Automate superconducting quantum circuit design with this Python library. Generate multi-layer 2D geometry for QPUs, masks, and EBL patterns using KLayout.
Simulate and visualize surface code error correction.
Quantum Error CorrectionVisualization
Simulate and visualize quantum error correction on surface codes. Inspect decoding processes, benchmark decoder performance, and explore modular implementations of codes, errors, and decoders.
Unified classical, LDPC, and quantum coding in Julia.
Quantum Error Correction
Julia library for classical, LDPC, and quantum coding theory. Construct, analyze, and work with various code families, leveraging Oscar framework for algebraic structures.
Design quantum pulses and schedules interactively.
Experiment AutomationPulse-level control+1 more
Interactively design quantum pulses and schedules using a Python widget library. Integrate directly into Jupyter notebooks, JupyterLab, and IPython kernels for hardware control.
Define, parametrize, and sequence complex pulses for qubit control experiments. Translate high-level pulse representations to hardware-specific instructions and waveforms.
Define precise quantum pulse sequences with a Python DSL. Manage qubit and instrument configurations. Includes examples for single and two-qubit experiments.
Platform-agnostic runtime for quantum job management
High-level Quantum ProgrammingTranspilers
Manage quantum jobs across diverse hardware and software platforms. This Python SDK provides a platform-agnostic runtime, configurable transpilation, and modular tools for the full job lifecycle.
Run AI, ML, and scientific code on any cloud or cluster.
HPCHybrid computing+2 more
Orchestrate machine learning, HPC, and quantum workflows across diverse compute environments. Execute Python functions on any cloud or cluster with minimal code changes.
A common Python framework for physics experiment data acquisition.
Control electronicsExperiment Automation
Python-based data acquisition framework for physics experiments. Reduces setup time, encourages code sharing, and leverages modern software practices for efficient research.
Enable research in mixed-dimensional qudit computing
Quantum CircuitsQuantum Information
Provides a framework for mixed-dimensional qudit quantum computing. Supports research and education in this area. Available via pip for easy installation.
Control quantum experiments with precise timing and low latency using a flexible, programmable system. Features Python-based programming, FPGA execution, and open hardware.
High-performance C++ library for quantum circuit synthesis.
CompilersOptimization+2 more
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 fast, anywhere, no setup.
Quantum CircuitsSimulators
Simulate quantum circuits, state-vectors, and density matrices with high performance. Utilizes multithreading, GPUs, and distribution. Stand-alone, requires no installation.
Solve time-dependent open quantum systems in parallel.
Simulators
Solve time-dependent open quantum systems efficiently in parallel. Supports general systems like Jaynes-Cummings and spin-boson models, plus quantum information features.
Accelerate quantum circuit simulation with low-level speed.
Simulators
Accelerate quantum circuit simulation using a fast, lightweight, modular library written in Assembly. Supports x86 and ARM64 architectures for high-performance computation.
High-performance simulation for multi-core and multi-node systems
Simulators
Achieve high-performance quantum circuit simulation on distributed systems. Optimized for multi-core and multi-node architectures using MPI for scalable state vector representation.
Unified platform for hybrid quantum-classical computing
HPCHybrid computing+1 more
An open-source platform integrating QPUs, GPUs, and CPUs for hybrid quantum-classical computing. Enables QPU-agnostic development and offers GPU-accelerated simulations.
Simulate large, noisy, and parametric quantum circuits rapidly using a parallelized C++/Python library with GPU acceleration and built-in noise models.
Numerical tools for quantum information theory research
Quantum Information
Provides numerical tools for quantum information theory research, analyzing states, channels, measurements, entanglement, and nonlocal games. Supports researchers and educators.
Deploy established error-robust quantum control protocols.
Control electronicsQuantum Cloud
Access and deploy established error-robust quantum control protocols. Easily integrate techniques from the literature onto custom hardware, cloud platforms, or Fire Opal.
Intuitive Python programming for diverse quantum backends.
CompilersHigh-level Quantum Programming+1 more
Implement quantum programs in Python with intuitive syntax. Translate code for execution on classical simulators or actual quantum hardware. Open-source framework.
Access and program D-Wave quantum systems with Python.
High-level Quantum ProgrammingHybrid computing+1 more
Provides a comprehensive Python SDK and command-line tools for developing applications on D-Wave quantum computers and hybrid solvers. Includes API, CLI, and package documentation.
Build and train quantum neural networks with a cloud-integrated platform. Offers easy-to-use APIs, comprehensive tutorials, toolkits for chemistry/optimization, and large-scale simulation.
Optimizes Quil programs for specific hardware architectures using advanced compilation techniques. Provides both binary and server interfaces for flexible integration.
Simulate Quil programs efficiently with a high-performance, featureful virtual machine. Model quantum computer characteristics and deploy as a binary or server.
