Python package for visualizing the loss landscape of parameterized quantum algorithms.

Related tags

Deep Learningorqviz
Overview

Image

orqviz

A Python package for easily visualizing the loss landscape of Variational Quantum Algorithms by Zapata Computing Inc.

orqviz provides a collection of tools which quantum researchers and enthusiasts alike can use for their simulations. It works with any framework for running quantum circuits, for example qiskit, cirq, pennylane, and Orquestra. The package contains functions to generate data, as well as a range of flexible plotting and helper functions. orqviz is light-weight and has very few dependencies.

Getting started

In doc/examples/ we provide a range of Jupyter notebook examples for orqviz. We have four Jupyter notebooks with tutorials for how to get started with any quantum circuit simulation framework you might use. You will find examples with qiskit, cirq, pennylane and Zapata's Orquestra library. The tutorials are not exhaustive, but they do provide a full story that you can follow along.

In this notebook we have the Sombrero example that we showcase in our paper. We also have an advanced example notebook which provides a thorough demonstration of the flexibility of the orqviz package.

We recently published a paper on arXiv where we review the tools available with orqviz:
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms

Installation

You can install our package using the following command:

pip install orqviz

Alternatively you can build the package from source. This is especially helpful if you would like to contribute to orqviz

git clone https://github.com/zapatacomputing/orqviz.git
cd orqviz
pip install -e ./

Examples

import orqviz
import numpy as np

np.random.seed(42)

def loss_function(pars):
    return np.sum(np.cos(pars))**2 + np.sum(np.sin(30*pars))**2

n_params = 42
params = np.random.uniform(-np.pi, np.pi, size=n_params)
dir1 = orqviz.geometric.get_random_normal_vector(n_params)
dir2 = orqviz.geometric.get_random_orthonormal_vector(dir1)

scan2D_result = orqviz.scans.perform_2D_scan(params, loss_function,
                                direction_x=dir1, direction_y=dir2,
                                n_steps_x=100)
orqviz.scans.plot_2D_scan_result(scan2D_result)

This code results in the following plot:

Image

Authors

The leading developer of this package is Manuel Rudolph at Zapata Computing.
For questions related to the visualization techniques, contact Manuel via [email protected] .

The leading software developer of this package is Michał Stęchły at Zapata Computing.
For questions related to technicalities of the package, contact Michał via [email protected] .

Thank you to Sukin Sim and Luis Serrano from Zapata Computing for their contributions to the tutorials.

You can also contact us or ask general questions using GitHub Discussions.

For more specific code issues, bug fixes, etc. please open a GitHub issue in the orqviz repository.

If you are doing research using orqviz, please cite our paper:

ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms

How to contribute

Please see our Contribution Guidelines.

Comments
  • Use transpile to build circuit only once

    Use transpile to build circuit only once

    Despite being wrapped up in a lambda function, the get_circuit function is actually still called for every function evaluation during plot generation or optimization, and hence the circuit is rebuilt each time. This rather defeats the concept of late binding of the parameter values. The PR uses a slightly different approach using the transpile function. The code is arguably more transparent than using the lambda function wrapper. Evaluation is faster now, but for this simple case rarely more than 10%. One downside, the circuit cannot be plotted anymore in a simple way.

    opened by RonMeiburg 11
  • ci: add step with Pythonic cruft cleanup

    ci: add step with Pythonic cruft cleanup

    Apparently, issues that we had with mypy stem from Github Actions caching some (?) directories (thanks @alexjuda for pointing this out!). This PR adds a cleaning step (taken from z-quantum-actions) that deletes potentially conflicting directories.

    opened by dexter2206 1
  • Clean notebooks

    Clean notebooks

    These are the once-run versions of the cirq and qiskit notebooks derived from the 'simplified qiskit get_circuit() return' commit from the main branch. I hope this works for you. If not, then I apologize, When it comes to git I still suffer from sas every now and then.

    opened by RonMeiburg 1
  • Loss function clarity

    Loss function clarity

    Goals of this draft PR:

    • Allow parameters to be any np.ndarray rather than strictly a 1D np.ndarray
    • Improve docstrings for what we define as a loss function
    • Improve README to specify what we define as a loss function, and how to wrap their loss function with functools.partial
    • Alternatively, allow loss_function_kwargs in the scanning functions that we pass to the loss_function with more than one argument.
    opened by MSRudolph 1
  • Utilize tqdm progress bar when verbose=True during scans.

    Utilize tqdm progress bar when verbose=True during scans.

    Is your feature request related to a problem? Please describe. We should replace the print calls when verbose=True in scans with tqdm from the tqdm library. Alternatively, we make it the default and find a way to mute the library's prints.

    Describe the solution you'd like

    verbose = True  # or False
for it in tqdm(range(n_iters), disable = not verbose):
   ...  # run scans

    Additional context Our verbosity options are currently very rudimentary and tqdm is one of the most used Python libraries.

    opened by MSRudolph 2
  • This repo should contain Issues for how people can contribute

    This repo should contain Issues for how people can contribute

    Is your feature request related to a problem? Please describe. Currently, when people enter the orqviz GitHub repository with the intent to contribute, there are no open Issues and not many PRs. They will not know what might be low-hanging fruit to contribute.

