A high-level abstraction layer bridging classical systems and the quantum frontier. Write once, execute across any QPU provider.
Write once, run anywhere. MPQP provides a consistent interface for complex circuit designs, handling low-level translations automatically.
Designed for pedagogical clarity. Ideal for teaching quantum logic without hardware-specific syntax bloat.
Licensed under GPLv3. Community-driven development focused on transparency and accessibility.
One framework to rule them all. No need to juggle multiple vendor SDKs for a single project.
Perfectly aligned with emerging hardware. Our abstraction layer adapts so your code doesn't have to.
MPQP is not just another library; it is a high-level programming framework designed to provide a unified syntax for the most popular quantum SDKs including Qiskit, AWS Braket, and Google Cirq.
By acting as an intelligent intermediary, MPQP allows researchers and developers to focus on algorithmic logic rather than the idiosyncrasies of specific hardware providers.
# MPQP Unified Circuit
import mpqp
circuit = mpqp.Circuit(qubits=5)
circuit.h(0)
circuit.cnot(0, 1)
circuit.measure_all()
# Deploy to any provider
provider = mpqp.get_provider("braket")
job = provider.run(circuit, shots=1024)
print(job.result())
Use a single library to develop modules and deploy algorithms automatically on any platform.
Develop and execute solutionsindependently of hardware and language evolution.
Discover basics of Quantum Computing with tutorials and exercises. Access simulators and machines.
Perform easy benchmarking and parallel execution on several hardware machines.
Tailored for industrial-scale simulation and productionworkflows. Robust, secure, and fully supported.
Request Enterprise DemoFocused on research, teaching, and algorithm validation.Special access to hardware resources.
MPQP Stands for Multi-Platform Quantum Programming and is an open source Python library developed by ColibriTD.
The library is designed to simplify the development, analysis, and execution of quantum programs. It provides a structured framework to manipulate quantum circuits, manage parameters, and organize complex quantum workflows.
One of the key objectives of MPQP is portability. Quantum programs often need to run on different infrastructures such as simulators or various quantum hardware providers. With MPQP, the same quantum code can be executed on multiple backends without rewriting the program for each platform.
MPQP acts as a foundational layer in the ColibriTD software stack. It is used internally to develop and deploy algorithms such as Hybrid Differential Equation Solver (H-DES) and to support platforms such as Quantum Innovative Computing Kit (QUICK).
Because MPQP is open source and developed in France by ColibriTD, it can be used by researchers, academic institutions, and industrial teams looking for a flexible framework to experiment with quantum computing.
MPQP was designed to simplify the management of quantum programs in research and industrial environments.
One advantage is code portability. A quantum program written with MPQP can run across different simulators and quantum hardware platforms without being rewritten. This allows users to compare infrastructures and run experiments on multiple backends.
Another advantage is the ability to manipulate and analyze quantum circuits programmatically. Researchers can transform circuits, inspect their structure, test different configurations, and integrate them into larger software pipelines.
MPQP also helps structure complex projects. As quantum programs grow larger, managing circuits, parameters, and hybrid workflows becomes difficult. MPQP provides a clear software architecture that helps organize these components.
Finally, because MPQP is open source, research teams and companies can adapt it to their own workflows while benefiting from a framework maintained by ColibriTD.
MPQP can be used by industrial R&D teams exploring quantum computing.
Companies can use the library to prototype quantum algorithms, integrate quantum experiments into existing software environments, and test different infrastructures without rewriting their code.
This is particularly useful for companies that collaborate with multiple quantum hardware providers. Instead of maintaining separate implementations for each platform, MPQP allows teams to keep a single codebase and execute it across different backends.
MPQP can also serve as a development layer to build internal quantum applications or to integrate algorithms such as H-DES into engineering workflows.
For companies working in sensitive sectors such as aerospace, defense, or critical infrastructure, MPQP also provides a quantum software framework developed in France and fully controlled by the ColibriTD team.
MPQP is well suited for research laboratories and universities working on quantum computing.
For researchers, the library provides tools to design, analyze, and transform quantum circuits programmatically. This makes it easier to explore new algorithmic ideas, test different circuit structures, and compare results across hardware platforms.
Researchers can also use MPQP to benchmark different quantum computers by executing the same program on several backends.
In academic environments, MPQP can support teaching activities in quantum computing courses. Students can learn how quantum circuits are structured and executed while using a consistent programming framework.
Because the library is written in Python and open source, it can easily be integrated into teaching materials, research projects, and collaborative academic programs.
Benchmarking quantum algorithms requires the ability to run several circuits, compare results, and test different execution environments.
MPQP provides a framework that allows researchers and engineers to structure these experiments. Users can implement several algorithmic approaches for the same problem and execute them in a controlled environment.
Because the library supports multiple backends, teams can compare how different algorithms behave on simulators and on real quantum hardware.
This allows researchers and companies to evaluate which algorithmic approach performs best for a given scientific or computational problem.
Comparing quantum hardware is an important step when evaluating quantum computing technologies.
Different providers use different physical implementations and infrastructures. As a result, performance, noise levels, and circuit behavior can vary significantly.
MPQP allows users to run the same quantum program across several backends without rewriting the code. This makes it easier to evaluate how a circuit behaves on different quantum processors or simulators.
By running identical experiments across multiple infrastructures, researchers and companies can analyze performance differences, measure stability, and benchmark quantum platforms for their specific use cases.
