QClassify
Train a variational quantum circuit for classification of data points using a labeled set of training data.
Learn MoreExplore our rapidly growing library of platforms, tools, and applications powered by Rigetti systems.
Train a variational quantum circuit for classification of data points using a labeled set of training data.
Learn MoreA cross-platform Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations.
Learn MoreCompress quantum data to boost the optimization of variational quantum algorithms.
Learn MoreRun problems on an array of quantum computing hardware platforms and simulators with access to algorithms for binary optimization, chemistry simulation and machine learning.
Learn MoreAn advanced Python-based toolkit for R&D teams to develop and deploy quantum control in their hardware or theoretical research.
Learn MoreA compiler that works with multiple backends like Rigetti.
Learn MoreDesign and execute quantum algorithms on simulators or real quantum computers.
Learn MoreAn end-to-end stack that compiles and executes quantum chemistry algorithms for NISQ devices.
Learn MoreEnables multiple languages to be compiled to multiple backends.
Learn MoreA modular package for the quantum approximate optimisation algorithm (QAOA) built on top of Rigetti’s Forest SDK.
Learn MoreExplore how quantum superposition can help solve the levels of this educational game while constructing a circuit to run on a Rigetti quantum computer.
Learn MoreAn open-source, optimizing compiler for gate-based quantum programs written in Quil or QASM.
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