Demo notebooks for Qiskit application modules demo sessions (Oct 8 & 15):

Overview
Comments
  • Add treon tests

    Add treon tests

    Fixes #7

    Comments

    I thought that opening this PR would trigger the new workflow, but it has not. I believe this is because I am not an admin in this repo; I'm not sure if there's a way for an admin to easily approve it. This PR's status does not indicate to me that any workflows are requiring approval, as I would expect from GitHub Actions' docs.

    Fom my experience running this locally, I expect a few test failures, e.g. because the IBMQ account is not configured. So I'll have to figure out how to handle these expected "failures." (EDIT: Done.)

    As an alternative to calling treon directly, I could introduce a tox.ini, and then people could easily use tox to do the treon tests locally as well. (EDIT: Done.) This might be preferred, but I decided to keep this first draft simple.

    opened by garrison 6
  • max-cut-qiskit-optimization-demo- Graph Output

    max-cut-qiskit-optimization-demo- Graph Output

    Hi, While running the code, I get the solution : [[0, 1, 4], [2, 3]]

    However, when I draw it using maxcut.draw(result, pos=pos):

    nodes 0,1,3 are red, while 2, 4 are blue.

    I have attached my jupyuter file for reference please. Kindly guide my-max-cut-qiskit-optimization-demo.pdf .

    opened by arian-code 5
  • Add test suite (treon via GitHub Actions)

    Add test suite (treon via GitHub Actions)

    I believe this repository would benefit from having a test suite that evaluates each notebook using treon to check for any errors. Running this periodically would allow us to ensure that the code provided in these demos continues to work in future versions of Qiskit.

    opened by garrison 2
  • Replace VQEProgram with VQEClient in qiskit-nature notebook

    Replace VQEProgram with VQEClient in qiskit-nature notebook

    VQEProgram has been deprecated and VQEClient should be used instead.

    https://github.com/qiskit-community/qiskit-application-modules-demo-sessions/blob/61b623a120a47a7ecc18dd1b13aa0a09985f2ec9/qiskit-nature/Qiskit_Nature_YouTube_Tutorial.ipynb?short_path=0da010a#L1915-L1957

    good first issue 
    opened by HuangJunye 3
  •  qiskit-application-modules-demo-sessions/qiskit-nature/Qiskit_Nature_YouTube_Tutorial.ipynb is broken

    qiskit-application-modules-demo-sessions/qiskit-nature/Qiskit_Nature_YouTube_Tutorial.ipynb is broken

    In [27] the code:

    runtime_result = runtime_vqe_solver.solve(problem_reduced)
    

    returns consistently:

    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-27-fb20d8ad77c2> in <module>
    ----> 1 runtime_result = runtime_vqe_solver.solve(problem_reduced)
    
    ~/work/Qiskit/qrel_env/lib/python3.8/site-packages/qiskit_nature/algorithms/ground_state_solvers/ground_state_eigensolver.py in solve(self, problem, aux_operators)
        144         raw_mes_result = self._solver.compute_minimum_eigenvalue(main_operator, aux_ops)
        145 
    --> 146         result = problem.interpret(raw_mes_result)
        147         return result
        148 
    
    ~/work/Qiskit/qrel_env/lib/python3.8/site-packages/qiskit_nature/problems/second_quantization/electronic/electronic_structure_problem.py in interpret(self, raw_result)
        174         result = ElectronicStructureResult()
        175         result.combine(eigenstate_result)
    --> 176         self._grouped_property_transformed.interpret(result)
        177         result.computed_energies = np.asarray([e.real for e in eigenstate_result.eigenenergies])
        178         return result
    
    ~/work/Qiskit/qrel_env/lib/python3.8/site-packages/qiskit_nature/properties/grouped_property.py in interpret(self, result)
        104         """
        105         for prop in self._properties.values():
    --> 106             prop.interpret(result)
    
    ~/work/Qiskit/qrel_env/lib/python3.8/site-packages/qiskit_nature/properties/second_quantization/electronic/particle_number.py in interpret(self, result)
        199 
        200             if aux_op_eigenvalues[_key] is not None:
    --> 201                 n_particles = aux_op_eigenvalues[_key][0].real  # type: ignore
        202                 result.num_particles.append(n_particles)
        203 
    
    TypeError: 'float' object is not subscriptable
    
    opened by jwoehr 1
ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS.

