IBM Releases New Version of Qiskit; Adds Additional Libraries for Natural Sciences and Machine Le...

IBM has released version 0.25 of its Qiskit quantum programming platform with the biggest changes related to a restructuring and extension of their application libraries. Previously they had introduced a package called Qiskit Aqua that included application modules for chemistry, AI, optimization and finance. Qiskit Aqua was a separate package independent of the core Qiskit Terra. In this new release they have moved and extended these application module into Qiskit Terra with application modules now called Qiskit Nature, Finance, Optimization and Machine Learning. One of the advantages to this new structure is that it will allow them to update one of these application modules without impacting the others, something they could not do with Qiskit Aqua. Although Qiskit Aqua is still present in release 0.25 to maintain some continuity for a while, it will be removed in the future and IBM has released a migration guide so that users can update their code for the new structure.

Besides the restructuring, IBM is expanding its support for various natural sciences applications by including new routines to model and solve domain-specific problems in the physics, material science and biology domains in addition to the previously supported chemistry domain. Also, the new application module for Machine Learning represents a major upgrade from what was previously available in Qiskit. This module introduces building blocks including Quantum Kernels and Quantum Neural Networks that can be used in different applications, such as classification and regression. In addition, it includes an integration capability with PyTorch, a popular open source machine learning library.

IBM has posted three blog articles on Medium to describe these changes. A general description of the restructuring can be found here, information about the introduction of Quantum Nature can be found here and information about Quantum Machine Learning can be viewed here. Additional details about these modules as well as all of Qiskit including and links to the documentation, GitHub repositories, migration documents, etc. can be found on the Qiskit home page at Qiskit.org.

April 10, 2021