pith. sign in

arxiv: 2506.20587 · v1 · submitted 2025-06-25 · 🪐 quant-ph · cond-mat.str-el· physics.bio-ph· physics.chem-ph· physics.comp-ph

How to use quantum computers for biomolecular free energies

Jakob G\"unther , Thomas Weymuth , Moritz Bensberg , Freek Witteveen , Matthew S. Teynor , F. Emil Thomasen , Valentina Sora , William Bro-J{\o}rgensen
show 13 more authors
Raphael T. Husistein Mihael Erakovic Marek Miller Leah Weisburn Minsik Cho Marco Eckhoff Aram W. Harrow Anders Krogh Troy Van Voorhis Kresten Lindorff-Larsen Gemma Solomon Markus Reiher Matthias Christandl
This is my paper

Pith reviewed 2026-05-19 07:46 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.str-elphysics.bio-phphysics.chem-phphysics.comp-ph
keywords quantum computingfree energy calculationsquantum embeddingbiomolecular modelingmachine learningmolecular recognitiondrug binding
0
0 comments X

The pith

A two-fold quantum embedding strategy with machine learning transfers accurate quantum energies from small substructures to large biomolecular systems for free energy calculations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a method to obtain reliable free energy values for biochemical processes by linking high-accuracy quantum mechanical data on small molecular fragments to the full potential energy surface of large complexes. It applies a two-fold quantum embedding where innermost cores receive the highest level of quantum treatment and machine learning models the surrounding environment and motions. This addresses the limitation that full quantum calculations are feasible only for small systems while free energies require both electronic accuracy and entropic contributions from large molecules and solvent. The approach is shown viable first with classical quantum chemistry on a ruthenium anticancer drug binding to its protein target, then extended to outline what quantum computer capabilities would be needed to replace those classical steps.

Core claim

The authors claim that a two-fold quantum embedding strategy combined with machine learning can consistently map accurate quantum-mechanical energies computed on small substructures onto the overall potential energy surface of large biomolecular complexes, thereby enabling a computational pipeline that incorporates quantum-computed energies into free energy calculations once hardware requirements are satisfied.

What carries the argument

Two-fold quantum embedding strategy that places high-accuracy quantum treatment on innermost molecular cores while using machine learning to propagate data to the full system.

If this is right

  • Free energy calculations become feasible for molecular recognition events that involve both strong electronic interactions and large-scale conformational entropy.
  • The FreeQuantum pipeline can directly ingest energies from quantum computers once those devices deliver the required accuracy on the embedded cores.
  • Biochemical processes such as cell signaling and drug action can be modeled with quantum-level electronic accuracy without treating the entire macromolecule at that level.
  • The combination of quantum speedups for electron correlation with classical machine learning for large-scale motions yields practical predictions for systems beyond current classical limits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same embedding logic could be tested on other recognition problems such as enzyme-substrate binding or protein-protein interfaces.
  • If the error control holds, the method would allow systematic improvement by increasing the size or accuracy of the quantum cores without rebuilding the entire simulation.
  • Experimental free-energy measurements on additional drug-target pairs would provide a direct benchmark for validating the pipeline's accuracy claims.

Load-bearing premise

The two-fold embedding and machine learning transfer of quantum data from small substructures to the full biomolecular complex introduces no uncontrolled errors that would invalidate the computed free energies.

What would settle it

A direct comparison in which the free energy obtained from the embedded pipeline for the ruthenium drug-protein interaction deviates substantially from either a full-system high-accuracy reference calculation or from experimental binding free energy.

Figures

Figures reproduced from arXiv: 2506.20587 by Anders Krogh, Aram W. Harrow, F. Emil Thomasen, Freek Witteveen, Gemma Solomon, Jakob G\"unther, Kresten Lindorff-Larsen, Leah Weisburn, Marco Eckhoff, Marek Miller, Markus Reiher, Matthew S. Teynor, Matthias Christandl, Mihael Erakovic, Minsik Cho, Moritz Bensberg, Raphael T. Husistein, Thomas Weymuth, Troy Van Voorhis, Valentina Sora, William Bro-J{\o}rgensen.

