The Qiskit Ecosystem, a quantum computing platform developed by IBM, has undergone significant changes to enhance community involvement in algorithm and application development. The qiskit.algorithms module has been migrated to a standalone package, qiskit-algorithms, allowing for more flexible development cycles. The Qiskit applications modules, including qiskit-nature, qiskit-machine-learning, qiskit-optimization, and qiskit-finance, have also been moved to the qiskit-community GitHub organization. New maintainers from IBM Quantum partner institutions, including Algorithmiq, STFC Hartree Centre, and Quantagonia, have been welcomed. The changes aim to simplify the code base and make the libraries more accessible to external contributors.
Qiskit Ecosystem Enhancements
The Qiskit Ecosystem has recently undergone a series of changes, additions, migrations, and upgrades. The primary objective behind these modifications is to provide the community with a more significant role in the development of algorithms and applications. To achieve this, the applications repositories have been moved, the algorithms in Qiskit have been relocated into their own repository, and external partners have been welcomed as additional maintainers to the repositories.
“Our mission is to revolutionise life sciences by exploiting the potential of quantum computing to solve currently inaccessible problems. Algorithmiq’s top quantum chemistry team and their knowledge of state-of-the-art quantum chemistry methods will, together with qiskit’s expert community, tackle some of the greatest quantum chemistry simulation challenges that lie ahead.”
Algorithmiq (qiskit-nature):
Qiskit Ecosystem: Independent Package for Algorithms
Since Qiskit 0.44.0, qiskit.algorithms has been migrated to a new standalone package, qiskit-algorithms. This new library can be found in the qiskit-community GitHub organization and PyPi. This move is significant as it separates the circuit-building tools from the libraries built on top of them. This separation allows algorithm development to move at a different pace than that of the core library. While Qiskit itself aims to provide a stable foundation layer for quantum development, algorithms are still an evolving field of research, and will benefit from more flexible development cycles.
Qiskit Ecosystem: Community-Oriented Algorithms and Applications
The Qiskit applications modules, including qiskit-nature, qiskit-machine-learning, qiskit-optimization, and qiskit-finance, are now also part of the qiskit-community GitHub organization. This move symbolizes the strengthened community focus of the projects, which also involves the newly created qiskit-algorithms. While these packages have always been open-source and welcomed external contributors, most feature development and maintenance efforts were sourced from within IBM Quantum. By joining forces with external partners, the community can have a stronger impact on the direction of these libraries, bringing in new perspectives and areas of expertise.
Qiskit Ecosystem: Welcoming New Maintainers
The algorithms and applications libraries have onboarded new code-owners and maintainers from IBM Quantum partner institutions. These include Algorithmiq for qiskit-nature, STFC Hartree Centre for qiskit-machine-learning, and Quantagonia for qiskit-optimization. These are domain experts in chemistry, machine learning and optimization, as well as active community contributors.
Qiskit Ecosystem: Cleaning Up Legacy Dependencies
To facilitate the community’s greater role in the development of algorithms and applications, legacy dependencies in the applications have been cleaned up to simplify the code base and make the libraries more accessible to external contributors and new maintainers. The newly released versions of the application modules no longer depend on, or support, the now deprecated modules from Qiskit, such as opflow and quantum instance. These modules will no longer be available in the upcoming Qiskit 1.0 release. The applications modules have been updated to use only qiskit-algorithms instead.
“We help UK businesses and organisations of any size to explore and adopt supercomputing, data analytics, AI and emerging technologies for enhanced productivity, smarter innovation and economic growth. Our Qiskit work will be supported by the Hartree National Centre for Digital Innovation (HNCDI) — a collaboration with IBM Research that bridges the gap between academic research and the adoption of new technologies to solve industry challenge and transfer the skills needed to adopt digital solutions.”
STFC Hartree Centre (qiskit-machine-learning)
Qiskit Ecosystem: Summary
The Qiskit Ecosystem, a platform for quantum computing, has undergone significant changes to encourage community involvement in the development of algorithms and applications. This includes separating the algorithm development from the core library, allowing for more flexible development cycles, and welcoming external partners as additional maintainers to the repositories.
“Quantagonia’s mission is to democratize quantum computing, making it accessible and manageable for businesses across sectors, enabling them to leverage this powerful technology for transformative growth and a competitive edge.”
Quantagonia (qiskit-optimization)
- The Qiskit Ecosystem, associated with IBM Quantum, has recently undergone changes to enhance community involvement in the development of algorithms and applications.
- The qiskit.algorithms has been migrated to a standalone package, qiskit-algorithms, separating it from the core library. This allows for more flexible development cycles as algorithms are an evolving field of research.
- Qiskit applications modules, including qiskit-nature, qiskit-machine-learning, qiskit-optimization, and qiskit-finance, have also been moved to the qiskit-community GitHub organization, symbolizing a strengthened community focus.
- New maintainers from IBM Quantum partner institutions, including Algorithmiq, STFC Hartree Centre, and Quantagonia, have been onboarded. These partners bring expertise in areas such as chemistry, machine learning, and optimization.
- Legacy dependencies in the applications have been cleaned up to simplify the code base and make the libraries more accessible to external contributors and new maintainers.
- The changes are part of a shift towards community-led development in the Qiskit Ecosystem, with the aim of enhancing community engagement.
