Related papers: The Quantum Density Matrix and its many uses: From quantum structure to quantum chaos and noisy simulators

The Quantum Density Matrix and its many uses: From quantum structure to quantum chaos and noisy simulators

URL: http://arxiv.org/abs/2303.08738v2
Date: Wed, 16 Aug 2023 13:57:24 GMT
Title: The Quantum Density Matrix and its many uses: From quantum structure to quantum chaos and noisy simulators
Authors: Apoorva D. Patel
Abstract summary: It gives the complete description of a quantum state as well as the observable quantities that can be extracted from it. Its mathematical structure is described, with applications to understanding quantum correlations, illustrating quantum chaos and its unravelling, and developing software simulators for noisy quantum systems with efficient quantum state tomography.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The quantum density matrix generalises the classical concept of probability distribution to quantum theory. It gives the complete description of a quantum state as well as the observable quantities that can be extracted from it. Its mathematical structure is described, with applications to understanding quantum correlations, illustrating quantum chaos and its unravelling, and developing software simulators for noisy quantum systems with efficient quantum state tomography.

Related papers

The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z)
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)
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)
Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo [1.1706540832106251]
Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes. Quantum computing and deep learning offer us potentially useful approaches to study the dynamics of matrix quantum mechanics.
arXiv Detail & Related papers (2021-08-06T05:20:02Z)
Characterizing quantum ensemble using geometric measure of quantum coherence [1.5630592429258865]
We propose a quantumness quantifier for the quantum ensemble. It satisfies the necessary axioms of a bonafide measure of quantumness. We compute the quantumness of a few well-known ensembles.
arXiv Detail & Related papers (2021-04-19T07:37:27Z)
Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z)
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)
Quantum simulation of quantum field theories as quantum chemistry [9.208624182273288]
Conformal truncation is a powerful numerical method for solving generic strongly-coupled quantum field theories. We show that quantum computation could not only help us understand fundamental physics in the lattice approximation, but also simulate quantum field theory methods directly.
arXiv Detail & Related papers (2020-04-28T01:20:04Z)
Scrambling and decoding the charged quantum information [8.497925513299606]
We show how the quantum information in the whole system has been represented by its charge sectors. We discuss possible implications for black hole thought experiments and conjectures about quantum gravity in the dynamical setup.
arXiv Detail & Related papers (2020-03-25T14:32:23Z)
On quantum ensembles of quantum classifiers [0.0]
Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A specific implementation of the quantum ensemble of quantum classifiers, called the accuracy-weighted quantum ensemble, can be fully dequantised. On the other hand, the general quantum ensemble framework is shown to contain the well-known Deutsch-Jozsa algorithm that notably provides a quantum speedup.
arXiv Detail & Related papers (2020-01-29T13:46:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.