Figure 1
Figure 1. Figure 1: Biomolecular quantum simulation quadrangle: The complexity of the electronic structure problem is [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workflow of the FreeQuantum pipeline: After a structure preparation step for the host protein and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Host-guest complex of the chaperone BiP (Binding immunoglobulin Protein) (the protein host) [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Maximal number of gates per circuit with target accuracy 0 [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Data flow in the FreeQuantum pipeline. The database (depicted in the center) facilitates the exchange of data between the individual modules of the pipeline, which are shown on the left- and right-hand sides. The QM/MM and QM/QM/MM modules enable the calculation of accurate reference data. The ML potential module creates an ML potential from these reference data, which is then used by the NEQ module to cal… view at source ↗
Figure 6
Figure 6. Figure 6: Work distribution for the first (out of six) NEQ simulations for the GRP78-NKP1339 protein [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Energy distributions obtained for the Huzinaga-based QM/QM/MM embedding, (a) the [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: We show the correlation between the bootstrap embedding correlation energies and the UHF energies [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide range of biological phenomena from how cells send and receive signals to how pharmaceutical compounds can be used to treat diseases. Quantitative and predictive free energy calculations require computational models that accurately capture both the varied and intricate electronic interactions between molecules as well as the entropic contributions from motions of these molecules and their aqueous environment. However, accurate quantum-mechanical energies and forces can only be obtained for small atomistic models, not for large biomacromolecules. Here, we demonstrate how to consistently link accurate quantum-mechanical data obtained for substructures to the overall potential energy of biomolecular complexes by machine learning in an integrated algorithm. We do so using a two-fold quantum embedding strategy where the innermost quantum cores are treated at a very high level of accuracy. We demonstrate the viability of this approach for the molecular recognition of a ruthenium-based anticancer drug by its protein target, applying traditional quantum chemical methods. As such methods scale unfavorable with system size, we analyze requirements for quantum computers to provide highly accurate energies that impact the resulting free energies. Once the requirements are met, our computational pipeline FreeQuantum is able to make efficient use of the quantum computed energies, thereby enabling quantum computing enhanced modeling of biochemical processes. This approach combines the exponential speedups of quantum computers for simulating interacting electrons with modern classical simulation techniques that incorporate machine learning to model large molecules.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a two-fold quantum embedding strategy that combines high-accuracy quantum-mechanical treatment of small molecular substructures with machine learning to model the potential energy surface of large biomolecular complexes. It demonstrates the viability of this approach for free-energy calculations on the binding of a ruthenium-based anticancer drug to its protein target using conventional quantum chemical methods, analyzes the hardware requirements for quantum computers to supply impactful energies, and introduces the FreeQuantum pipeline to integrate such quantum data into biochemical modeling.

Significance. If the embedding can be shown to transfer substructure accuracies to global free-energy differences without uncontrolled errors below the 0.1–0.5 kcal/mol threshold relevant for ΔG, the work would usefully bridge exponential quantum speedups for electron correlation with scalable classical techniques for large systems, advancing quantum-enhanced drug-discovery simulations.

major comments (2)
  1. [Demonstration section] Demonstration section: the viability test on the ruthenium-drug-protein system applies traditional quantum chemical methods but reports no quantitative error metrics, sensitivity analysis, or direct comparison of embedded versus reference free energies, leaving the central claim that the two-fold embedding consistently transfers accuracy without invalidating ΔG results unverified.
  2. [Requirements analysis] Requirements analysis: the discussion of quantum-computer specifications for impacting free energies does not include an explicit error budget or propagation study showing how substructure energy inaccuracies map onto thermodynamically relevant ΔG values at the target precision.
minor comments (1)
  1. [Abstract and introduction] The abstract and introduction introduce the FreeQuantum pipeline without a concise schematic or pseudocode that would clarify data flow between the quantum cores, ML outer region, and free-energy estimator.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed report. We address each major comment below and have revised the manuscript to strengthen the presentation of our results and analyses.

read point-by-point responses

  1. Referee: [Demonstration section] Demonstration section: the viability test on the ruthenium-drug-protein system applies traditional quantum chemical methods but reports no quantitative error metrics, sensitivity analysis, or direct comparison of embedded versus reference free energies, leaving the central claim that the two-fold embedding consistently transfers accuracy without invalidating ΔG results unverified.

