Unsupervised Machine Learning On A Hybrid Quantum Computer

Unsupervised Machine Learning on a Hybrid Quantum Computer Machine learning techniques have led to broad adoption of a statistical model of computing. The companys 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.

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We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine.

Unsupervised machine learning on a hybrid quantum computer. This quantum sampling module could be similarly employed in other domains where sampling is useful. Transfer learning is a machine learning approach where pre-trained models are used for similar purposes. Finally I show how the full computing stack can be used to run a hybrid quantumclassical algorithm for unsupervised machine learning on a 19-qubit processor.

Unsupervised Machine Learning on a Hybrid Quantum Computer Johannes Otterbach Bay Area Quantum Computing Meetup - YCombinator February 1 2018. Quantum computing presents itself as a great way to provide speed-ups for unsupervised machine learning due to the built-in ability to perform tasks simultaneously. Full Stack Quantum Computing Quantum Processor Hardware Cloud based Quantum Applications Operating System.

These ideas are framed within a hybrid quantum-classical computing paradigm. A proposed quantum alternative. Unsupervised Machine Learning on a Hybrid Quantum Computer.

The statistical distributions natively available on quantum processors are a superset of those available classically. 19Q-Acorn available now pyquilreadthedocsio arXiv171205771. This quantumclassical hybrid algorithm shows robustness to realistic noise and we find evidence that classical optimization can be used to train.

I introduce Quil the Quantum Instruction Language as a programming language abstraction akin to quantum assembler dialects to enable these computations via the Forest cloud API. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. 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. 2017 JS Otterbach R Manenti N Alidoust A Bestwick M Block B Bloom S Caldwell N Didier E Schuyler Fried S Hong et al Unsupervised machine learning on a hybrid quantum computer arXiv preprint arXiv171205771 2017. Machine learning techniques have led to broad adoption of a statistical model of computing.

Rigetti announced that it was able to demonstrate unsupervised machine learning on its new 19-qubit quantum computer. ArXiv preprint arXiv171205771 2017. Otterbach J Manenti R Alidoust N et al 2017 Unsupervised machine learning on a hybrid quantum computer.

We are excited to share that our team has demonstrated unsupervised machine learning using 19Q our new 19-qubit general purpose superconducting quantum processor. Finally I show how the full computing stack can be used to run a hybrid quantumclassical algorithm for unsupervised machine learning on a 19-qubit processor. Recently quantum computing startup Rigetti Computing proved a hybrid quantum computer running their 19Q a 19-qubit processor was capable of cluster analysis.

Many deep learning models for natural language processing and computer. Unsupervised Machine Learning on a Hybrid Quantum Computer. Unsupervised machine learning on a hybrid quantum computer JS Otterbach R Manenti N Alidoust A Bestwick M Block B Bloom.

Rigetti Computing has demonstrated unsupervised machine learning on a 19-qubit chip using a hybrid quantum algorithm. This quantumclassical hybrid algorithm shows robustness to realistic noise and we nd evidence that classical optimization. The statistical distributions natively available on quantum processors are a superset of those available classically.

On Morgan Stanley s list of 11 companies with the best quantum computing roadmaps Rigetti Computing is in sixth place having just. Given a classical machine learning infrastructure the idea is to replace the software module that generate samples eg via MCMC with a quantum annealing process. ArXiv preprint arXiv171205771 57.

Kerenidis I Prakash A 2017 Quantum gradient descent for linear systems and least squares. Otterbach et al. I introduce Quil the Quantum Instruction Language as a programming language abstraction akin to quantum assembler dialects to enable these computations via the Forest cloud API.

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