QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications
Nathan A. Baker, Brian Bilodeau, Chi Chen, Yingrong Chen, Marco Eckhoff, Alexandra Efimovskaya, Piero Gasparotto, Puck van Gerwen, Rushi Gong, Kevin Hoang, Zahra Hooshmand, Andrew J. Jenkins, Conrad S. N. Johnston, Run R. Li, Jiashu Liang, Hongbin Liu, Alexis Mills, Maximilian Mörchen, George Nishibuchi, Chong Sun, Bill Ticehurst, Matthias Troyer, Jan P. Unsleber, Stefan Wernli, David B. Williams-Young, Boqin Zhang
quant-phphysics.chem-phphysics.comp-phSubmitted Jan 21, 2026• Announced Jan 22, 2026• 1 citation
We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.
We present QDK/Chemistry, a software toolkit for quantum chemistry workflows targeting quantum computers. The toolkit addresses a key challenge in the field: while quantum algorithms for chemistry have matured considerably, the infrastructure connecting classical electronic structure calculations to quantum circuit execution remains fragmented. QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods, enabling researchers to compose workflows from interchangeable components. In addition to providing native implementations of targeted algorithms in the quantum-classical pipeline, the toolkit builds upon and integrates with widely used open-source quantum chemistry packages and quantum computing frameworks through a plugin system, allowing users to combine methods from different sources without modifying workflow logic. This paper describes the design philosophy, current capabilities, and role of QDK/Chemistry as a foundation for reproducible quantum chemistry experiments.
As quantum computing hardware systems continue to advance, the research and development of performant, scalable, and extensible software architectures, languages, models, and compilers is equally as important to bring this novel coprocessing capability to a diverse group of domain computational scientists. For the field of quantum chemistry, applications and frameworks exist for modeling and simulation tasks that scale on heterogeneous classical architectures, and we envision the need for similar frameworks on heterogeneous quantum-classical platforms. Here, we present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software that specifically targets chemistry applications. We review the fundamental design features in XACC, with special attention to its extensibility and modularity for key quantum programming workflow interfaces and provide an overview of the interfaces most relevant to simulations of chemistry. A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented, while also illustrating the various APIs that would enable the community to extend, modify, and devise new algorithms and applications in the realm of chemistry.
We introduce QFlux, an open-source Python package for simulating quantum dynamics in chemical systems across multiple levels of theory using a unified classical and quantum computational framework. QFlux integrates deterministic wavefunction propagation, Lindblad master equations, generalized quantum master equations (GQMEs), operator-splitting techniques, and variational quantum algorithms within a modular architecture designed for systematic benchmarking and hybrid classical-quantum execution. The framework supports both closed-and open-system dynamics, including Markovian and non-Markovian regimes, and provides explicit mappings between classical propagators and quantum circuit constructions executable on NISQ hardware. Built atop established scientific and quantum software ecosystems, QFlux emphasizes reproducibility, interoperability, and controlled cross-validation between numerical approximations and hardware-oriented algorithms. Part I in this series of papers presents the theoretical foundations, software design principles, and methodological scope of QFlux, and positions it within the broader landscape of classical, tensor-network, and quantum-circuit-based simulation platforms. The series is designed to serve as a teaching reference for graduate students, a practical guide for researchers implementing custom quantum simulations, and a foundational reference for the broader QFlux ecosystem.
Quantum algorithms and quantum hardware are advancing towards a promising paradigm for scientific applications. However, translating domain-specific problems into executable hybrid quantum-classical workflows remains a significant barrier for application researchers due to the required expertise in quantum algorithms, nuances in quantum programming, and hardware-aware system integration. At the same time, AI and primarily LLM based agents are increasingly capable of interpreting natural-language intent, reasoning over complex workflows, and translating high-level objectives into executable code and building computational pipelines. In this work, we introduce SQD Agent, an LLM-based agentic framework that translates natural-language user intent into executable workflows for Quantum Chemistry applications where algorithms from the Sample-Based Quantum Diagonalization (SQD) family are used. By automating this translation, SQD Agent reduces the level of human expertise and configuration overhead required, thereby simplifying experimentation in hybrid quantum-classical settings for application researchers new to quantum. SQD Agent adopts a modular and extensible architecture that supports seamless integration of heterogeneous quantum backends, classical solvers, and workflow components, ensuring adaptability to rapidly evolving quantum ecosystems. The framework further incorporates interactive capabilities for on-demand profiling, bottleneck analysis, resource optimization, intelligent result caching, and convergence visualization. Key features include quantum chemistry experiments, error mitigation on real quantum hardware, together with analysis of candidate mitigation schemes in terms of their potential error-recovery behavior and computational budget, helping users understand their practical trade-offs and decide which strategies to explore in subsequent experiments.
The Ghent Quantum Chemistry Package (GQCP) is an open-source electronic structure software package that aims to provide an intuitive and expressive software framework for electronic structure software development. Its high-level interfaces (accessible through C++ and Python) have been specifically designed to correspond to theoretical concepts, while retaining access to lower-level intermediates and allowing structural run-time modifications of quantum chemical solvers. GQCP focuses on providing quantum chemical method developers with the computational "building blocks" that allow them to flexibly develop proof of principle implementations for new methods and applications up to the level of two-component spinor bases.
macroqc is a quantum chemistry software package designed for high-accuracy electronic structure computations and large-scale molecular simulations. The macroqc framework provides efficient implementations of energies and analytic gradients for a wide range of many-body perturbation theory and coupled-cluster (CC) methods. In particular, the analytic gradient implementations of second-order Møller-Plesset perturbation theory (MP2), coupled-cluster singles and doubles (CCSD), and CCSD with perturbative triples [CCSD(T)] exhibit excellent computational performance, even in comparison with established commercial quantum chemistry programs. In addition to conventional correlated methods, macroqc incorporates several advanced capabilities, including molecular fragmentation techniques for ground and excited state molecular systems and density-fitted analytic gradients. The software also supports interoperability with selected external packages through a flexible plugin framework, allowing developers to construct customized interfaces and extensions. The notable features of macroqc are the linear-scaling systematic molecular fragmentation (LSSMF) and the cluster-in-molecule (CIM) approaches, which enable the application of high-level post-Hartree-Fock approaches to large molecular systems that would otherwise be computationally prohibitive. Furthermore, the new machine learning (ML) library enables the integration of data-driven approaches into quantum chemical workflows, providing efficient tools for developing and applying ML-based models for molecular property prediction and electronic structure computations. With its combination of efficient implementations, advanced theoretical methods, and extensible design, macroqc is expected to serve as a useful platform for a broad range of computational chemistry applications.
Quantum computers provide new opportunities for quantum chemistry. In this article,we present a versatile, extensible, and efficient software package, named Q2Chemistry, for developing quantum algorithms and quantum inspired classical algorithms in the field of quantum chemistry. In Q2Chemistry, the wave function and Hamiltonian can be conveniently mapped into the qubit space, then quantum circuits can be generated corresponding to a specific quantum algorithm already implemented in the package or newly developed by the users. The generated circuits can be dispatched to either a physical quantum computer, if available, or to the internal virtual quantum computer realized by simulating quantum circuits on classical computers. As demonstrated by our benchmark simulations, Q2Chemistry achieves excellent performance in simulating medium scale quantum circuits using the matrix product state algorithm. Applications of Q2Chemistry to simulate molecules and periodic systems are given with performance analysis.