Adaptation of QuFI to the Pennylane API
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Updated
Mar 6, 2024 - Jupyter Notebook
Adaptation of QuFI to the Pennylane API
Exercises associated with Microsoft's Q# and the Quantum Developer Kit
Travail d'initiative personelle encadré (TIPE), MPSI
Quantum Simulation of 3 qubit Ising Hamiltonian run on IBM Quantum Hardware.
Demonstrative Jupyter Notebook for evaluating matrix elements of observables (generic base) using Quantum Hardware (IBM Qiskit).
Julia package made for quasi-particle scattering simulations of one-dimensional uniform discrete quatum many-body systems using Tensor Network methods (MPS).
Problems that are logically equivalent to Bell test experiments and thus trivially invalidate every quantum physics textbook.
For some time now I have been interested in quantum computing, which has led me to explore some technologies such as the Microsoft Quantum Develop Kit.
Error mitigation matrices for each chip and every number of qubits, updated daily, so that you don't need to calculate them yourself.
Jupyter Notebooks for quantum algorithms. Language: Python (Qiskit). Platform: IBM Quantum's backends and simulators.
Implementation of [[2, 2]] four-cycle surface code
Bachelor thesis implementation of a quantum phase estimation algorithm for solving travelling salesperson problems related to mine scheduling.
A curated collection for transitioning into quantum computing. QuantumPrepKit offers tutorials, tools, and resources blending traditional IT expertise with quantum principles, aiming to simplify the quantum leap for enthusiasts and professionals alike.
A quantum circuit writing library for Go
The repository contains Jupyter notebooks with detailed description of basic quantum protocols and algorithms, the math, circuits and quantum programs using Python.
Implementing quantum algorithms with help from qiskit by IBM
Noise-assisted Variational Quantum Thermalization (NAVQT) is an algorithm used to learn the parameters in a variational quantum circuit which prepares a thermal state of a Hamiltonian. Different from other approaches it considers the noise itself as a variational parameter which can be learned using approximations on the entropy.
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