SlowQuant

SlowQuant is a molecular quantum chemistry program written in Python for classic and quantum computing. Its specialty is unitary coupled cluster and (time-dependent) linear response in various novel parametrization schemes. Even the computational demanding parts are written in Python, so it lacks speed, thus the name SlowQuant.

Documentation can be found at:

http://slowquant.readthedocs.io/en/latest/

Quantum Computing, Variational Quantum Eigensolver

• tUCCSD (trotterized UCCSD through Qiskit)
• fUCCSD (factorized UCCSD)
• tUPS (tiled Unitary Product State)
• Naive Linear Response SD, with singlet spin-adapted operators
• Projected Linear Response SD, with singlet spin-adapted operators

These features are also implemented with the active-space approximation and orbital-optimization. Suitable for ideal simulator, shot noise simulator, or quantum hardware via IBM Quantum Hub (Interface via Qiskit)

Conventional Computing, Unitary Coupled Cluster

Current implementation supports:

• UCCSD, spin-adapted operators
• UCCSDTQ56
• Linear Response SD, spin-adapted operators
• Linear Response SDTQ56

These features are also implemented with the active-space approximation and orbital-optimization.

Usual features

SlowQuant also got some conventional methods, such as Hartree-Fock and molecular integrals. Just use PySCF instead.

Cited in

• Ziems, K. M., Kjellgren, E. R., Sauer, S., Kongsted, J., & Coriani, S. (2024). Understanding and mitigating noise in molecular quantum linear response for spectroscopic properties on quantum computers. arXiv preprint arXiv:2408.09308.
• Kjellgren, E. R., Reinholdt, P., Ziems, K. M., Sauer, S., Coriani, S., & Kongsted, J. (2024). Divergences in classical and quantum linear response and equation of motion formulations. The Journal of Chemical Physics, 161(12).
• von Buchwald, T. J., Ziems, K. M., Kjellgren, E. R., Sauer, S. P., Kongsted, J., & Coriani, S. (2024). Reduced density matrix formulation of quantum linear response. Journal of Chemical Theory and Computation, 20(16), 7093-7101.
• Chan, M., Verstraelen, T., Tehrani, A., Richer, M., Yang, X. D., Kim, T. D., ... & Ayers, P. W. (2024). The tale of HORTON: Lessons learned in a decade of scientific software development. The Journal of Chemical Physics, 160(16).
• Ziems, K. M., Kjellgren, E. R., Reinholdt, P., Jensen, P. W., Sauer, S. P., Kongsted, J., & Coriani, S. (2024). Which options exist for NISQ-friendly linear response formulations?. Journal of Chemical Theory and Computation, 20(9), 3551-3565.
• Chaves, B. D. P. G. (2023). Desenvolvimentos em python aplicados ao ensino da química quântica.
• Lehtola, S., & Karttunen, A. J. (2022). Free and open source software for computational chemistry education. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5), e1610.

Feature Graveyard

Feature	Last living commit
KS-DFT	1b9c5669ab72dfceee0a69c8dca1c67dd4b31bfd
MP2	46bf811dfcf217ce0c37ddec77d34ef00da769c3
RPA	46bf811dfcf217ce0c37ddec77d34ef00da769c3
Geometry Optimization	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CIS	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CCSD	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CCSD(T)	46bf811dfcf217ce0c37ddec77d34ef00da769c3
BOMD	46bf811dfcf217ce0c37ddec77d34ef00da769c3