Unsupervised Machine Learning on Rigetti 19Q with Forest 1.2
by Will Zeng, Rigetti Computing
We are excited to share that our team has demonstrated unsupervised machine learning using 19Q, our new 19-qubit general purpose superconducting quantum processor. We did this with a quantum/classical hybrid algorithm for clustering developed at Rigetti.
Clustering is a fundamental technique in modern data science with applications from advertising and credit scoring to entity resolution and image segmentation. The 19 qubits in our processor make this the largest hybrid demonstration to date.
We believe in the fundamental power of hybrid quantum/classical computing. Our product Forest, a quantum development environment, is built upon this approach, with the Quil instruction set as its foundation. Today’s results are a demonstration of that power. We show that our algorithm has robustness to quantum processor noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent hardware imperfections.
Beating the best classical benchmarks will require more qubits and better performance, but hybrid proofs-of-concept like this one form the basis of valuable applications for the first quantum computers.
You can read more about the demo in our research paper.
19-Qubit Processor Now Available
Our 19Q processor is now available as a programmable backend in Forest. You can apply for access today. Learning to program Rigetti quantum computers on Forest is extremely easy. For example, just a few lines of Python initializes a connection to the quantum processor unit (QPU) and generates an entangled state between qubits with indices 0 and 1.
Specifications and performance metrics for 19Q are here. The chip was designed and fabricated at Rigetti Computing’s Fab-1, which has allowed us to rapidly build and improve our chips. The processor’s architecture is an intermediate step towards full 3D integration in upcoming designs.
New Forest Release
Forest 1.2, available today, includes important updates and upgrades based on your feedback:
- Customizable noise models in the Quantum Virtual Machine that allow you to simulate arbitrary quantum channels to study the robustness of algorithms to processor noise.
- Automatic compilation to the 19Q gate set and qubit layout.
- An improved API for moving between synchronous and asynchronous Forest calls.
- Additional updates to make Forest and its open source libraries of pyQuil and grove as easy to install and use as possible.
Thank you to the Forest community for your feedback and contributions. We are excited to see what you will do with 19Q.