Rigetti Computing has demonstrated the use of quantum machine learning for clustering on a 19-qubit chip via the cloud API Forest.

Rigetti Computing has demonstrated unsupervised machine learning on a 19-qubit chip using a hybrid quantum algorithm.

On Morgan Stanley’s list of 11 companies with the best quantum computing roadmaps, Rigetti Computing is in sixth place, having just edged out Lockheed Martin and Airbus.

If quantum computing is new to you, here are 11 Key Facts About Quantum Computers to help you get caught up.

To the surprise of most, Rigetti is forcing its way into the quantum race and taking on tech giants like IBM, Alphabet, and Microsoft while still only being in its early funding stage (Series B) and with less than 100 employees.

This Berkeley-based startup was founded in 2013 by an ex-IBM researcher and has just announced their first demo of unsupervised machine learning on a 19-qubit superconducting chip.

Quantum Computing set to Boost Neural Network Learning Capabilities



“This is going to be a very large industry,” Rigetti founder Chad Rigetti told Wired. “Every major organization in the world will have to have a strategy for how to use this technology.”

Developing quantum hardware and software, Rigetti Computing is on a mission “to build the world’s most powerful computer”, and it seems that this strategy is finally paying off.

Toward its goal to make quantum chips a market reality, Rigetti has done something many bigger companies have not been able to do.

In a post published Monday, December 18th in Medium, the director of software and apps at Rigetti, Will Zheng, announced the first demo of unsupervised machine learning using a superconducting quantum chip.

“Clustering is a fundamental technique in modern data science with applications from advertising and credit scoring to entity resolution and image segmentation,” said Zeng. “The 19 qubits in our processor make this the largest hybrid demonstration to date.”

Rigetti’s QPU (quantum processor unit), consists of 19 superconducting qubits and is made accessible via a cloud API Forest.

Available for developers, “Forest” is a public quantum/classical computing environment, which includes the programming language Quil (quantum instruction language).

Quantum Machine Learning

Quantum learning is the domain at the intersection between quantum computing and AI, which first appeared in the 1990s.

The main goal of quantum learning is to develop quantum machine learning algorithms for situations where there are no effective classical learning alternatives.

Quantum neural networks are multilayer networks of quantum architecture which use superpositioning and quantum entanglement to greatly increase their processing power. It is within the development of these complex hidden layers that has led to Rigetti’s central way of managing their (unsupervised) learning.

To achieve this, Rigetti researchers used a quantum/classical hybrid clustering algorithm developed in-house. Clustering refers to machine-learning algorithms used to arrange data into similar clusters.

However, according to MIT, “the demonstration does not, however, mean quantum computers are poised to revolutionize AI. Quantum computers are so exotic that no one quite knows what the killer apps might be. Rigetti’s algorithm, for instance, isn’t of any practical use, and it isn’t entirely clear how useful it would be to perform clustering tasks on a quantum machine.”

Rigetti researchers published a white paper detailing the unsupervised learning demo.

Quantum computing has been painted as a world-changing development since the early 90s but as of yet has shown no large-scale effect on any industry.

Do you think quantum computing will ever reach the height that many scientists have predicted? Or will it forever be relegated to research labs and future developments? Let us know your opinion in the comments section below.