Search Results for "open source kms"
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Showing 47 open source projects for "open source kms"
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Q-CTRL Open Controls
Q-CTRL Open Controls
Q-CTRL Open Controls is an open-source Python package that makes it easy to create and deploy established error-robust quantum control protocols from the open literature. The aim of the package is to be the most comprehensive library of published and tested quantum control techniques developed by the community, with easy-to-use export functions allowing users to deploy these controls on. -
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NumPy
The fundamental package for scientific computing with Python
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of... -
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Qiskit
Qiskit is an open-source SDK for working with quantum computers
Qiskit [kiss-kit] is an open-source SDK for working with quantum computers at the level of pulses, circuits, and application modules. When you are looking to start Qiskit, you have two options. You can start Qiskit locally, which is much more secure and private, or you get started with Jupyter Notebooks hosted in IBM Quantum Lab. Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application development. ... -
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QuTiP
QuTiP: Quantum Toolbox in Python
QuTiP is open-source software for simulating the dynamics of open quantum systems. The QuTiP library depends on the excellent Numpy, Scipy, and Cython numerical packages. In addition, graphical output is provided by Matplotlib. QuTiP aims to provide user-friendly and efficient numerical simulations of a wide variety of Hamiltonians, including those with arbitrary time-dependence, commonly found in a wide range of physics applications such as quantum optics, trapped ions, superconducting circuits, and quantum nanomechanical resonators. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Azure Quantum Development Kit
Azure Quantum Development Kit
Azure Quantum Development Kit, including the Q# programming language, resource estimator, and Quantum Katas. The playground is a small website that loads the Q# editor, compiler, samples, katas, and documentation for the standard library. It's a way to manually validate any changes you make to these components. The easiest way to develop in this repo is to use VS Code. When you open the project root, by default VS Code will recommend you install the extensions listed in... -
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Cirq
A python framework for creating, editing, and invoking NISQ
Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. -
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Tequila
A High-Level Abstraction Framework for Quantum Algorithms
Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state-of-the-art simulators as well as on real quantum devices. -
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Yao
Extensible, Efficient Quantum Algorithm Design for Humans
An intermediate representation to construct and manipulate your quantum circuit and let you make own abstractions on the quantum circuit in native Julia. Yao supports both forward-mode (faithful gradient) and reverse-mode automatic differentiation with its builtin engine optimized specifically for quantum circuits. Top performance for quantum circuit simulations. Its CUDA backend and batched quantum register support can make typical quantum circuits even faster. Yao is designed to be... -
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QPanda 2
QPanda 2 is an open source quantum computing framework
QPanda2 is an open source quantum computing framework developed by Origin Quantum, which can be used to build, run and optimize quantum algorithms. QPanda2 is the basic library of a series of software developed by Origin Quantum, which provides core components for QRunes, Qurator and quantum computing services. -
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Quantum++
Modern C++ quantum computing library
Quantum++ is a modern C++ general-purpose quantum computing library, composed solely of template header files. Quantum++ is written in standard C++17 and has very low external dependencies, using only the Eigen 3 linear algebra header-only template library and, if available, the OpenMP multiprocessing library. Quantum++ is not restricted to qubit systems or specific quantum information processing tasks, being capable of simulating arbitrary quantum processes. The main design factors taken in... -
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Superstaq
Quantum software platform that is optimized across the quantum stack
This repository is the home of the Superstaq development team's open-source work. Our quantum software platform is optimized across the quantum stack and enables users to write quantum programs in Cirq or Qiskit and target a variety of quantum computers and simulators. -
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Perceval
An open source framework for programming photonic quantum computers
An open-source framework for programming photonic quantum computers. Through a simple object-oriented Python API, Perceval provides tools for composing circuits from linear optical components, defining single-photon sources, manipulating Fock states, running simulations, reproducing published experimental papers and experimenting with a new generation of quantum algorithms. -
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Qulacs
Variational Quantum Circuit Simulator for Quantum Computation Research
Variational Quantum Circuit Simulator for Quantum Computation Research. Qulacs is a Python/C++ library for fast simulation of large, noisy, or parametric quantum circuits. Qulacs is developed at QunaSys, Osaka University, NTT, and Fujitsu. -
