IQM Quantum Computers (IQM) has released the IQM Quantum Approximate Optimization Algorithm (QAOA) Library, an open-source toolkit built in Python. The library provides a full-stack interface designed to support quantum optimization by enabling users to define problems and execute various QAOA configurations on hardware. This release aims to make QAOA more accessible for experimentation and prototyping.
The IQM QAOA Library is used for solving Quadratic Unconstrained Binary Optimization (QUBO) problems. It offers multiple methods for problem definition, including direct matrix Q input, graph representations for problems like MaxCut, and random instance generators for benchmarking. The library incorporates advanced training strategies such as exact energy expectation via tensor networks and analytical solutions for sparse problem instances. It also includes specialized transpilers, such as a swap network transpiler for grid-based Quantum Processing Units (QPUs), a greedy transpiler for sparse problems, and a transpiler tailored for IQM’s star-shaped QPU topology. Users can submit jobs directly to IQM Resonance or export circuits for other platforms.
The release of this open-source library is intended to facilitate the exploration of quantum optimization. IQM continues to support other quantum programming frameworks like Qiskit, Cirq, Cuda Quantum, and TKET, ensuring flexibility for users. Future plans for the library include additional built-in problem types, support for parameter concentration training, and integration with Qrisp. This toolkit provides a resource for researchers and developers working on combinatorial optimization problems with quantum algorithms.
Read more about this release in the IQM blog post here, access the IQM QAOA Library documentation here, and view the IQM Quantum Computers LinkedIn post here.
July 15, 2025
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