Posted by on
Tags:
Categories: Machine learning QUANTUM COMPUTING RigettiĀ 

@Rigetti has demonstrated unsupervised machine learning using #19Q, their new 19-#qubit general purpose superconducting quantum processor. We did this with a quantum/classical #hybridalgorithm for clustering developed at Rigetti. Arxiv – Unsupervised Machine Learning on a Hybrid #QuantumComputer #Machinelearning 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.

https://www.google.com/amp/s/www.nextbigfuture.com/2017/12/rigetti-has-a-19-qubit-quantum-computing-system-and-it-runs-unsupervised-machine-learning.html/amp#ampshare=https://www.nextbigfuture.com/2017/12/rigetti-has-a-19-qubit-quantum-computing-system-and-it-runs-unsupervised-machine-learning.html

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.