An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Last update: Jun 28, 2022

Overview

QVC Optimizer Review

Code for the paper "An Empirical Review of Optimization Techniques for Quantum Variational Circuits".

Each of the python files can be run and will generate the results for the associated set of problem setups. To enable/disable noise, comment out or uncomment the only line that has the .with_noise(cirq.depolarize(p=0.01)).

All the results are available in the results.txt organized by task, score, and standard deviation over the 3 runs.

Open Call For Research

Check out the CONTRIBUTING.md and take on one of the issues (or make your own). For more information, see the video:

Owner

Owen Lockwood

GitHub Repository

An Empirical Review of Optimization Techniques for Quantum Variational Circuits

Related tags

Overview

QVC Optimizer Review

Open Call For Research

Owner

Owen Lockwood

Neural network visualization toolkit for tf.keras

Lucid library adapted for PyTorch

Model analysis tools for TensorFlow

treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions.

A Practical Debugging Tool for Training Deep Neural Networks

👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

Interpretability and explainability of data and machine learning models

Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)

Visualizer for neural network, deep learning, and machine learning models

Convolutional neural network visualization techniques implemented in PyTorch.

A ultra-lightweight 3D renderer of the Tensorflow/Keras neural network architectures

tensorboard for pytorch (and chainer, mxnet, numpy, ...)

ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

Visualization Toolbox for Long Short Term Memory networks (LSTMs)

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Auralisation of learned features in CNN (for audio)

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Lime: Explaining the predictions of any machine learning classifier

Visualize a molecule and its conformations in Jupyter notebooks/lab using py3dmol

A data-driven approach to quantify the value of classifiers in a machine learning ensemble.