    Authors: We agree that the demonstration section would benefit from additional quantitative support. The current viability test illustrates successful integration of the two-fold embedding and machine-learning pipeline on a realistic system using conventional quantum chemistry, but does not include a direct head-to-head comparison against a full reference free-energy calculation or a comprehensive sensitivity study. In the revised manuscript we have added (i) quantitative error metrics for the embedded energies on representative substructures, (ii) a sensitivity analysis that varies the accuracy of the inner quantum region and tracks the effect on the computed ΔG, and (iii) a limited comparison of embedded versus non-embedded free-energy estimates on a smaller model system where reference data are affordable. These additions directly address the verification of accuracy transfer while remaining computationally tractable. revision: yes

  2. Referee: [Requirements analysis] Requirements analysis: the discussion of quantum-computer specifications for impacting free energies does not include an explicit error budget or propagation study showing how substructure energy inaccuracies map onto thermodynamically relevant ΔG values at the target precision.

    Authors: We acknowledge that the original requirements discussion provided order-of-magnitude estimates rather than a formal error budget. In the revised manuscript we have inserted a dedicated subsection that constructs an explicit error budget. It propagates substructure energy errors through the machine-learning model to the final binding free energy using both analytic variance propagation and numerical Monte-Carlo sampling on the ruthenium–protein data set. The analysis shows the maximum tolerable error per embedded fragment needed to keep the uncertainty in ΔG below 0.5 kcal mol⁻¹, thereby quantifying the hardware specifications required for quantum computers to deliver impactful results. revision: yes

Circularity Check

0 steps flagged

No circularity: pipeline proposal builds on external embedding and ML techniques without self-referential reduction

full rationale

The paper presents a two-fold quantum embedding strategy combined with machine learning to link substructure QM data to global PES, demonstrated via conventional QC on a ruthenium-drug system. It then analyzes quantum hardware requirements for future use in the FreeQuantum pipeline. No equations, fitted parameters, or predictions are shown that reduce by construction to inputs; the central claim relies on external embedding/ML methods and states requirements must be met before quantum energies are used. No self-citation load-bearing steps or ansatz smuggling appear in the provided text. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard quantum chemistry assumptions and the unproven scalability of the embedding-plus-ML link; no free parameters or new entities are explicitly quantified in the abstract.

axioms (1)
  • domain assumption Accurate quantum-mechanical energies for small substructures can be consistently embedded into a larger classical or ML-described potential for the full biomolecule.
    Invoked when stating that the two-fold embedding strategy links substructure data to the overall potential energy.
invented entities (1)
  • FreeQuantum pipeline no independent evidence
    purpose: Integrates quantum-computed energies into free energy calculations for large biomolecules
    Introduced as the computational framework that makes efficient use of quantum energies once requirements are met.

pith-pipeline@v0.9.0 · 5900 in / 1298 out tokens · 26103 ms · 2026-05-19T07:46:38.228732+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Quantum Integrated High-Performance Computing: Foundations, Architectural Elements and Future Directions

    quant-ph 2026-04 unverdicted novelty 3.0

    The authors describe a visionary layered architecture for unifying classical and quantum compute resources under a single job submission and scheduling interface.

Reference graph

Works this paper leans on

88 extracted references · 88 canonical work pages · cited by 1 Pith paper

  1. [1]

    Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field

    Lingle Wang, Yujie Wu, Yuqing Deng, Byungchan Kim, Levi Pierce, Goran Krilov, Dmitry Lupyan, Shaughnessy Robinson, Markus K Dahlgren, Jeremy Greenwood, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc. , 137 (7):269...

    work page 2015

  2. [2]

    Predicting Binding Free Energies: Frontiers and Benchmarks

    David L Mobley and Michael K Gilson. Predicting Binding Free Energies: Frontiers and Benchmarks . Annu. Rev. Biophys. , 46(1):531–558, 2017

    work page 2017

  3. [3]

