Related Experiment Video
Updated: Sep 12, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
Faster quantum subroutine for matrix chain multiplication via Chebyshev approximation
Xinying Li1, Pei-Lin Zheng1, Chengkang Pan1
1China Mobile Research Institute, Beijing, 100053, China.
Abstract:
Matrix operations are crucial to various computational tasks in various fields, and quantum computing offers a promising avenue to accelerate these operations. We present a quantum matrix multiplication (QMM) algorithm that employs amplitude encoding and combines quantum walks with Chebyshev polynomial approximation to achieve quadratic acceleration for matrix chain multiplication where the same matrix is applied K times while maintaining logarithmic complexity in matrix dimension and precision. The algorithm can be applied to any complex matrix. Furthermore, we discuss integrating our QMM algorithm as a subroutine in other matrix operations and propose strategies to optimize QMM for matrices with large condition numbers with numerical simulation.
Related Concept Videos
Chebyshev's Theorem to Interpret Standard Deviation
Linear Approximation in Time Domain
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
Fast Decoupled and DC Powerflow
Vector Algebra: Method of Components
In many applications, the magnitudes and directions of...
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
Scalar and Vector Triple Products
The scalar triple product is the dot product of a vector with the cross product of two vectors....

