Quantum machine learning, in the tools you already use.
PennyLane, Xanadu's open-source framework, makes quantum circuits differentiable so they train inside PyTorch, TensorFlow, and JAX, reaching patterns ordinary networks can't. Over a hundred thousand installs a month.
The Challenge
Today's models hit walls quantum machine learning is built to cross.
Pattern recognition and generative modeling in high-dimensional spaces, like drug design, materials, and risk, keep demanding more data, compute, and energy for smaller returns.
A parameterized quantum circuit computes Fourier-based operations ordinary networks can't cheaply represent. That's a different class of function, not a bigger version of the same one. PennyLane makes those circuits trainable with the gradients you already use.
How Xanadu Helps
The framework and the research behind real quantum machine learning
Quantum generative models
Ordinary generative models struggle to represent certain complex distributions relevant to drug and materials design.
Xanadu researches quantum generative models with Lockheed Martin, using Fourier-based operations ordinary machine learning can't reach.
Quantum inside your existing pipelines
Adding quantum components usually means rewriting the stack and locking into one vendor.
With PennyLane you drop quantum layers into existing PyTorch, TensorFlow, or JAX pipelines and run them on simulators or hardware. No lock-in.
Team enablement
The quantum-ML talent gap slows enterprise adoption.
Xanadu's training brings ML engineers up to speed, the same approach behind the Lockheed Martin Quantum Talent Pipeline.
Contact us
Find where quantum fits in your ML stack
Partner with our researchers to find where a quantum model earns its place, and how to train it in the tools you already use.