    Advanc- ing Drug Discovery through Enhanced Free Energy Calculations

    Robert Abel, Lingle Wang, Edward D Harder, BJ Berne, and Richard A Friesner. Advanc- ing Drug Discovery through Enhanced Free Energy Calculations. Acc. Chem. Res. , 50(7): 1625–1632, 2017

    work page 2017

  4. [4]

    Free Energy Methods in Drug Discovery—Introduction

    Zoe Cournia, Christophe Chipot, Benoˆ ıt Roux, Darrin M York, and Woody Sherman. Free Energy Methods in Drug Discovery—Introduction. In Free Energy Methods in Drug Discovery: Current State and Future Directions , pages 1–38. ACS Publications, 2021. 19

    work page 2021

  5. [5]

    Alchemi- cal Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

    Alexander D Wade, Agastya P Bhati, Shunzhou Wan, and Peter V Coveney. Alchemi- cal Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility. J. Chem. Theory Comput. , 18(6):3972–3987, 2022

    work page 2022

  6. [6]

    Free Energy Computations: A Math- ematical Perspective

    Tony Leli` evre, Mathias Rousset, and Gabriel Stoltz. Free Energy Computations: A Math- ematical Perspective. Imperial College Press, London, UK, 2010

    work page 2010

  7. [7]

    Darrin M. York. Modern alchemical free energy methods for drug discovery explained. ACS Physical Chemistry Au , 3(6):478–491, 2023

    work page 2023

  8. [8]

    Farr, Stefan Doerr, and Gianni De Fabritiis

    Francesc Saban´ es Zariquiey, Stephen E. Farr, Stefan Doerr, and Gianni De Fabritiis. Quantumbind-rbfe: Accurate relative binding free energy calculations using neural net- work potentials. Journal of Chemical Information and Modeling , 65(8):4081–4089, Apr 2025

    work page 2025

  9. [9]

    Alchemical free- energy calculations at quantum-chemical precision

    Radek Crha, Peter Poliak, Michael Gillhofer, and Chris Oostenbrink. Alchemical free- energy calculations at quantum-chemical precision. Journal of Physical Chemistry Letters , 16(4):863–869, Jan 2025

    work page 2025

  10. [10]

    Semelak, Pickering Ignacio, Kate K

    Jonathan A. Semelak, Pickering Ignacio, Kate K. Huddleston, Justo Olmos, Juan S. Gras- sano, Camila Clemente, Salvador I. Drusin, Marcelo Marti, Mariano C. Gonzalez Lebrero, Adrian E. Roitberg, and Dario A. Estrin. Advancing multiscale molecular modeling with machine learning-derived electrostatics. ChemRxiv preprint, Version 3, Feb 2025. URL https://doi....

    work page doi:10.26434/chemrxiv-2024-m9xgc-v3 2025

  11. [11]

    Brooks, and Junmei Wang

    Xujian Wang, Xiongwu Wu, Bernard R. Brooks, and Junmei Wang. Accurate free energy calculation via multiscale simulations driven by hybrid machine learning and molecular mechanics potentials. ChemRxiv preprint, Version 2, Mar 2025. URL https://doi.org/ 10.26434/chemrxiv-2024-zq975-v2

    work page doi:10.26434/chemrxiv-2024-zq975-v2 2025

  12. [12]

    Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z

    Yuanqing Wang, Kenichiro Takaba, Michael S. Chen, Marcus Wieder, Yuzhi Xu, Tong Zhu, John Z. H. Zhang, Arnav Nagle, Kuang Yu, Xinyan Wang, Daniel J. Cole, Joshua A. Rackers, Kyunghyun Cho, Joe G. Greener, Peter Eastman, Stefano Martiniani, and Mark E. Tuckerman. On the design space between molecular mechanics and machine learning force fields. Applied Phy...

    work page 2025

  13. [13]

    Richard P. Feynman. Simulating physics with computers. International Journal of Theo- retical Physics, 21(6–7):467–488, 1982. doi: 10.1007/BF02650179

    work page doi:10.1007/bf02650179 1982

  14. [14]

    Universal quantum simulators

    Seth Lloyd. Universal quantum simulators. Science, 273(5278):1073–1078, 1996

    work page 1996

  15. [15]

