Sign up to save your podcasts
Or
Share An Improved Method for Quantum Matrix Multiplication: Main Procedure
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
An Improved Method for Quantum Matrix Multiplication: Main Procedure
This story was originally published on HackerNoon at: https://hackernoon.com/an-improved-method-for-quantum-matrix-multiplication-main-procedure.
Quantum algorithms achieve exponential precision improvements in matrix applications using Chebyshev polynomials
Check more stories related to futurism at: https://hackernoon.com/c/futurism.
You can also check exclusive content about #quantum-computing, #quantum-matrix, #quantum-algorithms, #quantum-phase-estimation, #chebyshev-polynomials, #linear-systems, #quantum-matrix-multiplication, #eigenvalue-estimation, and more.
This story was written by: @eigenvector. Learn more about this writer by checking @eigenvector's about page,
and for more stories, please visit hackernoon.com.
Enhanced quantum algorithms utilize Chebyshev polynomials to exponentially improve precision and efficiency in matrix applications, advancing beyond traditional HHL algorithm limitations.
View all episodes
By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/an-improved-method-for-quantum-matrix-multiplication-main-procedure.
Quantum algorithms achieve exponential precision improvements in matrix applications using Chebyshev polynomials
Check more stories related to futurism at: https://hackernoon.com/c/futurism.
You can also check exclusive content about #quantum-computing, #quantum-matrix, #quantum-algorithms, #quantum-phase-estimation, #chebyshev-polynomials, #linear-systems, #quantum-matrix-multiplication, #eigenvalue-estimation, and more.
This story was written by: @eigenvector. Learn more about this writer by checking @eigenvector's about page,
and for more stories, please visit hackernoon.com.
Enhanced quantum algorithms utilize Chebyshev polynomials to exponentially improve precision and efficiency in matrix applications, advancing beyond traditional HHL algorithm limitations.