Presenting Three New Quantum Machine Learning Algorithms

March 15, 2022 8:00 AM
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QC Ware’s QML team recently published a paper on the arXiv introducing a new approach for quantum linear algebra based on quantum subspace states and presented three new quantum algorithms. In this webinar we will start by highlighting these new algorithms and invite you - during an open ended Q&A session - to share your thoughts on their applications to real-world use cases.

Determinant Sampling

Where is it used:

Determinant sampling is a sampling technique that is used in machine learning to reduce the amount of input data that goes through training without impacting the accuracy of the models.

What’s New: 

We introduce a quantum algorithm that executes the sampling in significantly less steps than what a classical computer would require.

Singular Value Estimation

Where is it used:

Singular Value Decomposition (SVD) is very popular in ML for a variety of tasks including dimensionality reduction, image recognition and noise reduction in images. 

What’s New: 

We introduce a quantum algorithm that executes SVD with a potentially exponential speed-up over classical approaches.

Topological Data Analysis

Where is it used:

Topological Data Analysis (TDA) is an approach to analyzing and extracting information from data sets that are high dimensional, incomplete, and noisy.

What’s New: 

We improve on the circuit depth of quantum circuits required to executed TDA from O(n) to O(logn).


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Iordanis Kerenidis
Iordanis Kerenidis

Senior Vice President, Quantum Algorithms


Yianni Gamvros

Senior Vice President, Sales and Marketing