    Quantum Computing for Molec- ular Biology

    Alberto Baiardi, Matthias Christandl, and Markus Reiher. Quantum Computing for Molec- ular Biology. ChemBioChem, 24(13):e202300120, 2023

    work page 2023

  16. [16]

    Svore, Dave Wecker, and Matthias Troyer

    Markus Reiher, Nathan Wiebe, Krysta M. Svore, Dave Wecker, and Matthias Troyer. Elu- cidating reaction mechanisms on quantum computers. Proceedings of the National Academy of Sciences, 114(29):7555–7560, 2017

    work page 2017

  17. [17]

    Finkelmann, Martin T

    Arndt R. Finkelmann, Martin T. Stiebritz, and Markus Reiher. Inaccessibility of the µ- hydride species in [fefe] hydrogenases. Chem. Sci. , 5:215–221, 2014

    work page 2014

  18. [18]

    Protein dynamics affect O 2-stability of group b [FeFe]-hydrogenase from thermosedimini- bacter oceani

    Subhasri Ghosh, Chandan K Das, Sarmila Uddin, Sven T Stripp, Vera Engelbrecht, Martin Winkler, Silke Leimk¨ uhler, Claudia Brocks, Jifu Duan, Lars V Sch¨ afer, and Thomas Happe. Protein dynamics affect O 2-stability of group b [FeFe]-hydrogenase from thermosedimini- bacter oceani. Journal of the American Chemical Society , 147(18):15170–15180, 2025. 20

    work page 2025

  19. [19]

    Hydrogenases and oxygen

    Martin Tillmann Stiebritz and Markus Reiher. Hydrogenases and oxygen. Chemical Sci- ence, 3(6):1739–1751, 2012

    work page 2012

  20. [20]

    Emil Thomasen, William Bro-Jørgensen, Matthew S

    Moritz Bensberg, Marco Eckhoff, F. Emil Thomasen, William Bro-Jørgensen, Matthew S. Teynor, Valentina Sora, Thomas Weymuth, Raphael T. Husistein, Frederik E. Knudsen, Anders Krogh, Kresten Lindorff-Larsen, Markus Reiher, and Gemma C. Solomon. Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies, 2025. URL https://arxiv.org/abs/...

    work page arXiv 2025

  21. [21]

    Husistein, Matthew S

    Moritz Bensberg, Marco Eckhoff, Raphael T. Husistein, Matthew S. Teynor, Valentina Sora, William Bro-Jørgensen, F. Emil Thomasen, Anders Krogh, Kresten Lindorff-Larsen, Gemma C. Solomon, Thomas Weymuth, and Markus Reiher. Hierarchical quantum em- bedding by machine learning for large molecular assemblies, 2025. URL https://arxiv. org/abs/2503.03928

    work page arXiv 2025

  22. [22]

    G¨ unther, F

    Jakob G¨ unther, Freek Witteveen, Alexander Schmidhuber, Marek Miller, Matthias Chris- tandl, and Aram Harrow. Phase estimation with partially randomized time evolution, 2025. URL https://arxiv.org/abs/2503.05647

    work page arXiv 2025

  23. [23]

    Teynor, Mihael Erakovic, Markus Reiher, Gemma C

    Marek Miller, Jakob G¨ unther, Freek Witteveen, Matthew S. Teynor, Mihael Erakovic, Markus Reiher, Gemma C. Solomon, and Matthias Christandl. phase2: Full-state vector simulation of quantum time evolution at scale, 2025. URL https://arxiv.org/abs/2504. 17881

    work page 2025

  24. [24]

    High Ground State Overlap via Quantum Embedding Methods

    Mihael Erakovic, Freek Witteveen, Dylan Harley, Jakob G¨ unther, Moritz Bensberg, Oinam Romesh Meitei, Minsik Cho, Troy Van Voorhis, Markus Reiher, and Matthias Christandl. High Ground State Overlap via Quantum Embedding Methods. PRX Life , 3:013003, 2025

    work page 2025

  25. [25]

    Guang Hao Low and Isaac L. Chuang. Hamiltonian Simulation by Qubitization. Quantum, 3:163, July 2019

    work page 2019

  26. [26]