ONNX Runtime Web demo is an interactive demo portal showing real use cases running ONNX Runtime Web in VueJS. It currently supports four examples for you to quickly experience the power of ONNX Runtime Web.

Microsoft 58 Dec 18, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2020 Links Doc

Sebastian Raschka 4.2k Jan 2, 2023
Implementation of self-attention mechanisms for general purpose. Focused on computer vision modules. Ongoing repository.

Self-attention building blocks for computer vision applications in PyTorch Implementation of self attention mechanisms for computer vision in PyTorch

AI Summer 962 Dec 23, 2022
Turning SymPy expressions into PyTorch modules.

sympytorch A micro-library as a convenience for turning SymPy expressions into PyTorch Modules. All SymPy floats become trainable parameters. All SymP

Patrick Kidger 89 Dec 13, 2022
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
DI-HPC is an acceleration operator component for general algorithm modules in reinforcement learning algorithms

DI-HPC: Decision Intelligence - High Performance Computation DI-HPC is an acceleration operator component for general algorithm modules in reinforceme

OpenDILab 185 Dec 29, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Weight initialization schemes for PyTorch nn.Modules

nninit Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin. ##Update This repo has been

Alykhan Tejani 69 Jan 26, 2021
Nest - A flexible tool for building and sharing deep learning modules

Nest - A flexible tool for building and sharing deep learning modules Nest is a flexible deep learning module manager, which aims at encouraging code

ZhouYanzhao 41 Oct 10, 2022
This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Developed By Google!

Machine Learning Hand Detector This is a Machine Learning Based Hand Detector Project, It Uses Machine Learning Models and Modules Like Mediapipe, Dev

Popstar Idhant 3 Feb 25, 2022
Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems

WideLinears Pytorch parallel Neural Networks A package of pytorch modules for fast paralellization of separate deep neural networks. Ideal for agent-b

null 1 Dec 17, 2021
Stacs-ci - A set of modules to enable integration of STACS with commonly used CI / CD systems

Static Token And Credential Scanner CI Integrations What is it? STACS is a YARA

STACS 18 Aug 4, 2022
Import Python modules from dicts and JSON formatted documents.

Paker Paker is module for importing Python packages/modules from dictionaries and JSON formatted documents. It was inspired by httpimporter. Important

Wojciech Wentland 1 Sep 7, 2022
Western-3DSlicer-Modules - Point-Set Registrations for Ultrasound Probe Calibrations

Point-Set Registrations for Ultrasound Probe Calibrations -Undergraduate Thesis-

Matteo Tanzi 0 May 4, 2022
Torch-mutable-modules - Use in-place and assignment operations on PyTorch module parameters with support for autograd

Torch Mutable Modules Use in-place and assignment operations on PyTorch module p

Kento Nishi 7 Jun 6, 2022
Visyerres sgdf woob - Modules Woob pour l'intranet et autres sites Scouts et Guides de France

Vis'Yerres SGDF - Modules Woob Vous avez le sentiment que l'intranet des Scouts

Thomas Touhey (pas un pseudonyme) 3 Dec 24, 2022
Convert scikit-learn models to PyTorch modules

sk2torch sk2torch converts scikit-learn models into PyTorch modules that can be tuned with backpropagation and even compiled as TorchScript. Problems

Alex Nichol 101 Dec 16, 2022
This application explain how we can easily integrate Deepface framework with Python Django application

deepface_suite This application explain how we can easily integrate Deepface framework with Python Django application install redis cache install requ

Mohamed Naji Aboo 3 Apr 18, 2022