    Describe the solution you'd like We (orqviz developers) should open Issues which are connected to how people can concretely contribute. For example, we could provide links to existing tutorials which we believe can be readily enhanced with our visualization techniques. In such cases, potential contributors could work on such enhancements and reach out to the authors of the original tutorials. Similarly, we can elaborate on future visualization techniques which we could experiment with. This may be done by external contributors.

    opened by MSRudolph 0
Releases(v0.3.0)

  • v0.3.0(Aug 19, 2022)

    What's Changed

    • ci: add step with Pythonic cruft cleanup by @dexter2206 in https://github.com/zapatacomputing/orqviz/pull/43
    • Update main by @mstechly in https://github.com/zapatacomputing/orqviz/pull/44
    • Fourier transform by @laurgao in https://github.com/zapatacomputing/orqviz/pull/45

    Full Changelog: https://github.com/zapatacomputing/orqviz/compare/v0.2.0...v0.3.0

    Source code(tar.gz)
    Source code(zip)
    orqviz-0.3.0-py3-none-any.whl(38.09 KB)

  • v0.2.0(Feb 8, 2022)

    New features:

    • orqviz now doesn't require parameters to be 1D vectors, they can be ND arrays instead
    • We introduced LossFunctionWrapper as a utility tool that helps with changing arbitrary python functions into orqviz-compatible loss functions.

    Minor changes:

    • Improvements in notebook examples
    • Improvements in readme and contribution guidelines
    Source code(tar.gz)
    Source code(zip)

  • v0.1.1(Nov 9, 2021)

    What's Changed

    • Fixed classifiers in the setup.cfg
    • Minor fixes in in README
    • Relax dependency versions

    Full Changelog: https://github.com/zapatacomputing/orqviz/compare/v0.1.0...v0.1.1

    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Nov 9, 2021)

Owner
Zapata Computing, Inc.
Zapata Computing, Inc.
GitHub Repository
Learnable Motion Coherence for Correspondence Pruning

Learnable Motion Coherence for Correspondence Pruning Yuan Liu, Lingjie Liu, Cheng Lin, Zhen Dong, Wenping Wang Project Page Any questions or discussi

liuyuan 41 Nov 30, 2022
The implementation for "Comprehensive Knowledge Distillation with Causal Intervention".

Comprehensive Knowledge Distillation with Causal Intervention This repository is a PyTorch implementation of "Comprehensive Knowledge Distillation wit

Xiang Deng 10 Nov 03, 2022
Label Mask for Multi-label Classification

LM-MLC 一种基于完型填空的多标签分类算法 1 前言 本文主要介绍本人在全球人工智能技术创新大赛【赛道一】设计的一种基于完型填空(模板)的多标签分类算法：LM-MLC，该算法拟合能力很强能感知标签关联性，在多个数据集上测试表明该算法与主流算法无显著性差异，在该比赛数据集上的dev效果很好，但是由

52 Nov 20, 2022
DeiT: Data-efficient Image Transformers

DeiT: Data-efficient Image Transformers This repository contains PyTorch evaluation code, training code and pretrained models for DeiT (Data-Efficient

Facebook Research 3.2k Jan 06, 2023
Algo-burn - Script to configure an Algorand address as a "burn" address for one or more ASA tokens

Algorand Burn Address This is a simple script to illustrate how a "burn address"

GSD 5 May 10, 2022
WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

HeadPoseEstimation-WHENet-yolov4-onnx-openvino ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L 1. Usage $ git clone htt

Katsuya Hyodo 49 Sep 21, 2022
Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

A Unified Objective for Novel Class Discovery This is the official repository for the paper: A Unified Objective for Novel Class Discovery Enrico Fini

Enrico Fini 118 Dec 26, 2022
In the AI for TSP competition we try to solve optimization problems using machine learning.

AI for TSP Competition Goal In the AI for TSP competition we try to solve optimization problems using machine learning. The competition will be hosted

Paulo da Costa 11 Nov 27, 2022
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain

Unity Technologies 187 Dec 24, 2022
ConvMixer unofficial implementation

ConvMixer ConvMixer 非官方实现 pytorch 版本已经实现。 nets 是重构版本 ，test 是官方代码 感兴趣小伙伴可以对照看一下。 keras 已经实现 tf2.x 中 是tensorflow 2 版本 gelu 激活函数要求 tf=2.4 否则使用入下代码代替gelu

Jian Tengfei 8 Jul 11, 2022
Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

Pytorch implementation of paper "Efficient Nearest Neighbor Language Models" (EMNLP 2021)

Junxian He 57 Jan 01, 2023
Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

clip-text-decoder Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script. Example Predi

Frank Odom 36 Dec 21, 2022
TensorFlow implementation of the paper "Hierarchical Attention Networks for Document Classification"

Hierarchical Attention Networks for Document Classification This is an implementation of the paper Hierarchical Attention Networks for Document Classi

Quoc-Tuan Truong 83 Dec 05, 2022
Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation. Intel iHD GPU (iGPU) support. NVIDIA GPU (dGPU) support.

mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.

Katsuya Hyodo 24 Mar 02, 2022
🏅 The Most Comprehensive List of Kaggle Solutions and Ideas 🏅

🏅 Collection of Kaggle Solutions and Ideas 🏅

Farid Rashidi 2.3k Jan 08, 2023
[CVPR 2021] Released code for Counterfactual Zero-Shot and Open-Set Visual Recognition

Counterfactual Zero-Shot and Open-Set Visual Recognition This project provides implementations for our CVPR 2021 paper Counterfactual Zero-S

144 Dec 24, 2022
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021) An efficient PyTorch library for Point Cloud Completion.

Microsoft 119 Jan 02, 2023
QICK: Quantum Instrumentation Control Kit

QICK: Quantum Instrumentation Control Kit The QICK is a kit of firmware and software to use the Xilinx RFSoC to control quantum systems. It consists o

81 Dec 15, 2022
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022