Quantum Computing Startup Demonstrates Unsupervised Machine Learning Using Their 19-Qubit Processor

If you’re familiar with machine learning, you know that clustering is a fundamental technique for today’s data scientist. Clustering’s applications are broad and useful to organizations from credit card companies to ad agencies. Recently quantum computing startup Rigetti Computing proved a hybrid quantum computer running their 19Q (a 19-qubit processor) was capable of cluster analysis.

The company’s scientists published a paper about the demonstration called “Unsupervised Machine Learning on a Hybrid Quantum Computer.” The abstract lays out the problem space and their approach better than I could:

Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.

But What’s In It For Me?

The Rigetti team has done a favor for both the quantum computing and artificial intelligence communities. (In my opinion, quantum computers are eventually going to power a new era of true artificial intelligence. The first step down that road is to advance machine learning.) But what I’m most excited about is that the 19Q processor is now available as a programmable back end in Forest, Rigetti’s developer environment. Anyone can apply for access, which you know I love.

Let’s show them what a great decision that was. Get thee to your local computing machine and check it out. Apply for access, and play around with this amazing system. It’s Python based and easy to wrap your head around, so do not tarry. Avail thyself of the 19Q forthwith.

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