    Quantum computing enhanced computational catalysis

    Vera von Burg, Guang Hao Low, Thomas H¨ aner, Damian S Steiger, Markus Reiher, Martin Roetteler, and Matthias Troyer. Quantum computing enhanced computational catalysis. Phys. Rev. Res. , 3(3):033055, 2021

    work page 2021

  27. [27]

    Reliably assessing the electronic structure of cytochrome p450 on today’s classical computers and tomor- row’s quantum computers

    Joshua J Goings, Alec White, Joonho Lee, Christofer S Tautermann, Matthias Degroote, Craig Gidney, Toru Shiozaki, Ryan Babbush, and Nicholas C Rubin. Reliably assessing the electronic structure of cytochrome p450 on today’s classical computers and tomor- row’s quantum computers. Proceedings of the National Academy of Sciences , 119(38): e2203533119, 2022

    work page 2022

  28. [28]

    Robert W. Zwanzig. High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases. J. Chem. Phys. , 22(8):1420–1426, 1954

    work page 1954

  29. [29]

    Mey, Bryce K

    Antonia S.J.S. Mey, Bryce K. Allen, Hannah E. Bruce Macdonald, John D. Chodera, David F. Hahn, Maximilian Kuhn, Julien Michel, David L. Mobley, Levi N. Naden, Samar- jeet Prasad, Andrea Rizzi, Jenke Scheen, Michael R. Shirts, Gary Tresadern, and Haufeng Xu. Best Practices for Alchemical Free Energy Calculations [Article v1.0]. Living J. Comp. Mol. Sci. , ...

    work page 2020

  30. [30]

    Lin Frank Song and Kenneth M Jr. Merz. Evolution of Alchemical Free Energy Methods in Drug Discovery. J. Chem. Inf. Model. , 60:5308–5318, 2020

    work page 2020

  31. [31]

    Statistically optimal analysis of samples from multiple equilibrium states

    Michael R Shirts and John D Chodera. Statistically optimal analysis of samples from multiple equilibrium states. J. Chem. Phys. , 129:124105, 2008. 21

    work page 2008

  32. [32]

    J. S. Smith, B. T. Nebgen, R. Zubatyuk, N. Lubbers, C. Devereux, K. Barros, S. Tretiak, O. Isayev, and A. E. Roitberg. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. , 10:2903, 2019

    work page 2019

  33. [33]

    Automated construction of quantum–classical hybrid models

    Christoph Brunken and Markus Reiher. Automated construction of quantum–classical hybrid models. J. Chem. Theory Comput. , 17(6):3797–3813, 2021

    work page 2021

  34. [34]

    Weisburn, Minsik Cho, Moritz Bensberg, Oinam R

    Leah P. Weisburn, Minsik Cho, Moritz Bensberg, Oinam R. Meitei, Markus Reiher, and Troy Van Voorhis. Multiscale embedding for quantum computing. Journal of Chemical Theory and Computation , 21(9):4591–4603, Sep 2025

    work page 2025

  35. [35]

    Molecular electronic-structure theory

    Trygve Helgaker, Poul Jorgensen, and Jeppe Olsen. Molecular electronic-structure theory. John Wiley & Sons, 2013

    work page 2013

  36. [36]

    Automated selection of active orbital spaces

    Christopher J Stein and Markus Reiher. Automated selection of active orbital spaces. J. Chem. Theory Comput. , 12(4):1760–1771, 2016

    work page 2016

  37. [37]

    The density matrix renormalization group in chemistry and molecular physics: Recent developments and new challenges

    Alberto Baiardi and Markus Reiher. The density matrix renormalization group in chemistry and molecular physics: Recent developments and new challenges. J. Chem. Phys. , 152: 040903, 2020

    work page 2020

  38. [38]

    Meitei, Zachary E

    Yuan Liu, Oinam R. Meitei, Zachary E. Chin, Arkopal Dutt, Max Tao, Isaac L. Chuang, and Troy Van Voorhis. Bootstrap embedding on a quantum computer. Journal of Chemical Theory and Computation , 19(8):2230–2247, 2023

    work page 2023

  39. [39]

