According to the company, they demonstrated unsupervised machine learning on their new 19-qubit general-purpose superconducting quantum processor, the 19Q. Unlike supervised machine learning, where the output of an algorithm is known based on "labeled" training data, unsupervised machine learning offers the potential to solve complex problems using just input data, with no training data set and where outcomes are unknown.

Unsupervised machine learning is currently used in a variety of applications, including bioinformatics for sequence analysis and genetic clustering, in data mining for sequence and pattern mining, in medical imaging for image segmentation, and in computer vision for object recognition.

The most common method used for unsupervised machine learning is cluster analysis to find hidden patterns within the data. This exploratory data analysis sorts different objects into groups such that the degree of association between two objects is greatest if they belong to the same group and minimal otherwise.

In their demonstration, the Rigetti team used a quantum/classical hybrid algorithm for clustering that they developed. According to Rigetti, the 19 qubits in their processor make this the largest such 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," says Will Zeng, product lead for Forest.

"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," says Zeng.

Beating the best classical benchmarks will require more qubits and better performance, says the company. But hybrid proofs-of-concept such as this "form the basis of valuable applications for the first quantum computers."

The company's 19Q processor is available as a programmable backend in Forest. The company also announced the latest version of its quantum development environment - Forest 1.2.

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