2026-07-15 –, University Hall
Quantum computers promise to tackle strongly correlated molecular systems that defeat classical electronic-structure methods, but realizing quantum utility depends on every stage of the pipeline, not just the quantum algorithm. QDK/Chemistry, an open-source package in the Microsoft Quantum Development Kit, treats this entire pipeline as a single, modular Python framework. Immutable data classes and stateless algorithms with fixed interfaces let researchers swap backends without changing application code. This talk introduces QDK/Chemistry's composable architecture, shows how classical and quantum stages interoperate to minimize quantum resources, and offers design patterns applicable beyond quantum computing.
QDK/Chemistry is an open-source package in the Microsoft Quantum Development Kit that provides a composable, end-to-end framework for quantum chemistry on quantum computers. It spans every stage of the quantum-classical workflow, from molecular setup and classical reference calculations through active-space reduction, Hamiltonian construction, fermion-to-qubit encoding, state preparation, and measurement. These stages are connected through a unified Python API backed by a high-performance C++ core.
The design rests on immutable data classes and stateless algorithms with fixed interfaces. A factory/registry plugin system makes every algorithm slot interchangeable: a researcher can swap a native backend for third party packages (e.g. PySCF, Qiskit, OpenFermion), or a custom implementation by changing a single string, with no rewiring of application code. Benchmarking, backend mixing, and custom extension are first-class operations rather than rewrites.
Because every stage is an interchangeable module, classical methods generate the high-quality inputs that quantum algorithms depend on, and the same classical results serve as baselines for judging where quantum methods offer genuine utility over the classical state of the art. The emphasis throughout is on minimizing quantum resources at every step and on making workflows reproducible and shareable. Reproducible serialization in XYZ, JSON, and HDF5 formats supports shareable, benchmarkable workflows across groups.
QDK/Chemistry is available on PyPI, with documentation, examples, and companion datasets openly available.
David Williams-Young is a Principal Quantum Software Architect at Microsoft Quantum, where he serves as software lead for quantum applications. His work focuses on quantum computing applications in chemistry and materials science, including quantum algorithms, classical simulation methods, and the development of tools that bridge quantum computing and computational many-body theory. Prior to joining Microsoft, he was as a Scientist in the Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory, where he developed exascale electronic structure methods and software for DOE Leadership Computing Facilities. He received his Ph.D. in Chemistry from the University of Washington, specializing in relativistic electronic structure theory. He is the author of numerous open-source computational chemistry libraries and has served as a major contributor numerous quantum chemistry software packages.