Quantum Algorithms Database
Quantum Support Vector Machine
A quantum version of the classical support vector machine classification algorithm, offering an exponential speedup under specific idealized conditions.
Year
2014
Inventor(s)
Rebentrost, Mohseni & Lloyd
Speedup Type
Exponential Speedup
Difficulty
★★★★★
The Problem
Classifying data points into categories by finding an optimal separating boundary — one of the most widely used classical machine learning techniques.
How It Works
Uses quantum matrix inversion techniques (related to HHL) to solve the optimization problem underlying support vector machines, assuming the data is already available as an efficiently preparable quantum state.
Real-World Impact
An influential early quantum machine learning proposal, though subsequent research has emphasized that the 'input problem' of loading classical data into quantum states efficiently undermines much of the theoretical speedup in practice.