Modular by design. Scalable to the fault-tolerant era.
OpenQARP is a comprehensive open-source software package for quantum computing research and application development. Combine reusable building blocks to prototype quickly and develop quantum applications across a range of computing environments.
Features
~70%
reduction in code volume
70+
composable building blocks
20+
ready-to-run algorithms
100+
software components
Build quantum applications with reusable modules.
Benefits of using OpenQARP
- Build faster with reusable modules
- Turn custom algorithms and workflows into reusable building blocks
- Focus on application development rather than low-level circuit coding
- Accelerate experimentation and prototyping
Scale from laptop to large-scale quantum simulators, with fault-tolerant quantum computing in view.
Develop quantum applications across multiple computing environments, from your laptop to GPU-accelerated systems and large-scale quantum simulators.
An applications library, not a gate library.
State of the art, batteries included. Every entry below ships as a runnable Jupyter Notebook in examples/ — the documentation is executable.
- Ground & excited states
- VQE, ADAPT-VQE, VQD, ADAPT-VQD, SSVQE, QSE, oo-pCCD / UpCCD
- Spectra & dynamics
- QPE, DOS-QPE, MMQCELS, QMEGS, VFF, Trotter, qDRIFT, Qubitization, QSVT
- Optimization & sampling
- QAOA, PCE, Quantum Monte Carlo
- Techniques
- Circuit cutting, Postselection, Measurement grouping, SABRE routing, Unitary synthesis, Symbolic parameters
OpenQARP Use Cases
Materials Science Quantum Simulation
We conducted quantum simulations to predict the optical properties and electronic states of molecules in solution, supporting materials discovery and evaluation. The approach may also contribute to future drug discovery research.
Traditionally, developing quantum chemistry applications required researchers to implement multiple algorithms and computational workflows individually, resulting in a significant development burden.
In this study, OpenQARP's quantum chemistry module (SSVQE) enabled efficient implementation and evaluation of ground-state and excited-state analyses. The results showed trends consistent with both conventional computational methods and experimental observations, demonstrating the potential of quantum simulation for materials science applications.
Looking ahead, the ability to evaluate promising material candidates before experimentation may help improve screening efficiency and reduce experimental costs.
Financial Portfolio Optimization
We developed and evaluated quantum algorithms for portfolio optimization involving large numbers of financial assets.
Traditionally, the scale of the optimization problem grows rapidly as the number of assets increases. Researchers also needed to prepare new implementations and evaluation environments whenever they wanted to test a new quantum algorithm, making iterative experimentation difficult.
In this study, we leveraged PCE (Pauli Correlation Encoding), implemented in OpenQARP, to efficiently develop and evaluate a quantum approach to portfolio optimization.
In the future, this work may contribute to more advanced investment strategies, improved risk analysis, and the realization of large-scale quantum finance applications.
Related Links
Press release
