Next-generation AI
Unlocking efficient machine learning with quantum hardware.
Modern AI's success is undeniable, but the underlying cost, such as the need for big data and hyperscale computing, is unsustainable.
Problem
The fundamental bottleneck in machine learning is to find a meaningful representation of data. If not relying on vast scale, good representations have to be cleverly crafted, and many promising strategies suffer from being computationally intractable.
Solution
Xanadu research leverages a surprising insight: The mathematical structure underpinning Shor’s algorithm provides a path towards powerful representations for machine learning. We are developing a portfolio of quantum algorithms that unlock this new approach to AI.
Solving the resource issue could make quantum hardware the "new GPU" that powers more sustainable and data-efficient machine learning.
Learn More
- Spectral methods: crucial for machine learning, natural for quantum computers?
March 25, 2026 - Probabilistic modeling over permutations using quantum computers
March 23, 2026 - Solving approximate hidden subgroup problems: quantum heuristics to detect weak entanglement
March 16, 2026 - Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits
March 4, 2026 - Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
August 30, 2024 - Better than classical? The subtle art of benchmarking quantum machine learning models
March 11, 2024 - Generalization despite overfitting in quantum machine learning models
December 20, 2023
Where quantum meets industry
Xanadu is focused on building useful quantum computers to solve the world's most intractable computational problems.
Advance your applications with quantum
Partner with our leading researchers to explore how our technology can address your most demanding applications.