QClassify
Train a variational quantum circuit for classification of data points using a labeled set of training data.
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Explore our rapidly growing library of platforms, tools, and applications powered by Rigetti systems.
Oak Ridge Leadership Computing Facility
Oak Ridge National LaboratoryOLCF is a U.S. Department of Energy user facility that provides state-of-the-art computational infrastructure to accelerate scientific discovery and engineering advances. Rigetti quantum systems are available to researchers through the OLCF’s Quantum User Program.
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A cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
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Compress quantum data to boost the optimization of variational quantum algorithms.
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Run problems on an array of quantum computing hardware platforms and simulators with access to algorithms for binary optimization, chemistry simulation and machine learning.
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An advanced Python-based toolkit for R&D teams to develop and deploy quantum control in their hardware or theoretical research.
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A compiler that works with multiple backends like Rigetti.
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Design and execute quantum algorithms on simulators or real quantum computers.
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An end-to-end stack that compiles and executes quantum chemistry algorithms for NISQ devices.
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Enables multiple languages to be compiled to multiple backends.
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A modular package for the quantum approximate optimisation algorithm (QAOA) built on top of Rigetti’s Forest SDK.
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Explore how quantum superposition can help solve the levels of this educational game while constructing a circuit to run on a Rigetti quantum computer.
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An open-source, optimizing compiler for gate-based quantum programs written in Quil or QASM.
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