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QMLT - The Quantum Machine Learning Toolbox (QMLT) is a Strawberry Fields application that simplifies the optimization of variational quantum circuits (also known as parametrized quantum circuits)

  •    Python

The Quantum Machine Learning Toolbox (QMLT) is a Strawberry Fields application that simplifies the optimization of variational quantum circuits. Tasks for the QMLT range from variational eigensolvers and unitary learning to supervised and unsupervised machine learning with models based on a variational circuit. To get started, please see the online documentation.

quantum-learning - This repository contains the source code used to produce the results presented in the paper "Machine learning method for state preparation and gate synthesis on photonic quantum computers"

  •    Jupyter

This repository contains the source code used to produce the results presented in "Machine learning method for state preparation and gate synthesis on photonic quantum computers" arXiv:1807.10781. state_learner.py: a Python script to automate quantum state learning using continuous-variable (CV) variational quantum circuits. Simply specify your one- or two-mode target state, along with other hyperparameters, and this script automatically constructs and optimizes the variational quantum circuit.

strawberryfields - Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits

  •    Python

Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous variable quantum optical circuits. To see Strawberry Fields in action immediately, try out our Strawberry Fields Interactive web application. Prepare your initial states, drag and drop gates, and watch your simulation run in real time right in your web browser.




pennylane-forest - This PennyLane plugin allows the Rigetti Forest QPUs, QVM, and wavefunction simulator to optimize quantum circuits

  •    Python

Contains the PennyLane Forest plugin. This plugin allows three Rigetti devices to work with PennyLane - the wavefunction simulator, the Quantum Virtual Machine (QVM), and Quantum Processing Units (QPUs). pyQuil is a Python library for quantum programming using the quantum instruction language (Quil) - resulting quantum programs can be executed using the Rigetti Forest SDK and the Rigetti QCS.