Detail & Related papers  (2021-05-17T18:00:57Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices [0.0]
  We define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register.
  arXiv  Detail & Related papers  (2020-03-11T12:01:03Z)

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.