Tensors and Dynamic neural networks in Python with strong GPU acceleration

Python package for compiling and analyzing quantum algorithms to simulate electronic structures.

Q# libraries for the Quantum Development Kit

OpenCGRA is an open-source framework for modeling, testing, and evaluating CGRAs.

CGRA-Flow is an integrated framework for CGRA compilation, exploration, synthesis, and development.

A low-level OpenQASM benchmark suite for NISQ evaluation and simulation. Please see our paper for details.

C++ compiler for heterogeneous quantum-classical computing built on Clang and XACC

Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite

DM-Sim: Quantum Simulator on GPU Cluster using Density Matrix

A C++ based quantum transpiler for NISQ devices

This package includes the implementation for four sparse linear algebra kernels: Sparse-Matrix-Vector-Multiplication (SpMV), Sparse-Triangular-Solve (SpTRSV), Sparse-Matrix-Transposition (SpTrans) …

QASMBench is an OpenQASM benchmark suite running on IBM Quantum-Experience backends.

SV-Sim: Scalable PGAS-based State Vector Simulation of Quantum Circuits

Singular Binarized Neural Network based on GPU Bit Operations (see our SC-19 paper)

The programming runtime and interfaces for ARENA.

NWQEC: A toolkit for fault-tolerant quantum circuit transpilation and T-count optimization.

This package includes the implementation for Sparse-Matrix-Vector-Multiplication (SpMV) and Sparse-Matrix-Matrix-Multiplication (SpMM) for Single-node Multi-GPU (scale-up) platforms such as NVIDIA …

Low precision-based nearest neighboring particle searching algorithm