Quantum Cross Entropy and Maximum Likelihood Principle

• URL: http://arxiv.org/abs/2102.11887v1
• Date: Tue, 23 Feb 2021 19:00:06 GMT
• Title: Quantum Cross Entropy and Maximum Likelihood Principle
• Authors: Zhou Shangnan, Yixu Wang
• Abstract summary: Quantum machine learning is an emerging field at the intersection of machine learning and quantum computing. We define its quantum generalization, the quantum cross entropy, and investigate its relations with the quantum fidelity and the maximum likelihood principle.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum machine learning is an emerging field at the intersection of machine learning and quantum computing. Classical cross entropy plays a central role in machine learning. We define its quantum generalization, the quantum cross entropy, and investigate its relations with the quantum fidelity and the maximum likelihood principle. We also discuss its physical implications on quantum measurements.

Related papers

• Quantum decoherence from complex saddle points [0.0]
  Quantum decoherence is the effect that bridges quantum physics to classical physics. We present some first-principle calculations in the Caldeira-Leggett model. We also discuss how to extend our work to general models by Monte Carlo calculations.
  arXiv  Detail & Related papers  (2024-08-29T15:35:25Z)
• Evolution of Quantum Resources in Quantum-walk-based Search Algorithm [3.604186493583444]
  We consider the effects of quantum coherence and quantum entanglement for the quantum walk search on the complete bipartite graph. First, we numerically show the complementary relationship between the success probability and the two quantum resources. At last, we discuss the role played by generalized depolarizing noises and find that it would influence the dynamics of success probability and quantum coherence sharply.
  arXiv  Detail & Related papers  (2023-09-30T12:16:28Z)
• 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)
• Unraveling the Mystery of Quantum Measurement with A New Space-Time Approach to Relativistic Quantum Mechanics [9.116661570248171]
  Quantum measurement is a fundamental concept in the field of quantum mechanics. Despite its significance, four fundamental issues continue to pose significant challenges to the broader application of quantum measurement. We employ a new space-time approach to relativistic quantum mechanics to address these issues systematically.
  arXiv  Detail & Related papers  (2023-06-01T13:25:08Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Quantum Data Compression and Quantum Cross Entropy [0.0]
  We show that quantum cross entropy acts as the compression rate for sub-optimal quantum source coding. This reveals that quantum cross entropy can effectively serve as a loss function in quantum machine learning algorithms.
  arXiv  Detail & Related papers  (2021-06-25T18:00:33Z)
• Quantum Entropic Causal Inference [30.939150842529052]
  We put forth a new theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. We apply our proposed framework to an experimentally relevant scenario of identifying message senders on quantum noisy links.
  arXiv  Detail & Related papers  (2021-02-23T15:51:34Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Quantum information spreading in a disordered quantum walk [50.591267188664666]
  We design a quantum probing protocol using Quantum Walks to investigate the Quantum Information spreading pattern. We focus on the coherent static and dynamic disorder to investigate anomalous and classical transport. Our results show that a Quantum Walk can be considered as a readout device of information about defects and perturbations occurring in complex networks.
  arXiv  Detail & Related papers  (2020-10-20T20:03:19Z)

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.