pennylane | PennyLane is a cross-platform Python library for differentiable programming of quantum computers. T

 by   PennyLaneAI Python Version: 0.36.0 License: Apache-2.0

kandi X-RAY | pennylane Summary

kandi X-RAY | pennylane Summary

pennylane is a Python library typically used in Quantum Computing, Deep Learning applications. pennylane has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install pennylane' or download it from GitHub, PyPI.

PennyLane is a cross-platform Python library for differentiable programming of quantum computers.
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              pennylane has a medium active ecosystem.
              It has 1795 star(s) with 474 fork(s). There are 51 watchers for this library.
              There were 5 major release(s) in the last 12 months.
              There are 203 open issues and 750 have been closed. On average issues are closed in 74 days. There are 102 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pennylane is 0.36.0

            kandi-Quality Quality

              pennylane has 0 bugs and 0 code smells.

            kandi-Security Security

              pennylane has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pennylane code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pennylane is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pennylane releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              pennylane saves you 44223 person hours of effort in developing the same functionality from scratch.
              It has 101660 lines of code, 8144 functions and 486 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pennylane and discovered the below as its top functions. This is intended to give you an instant insight into pennylane implemented functionality, and help decide if they suit your requirements.
            • Broadcast a tensor .
            • Cut a circuit into a circuit .
            • Takes a circuit .
            • Run backward match .
            • Decorator to reconstruct a complex Fourier series .
            • Compute the Jacobian of a qnode .
            • Calculate the parameter shift for a circuit .
            • Format a tape .
            • Computes the gradient of a circuit .
            • Find and place cuts on a graph .
            Get all kandi verified functions for this library.

            pennylane Key Features

            No Key Features are available at this moment for pennylane.

            pennylane Examples and Code Snippets

            Qlearnkit python library,Getting started with Qlearnkit
            Pythondot img1Lines of Code : 35dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            from qlearnkit.algorithms import QKNeighborsClassifier
            from qlearnkit.encodings import AmplitudeEncoding
            from qiskit import BasicAer
            from qiskit.utils import QuantumInstance, algorithm_globals
            
            from qlearnkit.datasets import load_iris
            
            seed = 42
            algo  
            Experiments,Training
            Pythondot img2Lines of Code : 10dot img2License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            $ pip install gym torch torchvision pennylane tensorboard
            
            $ cd cart_pole/
            $ python train.py 
            
            $ cd cart_pole/
            $ python train.py --batch_size=32
            
            $ cd cart_pole/
            $ python train.py --help
            
            $ cd cart_pole/
            $ python train.py
            $ tensorboard --logdir logs/  
            Qlearnkit python library,Development notes
            Pythondot img3Lines of Code : 4dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            python3 -m venv .venv
            
            source .venv/bin/activate 
            
            pip install -r requirements-dev.txt
            
            make test
              

            Community Discussions

            QUESTION

            SPARQL: Using filter in query
            Asked 2019-Dec-20 at 01:37

            I recently started using SPARQL and have an exercise to make a query that will allow me to get song titles that have a singer as their only vocalist? In this case being John Lennon the only singer.

            I have a beatles.ttl data file, so far I was able to select all the songs where John Lennon is lead singer, however there are songs where he is lead singer with 2 other band members.

            ...

            ANSWER

            Answered 2019-Dec-15 at 22:35

            The trick is to realize that you can rephrase "John Lennon is the only vocalist" to "none of the vocalists are anyone other than John Lennon". Then you can use FILTER NOT EXISTS, like so:

            Source https://stackoverflow.com/questions/59340913

            QUESTION

            using conditional CONSTRUCT queries in SPARQL
            Asked 2019-Dec-19 at 23:30

            I am in need of some help regarding SPARQL construct queries. How is it possible to make a CONSTRUCT query with an IF condition to get triples that enable rules representation?

            Like a CONSTRUCT query that enables us to express the following rules:

            1. If X is an instance of a C1 class, and C1 is a subclass of a C2 class, then X is an instance of C2.

            and

            1. If X has in P1 property the value V, and P1 is a subproperty of P2, then X has in property P2 the value V.

            For this Data File (Beatles.ttl)

            ...

            ANSWER

            Answered 2019-Dec-19 at 23:30

            As suggested in the comments: you don't actually need a conditional to express this. All you're doing in the left-hand side of the rule is matching a combination of triple patterns. This is exactly what the WHERE clause in a SPARQL query does. Similarly, the right-hand side of the rule simply recombines matched resources into new triple patterns - this is exactly what the CONSTRUCT clause does. For example:

            If X is an instance of a C1 class, and C1 is a subclass of a C2 class, then X is an instance of C2.

            That could be expressed as:

            Source https://stackoverflow.com/questions/59411550

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install pennylane

            PennyLane requires Python version 3.7 and above. Installation of PennyLane, as well as all dependencies, can be done using pip:.
            For an introduction to quantum machine learning, guides and resources are available on PennyLane's quantum machine learning hub:. You can also check out our documentation for quickstart guides to using PennyLane, and detailed developer guides on how to write your own PennyLane-compatible quantum device.
            What is quantum machine learning?
            QML tutorials and demos
            Frequently asked questions
            Key concepts of QML
            QML videos

            Support

            Docker support exists for building using CPU and GPU (Nvidia CUDA 11.1+) images. See a more detailed description here.
            Find more information at:

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            Install
          • PyPI

            pip install PennyLane

          • CLONE
          • HTTPS

            https://github.com/PennyLaneAI/pennylane.git

          • CLI

            gh repo clone PennyLaneAI/pennylane

          • sshUrl

            git@github.com:PennyLaneAI/pennylane.git

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