Open Quantum Application Research Package

OpenQARP (code name)

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.

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Features

~70%

reduction in code volume

70+

composable building blocks

20+

ready-to-run algorithms

100+

software components

Build quantum applications with reusable modules.

Illustration showing how OpenQARP simplifies quantum application development by replacing many small fragmented routines with a smaller number of larger reusable building blocks.

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

ADAPT-VQE implementation example using OpenQARP. The Python code demonstrates a quantum chemistry workflow from molecular system definition and Hamiltonian generation to ansatz construction and energy estimation.

Code example (ADAPT-VQE using OpenQARP)

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Documentation

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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.

Illustration showing how the same quantum application can be deployed across multiple computing environments, from laptop-based development to GPU-accelerated systems and large-scale quantum simulators running on supercomputers.

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Documentation

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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

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Documentation

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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.

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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.