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Mitiq
Mitiq is an open source toolkit for implementing error mitigation
Mitiq is a Python toolkit for implementing error mitigation techniques on quantum computers. Current quantum computers are noisy due to interactions with the environment, imperfect gate applications, state preparation and measurement errors, etc. Error mitigation seeks to reduce these effects at the software level by compiling quantum programs in clever ways. -
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CUDA-Q
C++ and Python support for the CUDA Quantum programming model
CUDA-Q is an open-source platform for developing hybrid quantum-classical applications using a unified programming model across CPUs, GPUs, and quantum processing units. It provides a full toolchain that includes compilers, runtimes, and libraries for writing quantum programs in both C++ and Python. The platform is designed to be hardware-agnostic, allowing developers to run applications on different quantum backends or simulate them efficiently using GPU acceleration when physical quantum hardware is unavailable. ... -
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OpenFermion
The electronic structure package for quantum computers
OpenFermion is an open source library for compiling and analyzing quantum algorithms to simulate fermionic systems, including quantum chemistry. Among other functionalities, this version features data structures and tools for obtaining and manipulating representations of fermionic and qubit Hamiltonians. For more information, see our release paper. Currently, OpenFermion is tested on Mac, Windows, and Linux. -
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BQSKit
Berkeley Quantum Synthesis Toolkit
The Berkeley Quantum Synthesis Toolkit (BQSKit) [bis • kit] is a powerful and portable quantum compiler framework. It can be used with ease to compile quantum programs to efficient physical circuits for any QPU. A standard workflow utilizing BQSKit consists of loading a program into the framework, modeling the target QPU, compiling the program, and exporting the resulting circuit. -
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CUDA-QX
Accelerated libraries for quantum-classical computing built on CUDA-Q
CUDA-QX is a collection of accelerated libraries built on top of the CUDA-Q platform, designed to enable rapid development of hybrid quantum-classical applications. It extends the CUDA-Q programming model by providing optimized implementations of domain-specific quantum computing primitives and workflows. The libraries are intended to help researchers and developers leverage GPUs, CPUs, and quantum processing units together in a unified computational model. CUDA-QX focuses on key areas such... -
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PyQuil
A Python library for quantum programming using Quil
PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions. PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To... -
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OpenQASM
Quantum assembly language for extended quantum circuits
OpenQASM is an imperative programming language designed for near-term quantum computing algorithms and applications. Quantum programs are described using the measurement-based quantum circuit model with support for classical feed-forward flow control based on measurement outcomes. OpenQASM presents a parameterized set of physical logic gates and concurrent real-time classical computations. Its main goal is to serve as an intermediate representation for higher-level compilers to communicate... -
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Quantum Information Toolkit is a comprehensive, easy-to-use interactive numerical toolkit for quantum information and computing, available for both MATLAB and Python.
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staq
Full-stack quantum processing toolkit
staq is a modern C++ library for the synthesis, transformation, optimization and compilation of quantum circuits. staq is written in standard C++17 and has very low external dependencies. It is usable either through the provided binary tools, or as a header-only library that can be included to provide direct support for parsing & manipulating circuits written in the OpenQASM circuit description language. Inspired by Clang, staq is designed to manipulate OpenQASM syntax trees directly, rather... -
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PetoronHash2
PH2 Permutation-based cryptographic post-quantum hash library with duplex construction (C++20)
PetoronHash2 (PH2) is a standalone ARX-based sponge construction that combines: A 4096-bit internal permutation core A sponge/duplex architecture with explicit capacity separation An extendable-output function (XOF) Memory-hard (HARD) and burn (BURN) modes A structured tagged absorb scheme A parameterized architecture suitable for hashing, KDF, and stress-oriented derivation PH2 is designed as a wide-state cryptographic primitive with explicit domain separation and configurable output... -
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PetoronHash-System
PHASH | post-quantum XOF hashing algorithm | C++20
PHASH is a self-contained, dependency-free, post-quantum XOF hashing algorithm implemented in modern C++20. This release delivers the first fully stable production implementation of the PetoronHash-System — a 1600-bit sponge-based hash function with domain separation, extendable output, and deterministic behavior. Key Features No external dependencies — pure C++20 implementation. Extendable Output (XOF) — supports arbitrary output length (256–8192+ bits). Post-quantum oriented design... -
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TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing.
