Rigetti Computing has demonstrated unsupervised machine learning running on a 19-qubit general purpose superconducting quantum processor.
This used clustering, a fundamental technique in modern data science with applications from advertising and credit scoring to entity resolution and image segmentation. This was running on its Forest quantum development environment that uses the Quil instruction set.
The Rigetti 19Q superconducting processor
The 19Q processor is now available as a programmable backend in Forest, accessible via an API. 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.
The chip was designed and fabricated at Rigetti Computing’s Fab-1 and is an intermediate step towards full 3D integration in upcoming designs.
The latest Forest 1.2 development environment includes 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 and an improved API for moving between synchronous and asynchronous Forest calls.