QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications
- URL: http://arxiv.org/abs/2601.15253v1
- Date: Wed, 21 Jan 2026 18:35:40 GMT
- Title: QDK/Chemistry: A Modular Toolkit for Quantum Chemistry Applications
- Authors: 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,
- Abstract summary: We present QDK/Chemistry, a software toolkit for quantum chemistry targeting quantum computers.<n>QDK/Chemistry provides this infrastructure through a modular architecture that separates data representations from computational methods.<n>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.
- Score: 27.823025732308906
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Quantum simulation of actinide chemistry: towards scalable algorithms on trapped ion quantum computers [0.0]
This paper compares the method of quantum computed moments (QCM) with a single-ancilla version of quantum phase estimation (QPE)<n>We derive electronic structure descriptions from a series of representative chemical models and compute the energetics from quantum experiments on Quantinuum's H-series ion trap devices using up to 19 qubits.
arXiv Detail & Related papers (2025-10-29T16:42:24Z) - Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
arXiv Detail & Related papers (2025-10-08T17:59:51Z) - The Evolution of IBM's Quantum Information Software Kit (Qiskit): A Review of its Applications [0.0]
IBM's open-source quantum computing toolkit 'Qiskit' has become a key player in this space.<n>This survey provides a systematic review of how Qiskit has evolved and what it has contributed to several critical fields.<n>We show how Qiskit facilitates hybrid classical-quantum and enables the deployment of algorithms on physical quantum hardware.
arXiv Detail & Related papers (2025-08-17T05:22:55Z) - Shortcut to Chemically Accurate Quantum Computing via Density-based Basis-set Correction [0.4909687476363595]
We embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC)
We provide a shortcut towards chemically accurate quantum computations by approaching the complete-basis-set limit.
The resulting approach self-consistently accelerates the basis-set convergence, improving electronic densities, ground-state energies, and first-order properties.
arXiv Detail & Related papers (2024-05-19T14:31:01Z) - Benchmarking the Variational Quantum Eigensolver using different quantum
hardware [0.0]
The Variational Quantum Eigensolver (VQE) is a promising quantum algorithm for applications in chemistry.
We present results using the VQE for the simulation of the hydrogen molecule, comparing superconducting and ion trap quantum computers.
arXiv Detail & Related papers (2023-05-11T18:56:07Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Open Source Variational Quantum Eigensolver Extension of the Quantum
Learning Machine (QLM) for Quantum Chemistry [0.0]
We introduce a novel open-source QC package, denoted Open-VQE, providing tools for using and developing chemically-inspired adaptive methods.
It is able to use the Atos Quantum Learning Machine (QLM), a general programming framework enabling to write, optimize simulate computing programs.
Along with OpenVQE, we introduce myQLMFermion, a new open-source module (that includes the key QLM ressources that are important for QC developments)
arXiv Detail & Related papers (2022-06-17T14:24:22Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum
Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-05-17T16:17:57Z) - A backend-agnostic, quantum-classical framework for simulations of
chemistry in C++ [62.997667081978825]
We present the XACC system-level quantum computing framework as a platform for prototyping, developing, and deploying quantum-classical software.
A series of examples demonstrating some of the state-of-the-art chemistry algorithms currently implemented in XACC are presented.
arXiv Detail & Related papers (2021-05-04T16:53:51Z) - Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-01-20T14:20:14Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.