qiskit-machine-learning | Quantum Machine Learning | Machine Learning library

by Qiskit Python Version: 0.6.1 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | qiskit-machine-learning Summary

qiskit-machine-learning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. qiskit-machine-learning has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install qiskit-machine-learning' or download it from GitHub, PyPI.

The Machine Learning package simply contains sample datasets at present. It has some classification algorithms such as QSVM and VQC (Variational Quantum Classifier), where this data can be used for experiments, and there is also QGAN (Quantum Generative Adversarial Network) algorithm.

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Quality

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qiskit-machine-learning has a low active ecosystem.

It has 439 star(s) with 276 fork(s). There are 18 watchers for this library.

It had no major release in the last 12 months.

There are 19 open issues and 173 have been closed. On average issues are closed in 111 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of qiskit-machine-learning is 0.6.1

Quality

qiskit-machine-learning has 0 bugs and 0 code smells.

Security

qiskit-machine-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

qiskit-machine-learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

qiskit-machine-learning 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.

Reuse

qiskit-machine-learning 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.

It has 10404 lines of code, 598 functions and 115 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed qiskit-machine-learning and discovered the below as its top functions. This is intended to give you an instant insight into qiskit-machine-learning implemented functionality, and help decide if they suit your requirements.

Generates a Gaussian distribution .
Perform ad - hoc training data .
Discretize and truncate data .
Assign user parameters .
Backward propagation .
Check the copyright header .
Execute the circuit .
Plots training and test features .
Generate the IRIS dataset .
Generate a wine test .

Get all kandi verified functions for this library.

qiskit-machine-learning Key Features

No Key Features are available at this moment for qiskit-machine-learning.

qiskit-machine-learning Examples and Code Snippets

No Code Snippets are available at this moment for qiskit-machine-learning.

Community Discussions

Trending Discussions on qiskit-machine-learning

Quantum Neural Networks - Noise Models

QUESTION

Quantum Neural Networks - Noise Models

Asked 2021-Aug-09 at 12:35

Surveying the QML module in Qiskit for a quantum neural network project and wondering if there is support to add noise models and run noisy simulations?

Going through this tutorial and wondering how one would define a noise model in the argument of qnn.forward when doing a batched forward pass.

...

ANSWER

Answered 2021-Aug-09 at 11:40

You can create a noisy simulator like in the snippet below and then use it when you create a QuantumInstance:

Source https://stackoverflow.com/questions/68709800

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

Vulnerabilities

No vulnerabilities reported

Install qiskit-machine-learning

We encourage installing Qiskit Machine Learning via the pip tool (a python package manager). pip will handle all dependencies automatically and you will always install the latest (and well-tested) version. If you want to work on the very latest work-in-progress versions, either to try features ahead of their official release or if you want to contribute to Machine Learning, then you can install from source. To do this follow the instructions in the documentation.

Support

If you'd like to contribute to Qiskit, please take a look at our contribution guidelines. This project adheres to Qiskit's code of conduct. By participating, you are expected to uphold this code. We use GitHub issues for tracking requests and bugs. Please join the Qiskit Slack community and for discussion and simple questions. For questions that are more suited for a forum, we use the Qiskit tag in Stack Overflow.

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