Effortless memory for quantum computing fundamentals
Education
Learn quantum computing and mechanics fundamentals with a free, interactive introduction. Utilizes a mnemonic medium for effortless recall of complex concepts.
Visual reasoning for quantum circuits and protocols
High-level Quantum ProgrammingZX Calculus
A graphical language extending circuit diagrams, revealing compositional structure for visual reasoning. Enables state-of-the-art circuit optimization and error correction design.
Advancing quantum tech through grants, research, and community
CommunityEducation
A 501(c)3 non-profit fostering the quantum technology ecosystem. Provides microgrants, conducts open-source research, and hosts a community for broad benefit.
Provides client-side access for direct pulse-level control of quantum computers. Enables fine-grained hardware interaction for advanced experiments and calibration.
High-level language for scalable quantum algorithm development
High-level Quantum ProgrammingQuantum Circuits
Develop quantum algorithms with a high-level language. Features typed variables, automatic uncomputation, modularity, integrated arithmetic, and broad hardware compatibility.
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.
