Open Systems Dynamics for the Group project
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Updated
Jun 4, 2016 - Python
Quantum computing is a field of computing that uses quantum phenomena such as superposition and entanglement to perform operations on data. It is a rapidly growing field with potential applications in fields such as cryptography, chemistry, and optimization. Quantum computers can solve certain problems much faster than classical computers. Various programming languages such as Q#, Python and C++ can be used to write quantum algorithms to be run on quantum computers. The development of quantum computers is an active area of research and engineering.
Open Systems Dynamics for the Group project
A multi-layer perceptron for classifying nitrogen vacancy centers from spectra scans.
A python library providing the building blocks for the simulation of a quantum circuit.
Implementation of Shor's quantum algorithm for integer factorization
Numerical implementation of diagrammatic scattering theory for few-photon transport through Bose-Hubbard lattices
Learning quantum programming techniques via Qutip tutorials and additional resources
NOTE, this is out of date. See https://github.com/physicsnerd/qc-sim Python 3 simulation of an ideal quantum computer.
Classical implementation of a quantum algorithm for finding approximate solutions to the minimum vertex cover problem.
Quantum Computation Simulation with SAGE
Implementation of the paper on feature subset selection
Neural Network Decoders for Quantum Error Correcting Codes
Dissipative Grover's algorithm continuous simulation
TQEC circuit generator
🔮 Monitoring the load of IBM Q processors.
Rigetti Quantum Experments
Quantum Algorithm with Qiskit
Implementation of Solovay Kitaev Algorithm for approximating any n-qubit gate using gates from library set {H,T,CNOT,S}.
Created by Richard Feynman and Yuri Manin