# 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?](https://arxiv.org/abs/2603.24654)  
  [March 25, 2026](https://arxiv.org/abs/2603.24654)
- [Probabilistic modeling over permutations using quantum computers](https://arxiv.org/abs/2603.22401v1)  
  [March 23, 2026](https://arxiv.org/abs/2603.22401v1)
- [Solving approximate hidden subgroup problems: quantum heuristics to detect weak entanglement](https://arxiv.org/abs/2603.15733)  
  [March 16, 2026](https://arxiv.org/abs/2603.15733)
- [Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits](https://arxiv.org/abs/2503.02934)  
  [March 4, 2026](https://arxiv.org/abs/2503.02934)
- [Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data](https://arxiv.org/abs/2409.00172)  
  [August 30, 2024](https://arxiv.org/abs/2409.00172)
- [Better than classical? The subtle art of benchmarking quantum machine learning models](https://arxiv.org/abs/2403.07059)  
  [March 11, 2024](https://arxiv.org/abs/2403.07059)
- [Generalization despite overfitting in quantum machine learning models](https://quantum-journal.org/papers/q-2023-12-20-1210/)  
  [December 20, 2023](https://quantum-journal.org/papers/q-2023-12-20-1210/)

## 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.