AI News, Unsupervised Machine Learning on Rigetti 19Q with Forest1.2

Unsupervised Machine Learning on Rigetti 19Q with Forest1.2

Unsupervised Machine Learning on Rigetti 19Q with Forest 1.2 by Will Zeng, Rigetti Computing We are excited to share that our team has demonstrated unsupervised machine learning using 19Q, our new 19-qubit general purpose superconducting quantum processor.

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.

Beating the best classical benchmarks will require more qubits and better performance, but hybrid proofs-of-concept like this one form the basis of valuable applications for the first quantum computers.

For example, just a few lines of Python initializes a connection to the quantum processor unit (QPU) and generates an entangled state between qubits with indices 0 and 1.

Forest 1.2, available today, includes important updates and upgrades based on your feedback: Customizable noise models in the Quantum Virtual Machine that allow you to simulate arbitrary quantum channels to study the robustness of algorithms to processor noise.

Unsupervised Machine Learning on Rigetti 19Q with Forest1.2

Unsupervised Machine Learning on Rigetti 19Q with Forest 1.2 by Will Zeng, Rigetti Computing We are excited to share that our team has demonstrated unsupervised machine learning using 19Q, our new 19-qubit general purpose superconducting quantum processor.

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.

Beating the best classical benchmarks will require more qubits and better performance, but hybrid proofs-of-concept like this one form the basis of valuable applications for the first quantum computers.

For example, just a few lines of Python initializes a connection to the quantum processor unit (QPU) and generates an entangled state between qubits with indices 0 and 1.

Forest 1.2, available today, includes important updates and upgrades based on your feedback: Customizable noise models in the Quantum Virtual Machine that allow you to simulate arbitrary quantum channels to study the robustness of algorithms to processor noise.

Rigetti

Free access to up to 26 simulated qubits Customizable noise models Private access for 30+ qubits 19Q Processor The latest generation of our superconducting quantum processors provides 19 fully programmable qubits.

On a mission to build the world's most powerful computer.

Startup Uses Quantum Computing to Boost Machine Learning MIT Technology Review Dec 18, 2017 RESEARCH PAPER Unsupervised Machine Learning on a Hybrid Quantum Computer Rigetti Computing Dec 18, 2017 BLOG OpenFermion, a new library for simulating quantum chemistry with quantum computers Oct 23, 2017 BLOG Forest 1.1 Update Released Oct 11, 2017 BLOG Rigetti Partners with CDL to Drive Quantum Machine Learning Aug 24, 2017 PRESS RELEASE Rigetti Computing Appoints General Peter Pace to Board of Directors PR Newswire Jul 13, 2017 NEWS 50 Smartest Companies of 2017 MIT Technology Review Jun 27, 2017 ARTICLE The Quantum Computing Factory That's Taking on Google and IBM Wired Jun 20, 2017 BLOG Introducing Forest 1.0 Medium Jun 20, 2017 RESEARCH PAPER Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice Rigetti Computing Jun 20, 2017 RESEARCH PAPER Parametrically-Activated Entangling Gates Using Transmon Qubits Rigetti Computing Jun 20, 2017 RESEARCH PAPER Analytical modeling of parametrically-modulated transmon qubits Rigetti Computing Jun 20, 2017 BLOG Rigetti Computing Andreessen Horowitz Jun 20, 2017 PODCAST Quantum Computing, Now and Next a16z Podcast with Chad Rigetti & Chris Dixon May 13, 2017 ARTICLE Y