Quantum Circuits

Provides client-side access for direct pulse-level control of quantum computers. Enables fine-grained hardware interaction for advanced experiments and calibration.

Develop quantum algorithms with a high-level language. Features typed variables, automatic uncomputation, modularity, integrated arithmetic, and broad hardware compatibility.

Build quantum circuits, run simulations, analyze results, and connect to hardware. Features include Hamiltonian manipulation, QAOA, and QASM export.

Define precise quantum pulse sequences with a Python DSL. Manage qubit and instrument configurations. Includes examples for single and two-qubit experiments.

Create and manipulate quantum circuits. Includes basic gates, circuit structures, and mock hardware for simulation and testing.

Debug quantum programs with a semi-automated tool. Simulate circuits, diagnose errors, and integrate with IDEs using a DAP server.

Provides a framework for mixed-dimensional qudit quantum computing. Supports research and education in this area. Available via pip for easy installation.

Perform provably optimal quantum circuit synthesis using discrete optimization in Julia. Minimize gate counts for efficient, hardware-aware decompositions.

Synthesize reversible circuits using an HDL-based approach. Easily installable via pip, includes a GUI for interactive design and synthesis.

Perform high-speed analysis, synthesis, compilation, and optimization of quantum circuits using a scalable C++17 library designed for performance and flexibility.

Simulate quantum circuits, state-vectors, and density matrices with high performance. Utilizes multithreading, GPUs, and distribution. Stand-alone, requires no installation.

Quantum Computing library leveraging graph building to build efficient quantum circuit simulations.

A Python library for quantum programming using Quil.

Implement quantum circuit unoptimization for zero-noise extrapolation. Increase circuit depth to amplify noise for ZNE without traditional folding.

A tool for quantum circuit compilation developed as part of the Munich Quantum Toolkit (MQT). It builds upon MQT Core, which forms the backbone of the MQT.

A tool for quantum circuit equivalence checking developed as part of the Munich Quantum Toolkit (MQT). It builds upon MQT Core, which forms the backbone of the MQT.

A tool for classical quantum circuit simulation developed as part of the Munich Quantum Toolkit (MQT). It builds upon MQT Core, which forms the backbone of the MQT.

The Munich Quantum Toolkit (MQT) is a collection of software tools for quantum computing that explicitly utilizes this design automation expertise.

Design scalable quantum circuits using a functional language. Features high-level description, automatic reversible synthesis, hierarchical circuits, and programmable transformers.

Simulate open quantum systems dynamics efficiently. Supports various Hamiltonians, time-dependence, and applications in quantum optics, superconducting circuits, and more.

Quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

Simulate and analyze quantum stabilizer circuits at high speed for quantum error correction. Generate detector error models to configure decoders.

Design, simulate, and optimize fault-tolerant quantum computer architectures. Model over 20 hardware imperfections to accelerate logical qubit experiments.

QIR specification defining how to represent quantum programs within the LLVM IR

Access cutting-edge quantum processors and hardware via a fully managed cloud platform. Accelerate research and development for enterprises and academia.

Design, simulate, and execute quantum circuits directly in your web browser. Supports synthesis, transpilation, hybrid algorithms, and multiple hardware platforms.

An open-source Python framework for quantum machine learning, chemistry, and computing. Built by researchers for research, it integrates quantum computation with classical ML and scientific libraries.

Software platform for the development and execution of gate-level quantum computation, providing state-of-the-art performance in circuit compilation. The toolset is designed to aid platform-agnostic software and extract the most out of the available NISQ devices of today.

Develop quantum algorithms using the Q# language, comprehensive tooling, and learning resources like Quantum Katas. Includes a resource estimator and integrates with Azure Quantum.

Write, manipulate, and optimize quantum circuits in Python. Run them on quantum hardware and simulators, leveraging hardware-specific details crucial for noisy intermediate-scale devices.

Build, refine, and execute quantum workloads at scale. This open-source toolkit is the highest-performing SDK for building and transpiling circuits, offering simplified workflows and powerful tools.