SlowQuant

SlowQuant is a molecular quantum chemistry program written in Python for classic and quantum computing. Its specialty is unitary parameterized wave functions 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 targeting hardware through Qiskit

Suitable for ideal simulator, shot noise simulator, or quantum hardware via IBM Quantum Hub (Interface via Qiskit)

Variational quantum eigensolver

• Various fermionic ansätze such as; factorized UCC, tUPS, QNP, etc.
• Active-space approximation and orbital-optimzation for all ansätze.
• Analytical electronic gradients with parameter-shift rule.
• Both single reference wave functions, and state-averaged wave fuctions.

Linear response

All linear response is up to SDTQ56 with SD being singlet spin-adapted operators.

• Naive linear response with contribution from orbital response.
• Projected linear response with contribution from orbital response.
• Self-consistent linear response in the active-space.
• State-transfer linear response in the active-space.

Error mitigation techniques

• Post-selection of Pauli-strings in the computational basis.
• Mansatz0 error mitigation technique.

Other

• Qubit-wise commutatitive to save on measurements.
• Pauli saving to save measurements.
• Efficient implementation of fermionic circuits.

State-vector simulator for unitary wave functions

Wave functions

All unitary product state wave functions that are in the quantum computing part of SlowQuant are parameter compatible with the state-vector versions.

• Unitary product state wave functions such as; factorized UCC, tUPS, QNP etc.
• Unitary coupled-cluster without factorization or trotterization.
• Active-space approximation and orbital-optimzation for all wave functions.
• Both single reference wave functions, and state-averaged wave fuctions.

Linear response

All linear response is up to SDTQ56 with SD being singlet spin-adapted operators.

• Naive linear response with contribution from orbital response.
• Projected linear response with contribution from orbital response.
• Self-consistent linear response with contribution from orbital response.
• State-transfer linear response with contribution from orbital response.

Usual features

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

Cited in

• Kjellgren, E.R., Reinholdt, P., Ziems, K.M., Sauer, S., Coriani, S. and Kongsted, J., 2025. Redundant parameter dependencies in conventional and quantum linear response and equation of motion theory for unitary parameterized wave functions. The Journal of Chemical Physics, 163(13).
• Kjellgren, E. R., Ziems, K. M., Reinholdt, P., Sauer, S., Coriani, S., & Kongsted, J. (2025). Exact closed-form expression for unitary spin-adapted fermionic singlet double excitation operators. The Journal of Chemical Physics, 163, 134115 (2025)
• Jensen, P.W., Hedemark, G.S., Ziems, K.M., Kjellgren, E.R., Reinholdt, P., Knecht, S., Coriani, S., Kongsted, J. and Sauer, S.P., 2025. Hyperfine coupling constants on quantum computers: Performance, errors, and future prospects. Journal of Chemical Theory and Computation, 21(16), pp.7878-7889.
• Ziems, K. M., Kjellgren, E. R., Sauer, S. P., Kongsted, J., & Coriani, S. (2025). Understanding and mitigating noise in molecular quantum linear response for spectroscopic properties on quantum computers. Chemical Science.
• 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
Qiskit Estimator	1fe8c4cac7ff5a620b760ee18ff1a8179cf40898
RDM trace correction quantum wave function	e26074fc8aae8dc0f6528308022ad265c5ca18bc
No submatrix saving in proj and all-proj LR	3f5df6818c4dbbb2b54606d0a1a4e00badfb766d
Approxmiate Hermitification in linear response	3f5df6818c4dbbb2b54606d0a1a4e00badfb766d
Approxmiate linear response formalism	3f5df6818c4dbbb2b54606d0a1a4e00badfb766d
KS-DFT	1b9c5669ab72dfceee0a69c8dca1c67dd4b31bfd
MP2	46bf811dfcf217ce0c37ddec77d34ef00da769c3
RPA	46bf811dfcf217ce0c37ddec77d34ef00da769c3
Geometry Optimization	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CIS	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CCSD	46bf811dfcf217ce0c37ddec77d34ef00da769c3
CCSD(T)	46bf811dfcf217ce0c37ddec77d34ef00da769c3
BOMD	46bf811dfcf217ce0c37ddec77d34ef00da769c3