Accelerating variational quantum algorithms with multiple quantum processors

06/24/2021
by   Yuxuan Du, et al.
0

Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages over classical methods. Nevertheless, modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large-volume data. As such, to better exert the superiority of VQAs, it is of great significance to improve their runtime efficiency. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue. Specifically, in QUDIO, a classical central server partitions the learning problem into multiple subproblems and allocate them to multiple local nodes where each of them consists of a quantum processor and a classical optimizer. During the training procedure, all local nodes proceed parallel optimization and the classical server synchronizes optimization information among local nodes timely. In doing so, we prove a sublinear convergence rate of QUDIO in terms of the number of global iteration under the ideal scenario, while the system imperfection may incur divergent optimization. Numerical results on standard benchmarks demonstrate that QUDIO can surprisingly achieve a superlinear runtime speedup with respect to the number of local nodes. Our proposal can be readily mixed with other advanced VQAs-based techniques to narrow the gap between the state of the art and applications with quantum advantage.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 19

09/16/2019

Near-term quantum algorithms for linear systems of equations

Solving linear systems of equations is an essential component in science...
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...
04/30/2020

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical...
05/10/2022

Fundamental limitations on optimization in variational quantum algorithms

Exploring quantum applications of near-term quantum devices is a rapidly...
06/08/2020

The Snake Optimizer for Learning Quantum Processor Control Parameters

High performance quantum computing requires a calibration system that le...
02/18/2020

Predicting Many Properties of a Quantum System from Very Few Measurements

Predicting properties of complex, large-scale quantum systems is essenti...
08/26/2016

An Octree-Based Approach towards Efficient Variational Range Data Fusion

Volume-based reconstruction is usually expensive both in terms of memory...
This week in AI

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