521 skills found · Page 4 of 18
qiskit-community / Qopt Best PracticesA collection of guidelines to run quantum optimization algorithms on superconducting qubits with Qiskit, using as reference the Quantum Approximate Optimization Algorithm (QAOA) workflow.
isi-usc-edu / PyLIQTRLincoln Laboratory Quantum Algorithm Test and Research
qilimanjaro-tech / QililabQililab is a generic and scalable quantum control library used for fast characterization and calibration of quantum chips. Qililab also offers the ability to execute high-level quantum algorithms with your quantum hardware.
qiyaoliang / Quantum Deep LearningRecent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
yuewuo / MwpfHypergraph Minimum-Weight Parity Factor (MWPF) Algorithm for Decoding General Quantum LDPC Codes
erfanMhi / A Quantum Inspired Genetic Algorithm For K Means ClusteringImplementation of a Quantum inspired genetic algorithm proposed by A quantum-inspired genetic algorithm for k-means clustering paper.
lanl-ansi / QuantumAnnealing.jlTools for the Simulation and Execution of Quantum Annealing Algorithms
givgramacho / CERN Quantum Computing CourseQuantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.
qdevpsi3 / Qrl Dqn GymPennyLane/PyTorch implementation of Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning (Skolik et al., 2021)
kshyatt / Introduction To QMCA set of iPython Notebooks to illustrate how some Quantum Monte Carlo algorithms work
Sumitchongder / Hybrid Quantum Classical AlgorithmsHybrid Quantum–Classical QAOA framework using Qiskit Aer with GPU acceleration and SciPy optimizers. Evaluates MAX‑CUT on 3-regular graphs via depth and repetition cost sweeps.
mullvad / Oqs RsRust bindings and key exchange for liboqs (Open Quantum Safe), a library for quantum-resistant cryptographic algorithms
sanshar / BlockBlock implements the density matrix renormalization group (DMRG) algorithm for quantum chemistry.
quantummind / Quantum Deep Neural NetworkQuantum algorithm to train an infinite-width deep neural network
ArlineQ / Arline BenchmarksArline Benchmarks platform allows to benchmark various algorithms for quantum circuit mapping/compression against each other on a list of predefined hardware types and target circuit classes
OpenVQE / OpenVQEQuantum computing algorithms and applications package. Please check our article https://doi.org/10.1002/wcms.1664. Do you want to contribute? Fork the project, create a pull request directed to the alpha branch of the repo.
yuanhangzhang98 / Ml Quantum CompilingA machine learning algorithm for topological quantum compiling
Quantum-Computing-Cooperation / 6.s089 Intro To Quantum ComputingQuantum computation is a growing field at the intersection of physics, computer science, electrical engineering, and applied math. This course provides an introduction to the basics of quantum computation. Specifically, we will cover some fundamental quantum mechanics, survey quantum circuits, and introduce the most significant quantum algorithms. Furthermore, we will survey advanced topics towards the end of the course. In the past, these topics have included quantum error correction, quantum communication, and applications to fields ranging from machine learning to chemistry. This course is self-contained and does not require any prior knowledge of quantum mechanics.
haimengzhao / Quantum Fed InferA quantum machine learning algorithm for quantum non-IID federated learning
Michaelvll / MyQShorA implementation of Shor's algorithm written in Python calling Q# for the quantum part
