qiskit
pennylane
| qiskit | pennylane | |
|---|---|---|
| 23 | 3 | |
| 4,643 | 2,124 | |
| 3.6% | 1.9% | |
| 9.8 | 9.8 | |
| 3 days ago | 1 day ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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qiskit
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Library for Machine learning and quantum computing
Qiskit
- Reorientation vers metiers de l'informatique quantique
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Basic question about quantum operations
Hello, guys. I am a newbie to quantum computing. I got a question when reading the textbook on Qiskit.org.
- Calcul quantique
- Mio padre sta diventando un complottaro. Internet corrompe i boomer
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Meetup Announcement: Quantum Computing meets Data Science, 6th of June, 2023
Second talk: Gesture Classification on a Smartphone Web-App using a Quantum ComputerDavid Alber and Olaf Hahn will demonstrate that Quantum enhanced Support Vector Machines (QSVMs) can be utilized to classify gestures made by a conventional smartphone. They will showcase how developers can utilize the Qiskit Python framework and provision IBM Cloud and IBM Quantum resources to integrate such models in a traditional application environment seamlessly.Machine learning and quantum are promising technologies with the potential to address yet intractable problems. The hybrid nature of QSVMs makes it possible to deploy such models already today. We will show you how.
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p=np is a hardware problem maybe
Also, see Shor's algorithm for a quantum approach to prime factorization, and maybe have a play with qiskit
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Qiskit #0
Qiskit
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Which programming language is best to simulate a quantum computer?
I think Python would be a more mainstream choice and so you'll find modules like qiskit or [qutip(https://qutip.org/) already exist and will make life easier.
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How much would I benefit if I started working on my coding skills before uni?
If you want to be a bit more physics-focused in your coding, it might help to dig up a course or textbook on numerical methods in physics. Being able to numerically solve differential equations is probably the most generally applicable skill in physics. Machine learning methods are pretty ''hot right now'' and might be fun to have a look into. And for quantum technology in particular, you might enjoy having a look at some python packages like Kwant for quantum transport, QuTiP for quantum dynamics and Qiskit for quantum computing. You won't understand the physics for this for quite some time, they might help serve as a bit of inspiration and an indication as to what physicists can use programming for.
pennylane
- PennyLane: Python library for differentiable programming of quantum computers
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Library for Machine learning and quantum computing
Pennylane
- pennylane: PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network
What are some alternatives?
QuTiP - QuTiP: Quantum Toolbox in Python
qiskit-ibm-provider - Qiskit Provider for accessing the IBM Quantum Services: Online Systems and Simulators
mitiq - Mitiq is an open source toolkit for implementing error mitigation techniques on most current intermediate-scale quantum computers.
ADCME.jl - Automatic Differentiation Library for Computational and Mathematical Engineering
pyquil - A Python library for quantum programming using Quil.
Cirq - A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
pyquirk - A simple python program to convert graphical circuits to quantikz figures.
machine_learning_refined - Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
qiskit-tutorials - A collection of Jupyter notebooks showing how to use the Qiskit SDK
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
beets - music library manager and MusicBrainz tagger
foolbox - A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX