Quantum Circuit Simulator implemented in JavaScript

Slide decks and Jupyter notebooks for training on Rigetti's quantum software stack at the Creative Destruction Lab 2018.

Quantum Computing for Humans!

Implementation of an algorithm for training Quantum Boltzmann Machine neural networks using variational methods. Based on https://arxiv.org/abs/1712.05304 and their sample code.

Implementation of stabilizer codes in pyQuil

⚛️ 💥 ⚙️ A project based in Quantum Computing. This project was built using IBM Q Experience/QisKit (Jupyter Notebook/Python Environment Framework from IBM), PyQuil (Python Environment Framework from Rigetti Computing/Rigetti Forest SDK), ProjectQ (Python Environment Open-Source Framework from ETH Zurich), Q# (Q Sharp Programming Language from Mi…

📚 A series of jupyter notebooks dedicated to introduction to Quantum Computing

Implementations of a few programs which can run on simulators as well as actual quantum hardware written using libraries provided by major quantum software stack providers

Implementing a variational algorithm: QCL using pyQuil. Based on: https://arxiv.org/abs/1803.00745 and http://dkopczyk.quantee.co.uk/qcl/

Solutions for the Jupyter notebook exercises for the training on Rigetti's quantum software stack at the Creative Destruction Lab 2018.

Implementing a distance-based classifier with a quantum interference circuit. Based on https://arxiv.org/abs/1703.10793

Exercises in architecture and programming of quantum computers with cirq , qiskit, tket , projectq and pyquil forest

Comparing the efficiency of Classical Evolutionary Algorithms vs. Quantum Evolutionary Algorithms

A collection of quantum algorithms written in two popular quantum programming languages, PyQuil and Qiskit.

Demonstrates the implementation of various logical gates using quantum circuits. The code utilizes three popular quantum computing libraries: Cirq, PyQuil, and ProjectQ.

Variational Quantum Factoring

Notebooks exploring various features of the Rigetti Forest & Grove using pyQuil

Open-source noise models for Rigetti quantum processors (Novera 9Q, Cepheus-1 36Q). Dual PyQuil/Qiskit implementations.

Docker image for https://github.com/rigetticomputing/pyquil

PQC binary classifier for HEP signal/background separation. Four implementations across Qiskit and PyQuil.