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Everything about HHL algorithm
Quantum algorithm for solving systems of linear equations
The Harrow–Hassidim–Lloyd (HHL) algorithm is a quantum algorithm for obtaining certain limited information about the solution to a system of linear equations, introduced by Aram Harrow, Avinatan Hassidim, and Seth Lloyd. Specifically, the algorithm estimates quadratic functions of the solution vector to a given system. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: HHL algorithmA 5-minute orientation
- Skim a recent paper: Survey on the Improvement and Application of HHL AlgorithmInfluential work since 2022
- Read the landmark paper: A survey on HHL algorithm: From theory to application in quantum machine learningCited 91 times
- Go sideways: Quantum algorithmA closely related topic
- Go sideways: Quantum machine learningA closely related topic
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A survey on HHL algorithm: From theory to application in quantum machine learning
A Step-by-Step HHL Algorithm Walkthrough to Enhance Understanding of Critical Quantum Computing Concepts
Improved circuit implementation of the HHL algorithm and its simulations on QISKIT
Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
Research on quantum dialogue protocol based on the HHL algorithm
Quantum security of Grain-128/Grain-128a stream cipher against HHL algorithm
Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers
An enhanced hybrid HHL algorithm
🆕 Influential since 2022
Survey on the Improvement and Application of HHL Algorithm
HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids
Limitations of the Macaulay matrix approach for using the HHL algorithm to solve multivariate polynomial systems
State-Domain Equations and Their Quantum Computing Solution Based HHL Algorithm
The classical iterative HHL-based hemodynamic simulation quantum linear equation algorithm for abdominal aortic aneurysm
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