On exploring practical potentials of quantum auto-encoder with advantages

06/29/2021
by   Yuxuan Du, et al.
0

Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics, celebrated by the ability to extract low-dimensional patterns from quantum states living in the high-dimensional space. Despite its attractive properties, little is known about the practical applications of QAE with provable advantages. To address these issues, here we prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state with the low-rank property. With this regard, we devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks, respectively. Notably, all of these protocols are scalable and can be readily executed on near-term quantum machines. Moreover, we prove that the error bounds of the proposed QAE-based methods outperform those in previous literature. Numerical simulations collaborate with our theoretical analysis. Our work opens a new avenue of utilizing QAE to tackle various quantum physics and quantum information processing problems in a scalable way.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

04/23/2021

Low Rank Approximation in Simulations of Quantum Algorithms

Simulating quantum algorithms on classical computers is challenging when...
11/04/2017

Provable quantum state tomography via non-convex methods

With nowadays steadily growing quantum processors, it is required to dev...
12/22/2021

Parametrized Complexity of Quantum Inspired Algorithms

Motivated by recent progress in quantum technologies and in particular q...
03/31/2021

Towards understanding the power of quantum kernels in the NISQ era

A key problem in the field of quantum computing is understanding whether...
05/24/2019

Quantum-inspired algorithms in practice

We study the practical performance of quantum-inspired algorithms for re...
12/18/2019

Quantum-inspired annealers as Boltzmann generators for machine learning and statistical physics

Quantum simulators and processors are rapidly improving nowadays, but th...
09/22/2020

Control dynamics using quantum memory

We propose a new quantum numerical scheme to control the dynamics of a q...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.