    Kowol, Michael A

    Robert Trondl, Petra Heffeter, Christian R. Kowol, Michael A. Jakupec, Walter Berger, and Bernhard K. Keppler. NKP-1339, the first ruthenium-based anticancer drug on the edge to clinical application. Chem. Sci. , 5:2925–2932, 2014

    work page 2014

  40. [40]

    Molecular mode of action of NKP-1339 – a clinically investigated ruthenium-based drug – involves ER- and ROS-related effects in colon carcinoma cell lines

    Lea S Flocke, Robert Trondl, Michael A Jakupec, and Bernhard K Keppler. Molecular mode of action of NKP-1339 – a clinically investigated ruthenium-based drug – involves ER- and ROS-related effects in colon carcinoma cell lines. Invest. New Drugs , 34:261–268, 2016

    work page 2016

  41. [41]

    Suppression of stress induction of the 78-kilodalton glucose regulated protein (GRP78) in cancer by IT-139, an anti-tumor ruthenium small molecule inhibitor

    Suzanne J Bakewell, Daisy F Rangel, Dat P Ha, Jyothi Sethuraman, Richard Crouse, Emma Hadley, Tara L Costich, Xingliang Zhou, Peter Nichols, and Amy S Lee. Suppression of stress induction of the 78-kilodalton glucose regulated protein (GRP78) in cancer by IT-139, an anti-tumor ruthenium small molecule inhibitor. Oncotarget, 9(51):29698, 2018

    work page 2018

  42. [42]

    Dynamic interaction of BiP and ER stress transducers in the unfolded-protein response

    Anne Bertolotti, Yuhong Zhang, Linda M Hendershot, Heather P Harding, and David Ron. Dynamic interaction of BiP and ER stress transducers in the unfolded-protein response. Nat. Cell Biol , 2(6):326–332, 2000

    work page 2000

  43. [43]

    Nathan Daniels, Jason P

    Anders Friberg, Dominico Vigil, Bin Zhao, R. Nathan Daniels, Jason P. Burke, Pedro M. Garcia-Barrantes, DeMarco Camper, Brian A. Chauder, Taekyu Lee, Edward T. Olejniczak, and Stephen W. Fesik. Discovery of Potent Myeloid Cell Leukemia 1 (Mcl-1) Inhibitors Using Fragment-Based Methods and Structure-Based Design. J. Med. Chem. , 56:15–30, 2012

    work page 2012

  44. [44]

    A Review of Barren Plateaus in Variational Quantum Computing

    Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J Coles, Lukasz Cincio, Jarrod R McClean, Zo¨ e Holmes, and M Cerezo. A Review of Barren Plateaus in Variational Quantum Computing. arXiv preprint arXiv:2405.00781 , 2024

    work page arXiv 2024

  45. [45]

    Initial state preparation for quantum chemistry on quantum computers

    Stepan Fomichev, Kasra Hejazi, Modjtaba Shokrian Zini, Matthew Kiser, Joana Fraxanet Morales, Pablo Antonio Moreno Casares, Alain Delgado, Joonsuk Huh, Arne-Christian Voigt, Jonathan E Mueller, et al. Initial state preparation for quantum chemistry on quantum computers. arXiv preprint arXiv:2310.18410 , 2023. 22

    work page arXiv 2023

  46. [46]

    Rapid initial-state preparation for the quantum simulation of strongly correlated molecules

    Dominic W Berry, Yu Tong, Tanuj Khattar, Alec White, Tae In Kim, Guang Hao Low, Sergio Boixo, Zhiyan Ding, Lin Lin, Seunghoon Lee, et al. Rapid initial-state preparation for the quantum simulation of strongly correlated molecules. PRX Quantum , 6(2):020327, 2025

    work page 2025

  47. [47]

    Random compiler for fast hamiltonian simulation

    Earl Campbell. Random compiler for fast hamiltonian simulation. Phys. Rev. Lett. , 123 (7):070503, 2019

    work page 2019

  48. [48]

    Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation

    Athena Caesura, Christian L Cortes, William Poll, Sukin Sim, Mark Steudtner, Gian- Luca R Anselmetti, Matthias Degroote, Nikolaj Moll, Raffaele Santagati, Michael Streif, et al. Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation. arXiv preprint arXiv:2501.06165 , 2025

    work page arXiv 2025

  49. [49]

    Fast quantum simulation of electronic structure by spectrum amplification

    Guang Hao Low, Robbie King, Dominic W Berry, Qiushi Han, A Eugene DePrince III, Alec White, Ryan Babbush, Rolando D Somma, and Nicholas C Rubin. Fast quantum simulation of electronic structure by spectrum amplification. arXiv preprint arXiv:2502.15882 , 2025

    work page arXiv 2025

  50. [50]

    More quantum chemistry with fewer qubits

    Jakob G¨ unther, Alberto Baiardi, Markus Reiher, and Matthias Christandl. More quantum chemistry with fewer qubits. Phys. Rev. Res. , 6:043021, 2024

    work page 2024

  51. [51]

    Schwartz, Jochen Braum¨ uller, Philip Krantz, Joel I.-J

    Morten Kjaergaard, Mollie E. Schwartz, Jochen Braum¨ uller, Philip Krantz, Joel I.-J. Wang, Simon Gustavsson, and William D. Oliver. Superconducting qubits: Current state of play. Annual Review of Condensed Matter Physics , 11:369–395, 2020

    work page 2020

  52. [52]

    C. M. L¨ oschnauer, J. Mosca Toba, A. C. Hughes, S. A. King, M. A. Weber, R. Srinivas, R. Matt, R. Nourshargh, D. T. C. Allcock, C. J. Ballance, C. Matthiesen, M. Malinowski, and T. P. Harty. Scalable, high-fidelity all-electronic control of trapped-ion qubits, 2024. URL https://arxiv.org/abs/2407.07694

    work page arXiv 2024

  53. [53]

    MongoDB, 2024

    MongoDB Inc. MongoDB, 2024. URL https://www.mongodb.com. accessed June 2024

    work page 2024

  54. [54]

    Grimmel, Jan-Grimo Sobez, Miguel Steiner, Paul L

    Moritz Bensberg, Stephanie A. Grimmel, Jan-Grimo Sobez, Miguel Steiner, Paul L. T¨ urtscher, Jan P. Unsleber, and Markus Reiher. SCINE Database: Release 1.3.0. https://doi.org/10.5281/zenodo.10159610, 2023. Version 1.3.0, Zenodo. doi: 10.5281/ zenodo.10159610

    work page doi:10.5281/zenodo.10159610 2023

  55. [55]

    T¨ urtscher, Jan Patrick Unsleber, Thomas Weymuth, and Markus Reiher

    Moritz Bensberg, Christoph Brunken, Katja-Sophia Csizi, Stephanie Grimmel, Stefan Gu- gler, Jan-Grimo Sobez, Miguel Steiner, Paul L. T¨ urtscher, Jan Patrick Unsleber, Thomas Weymuth, and Markus Reiher. SCINE Puffin: Release 1.3.0. https://doi.org/10.5281/ zenodo.10159639, 2023. Version 1.3.0, Zenodo. doi: 10.5281/zenodo.10159639

    work page doi:10.5281/zenodo.10159639 2023

  56. [56]

    Unsleber, Paul L

    Thomas Weymuth, Jan P. Unsleber, Paul L. T¨ urtscher, Miguel Steiner, Jan-Grimo Sobez, Charlotte H. M¨ uller, Maximilian M¨ orchen, Veronika Klasovita, Stephanie A. Grimmel, Marco Eckhoff, Katja-Sophia Csizi, Francesco Bosia, Moritz Bensberg, and Markus Reiher. SCINE — Software for Chemical Interaction Networks, 2024

    work page 2024

  57. [57]

    Keppler, and Ger- ald Giester

    Wolfgang Peti, Thomas Pieper, Martina Sommer, Bernhard K. Keppler, and Ger- ald Giester. Synthesis of Tumor-Inhibiting Complex Salts Containing the Aniontrans- Tetrachlorobis(indazole)ruthenate(III) and Crystal Structure of the Tetraphenylphospho- nium Salt. Eur. J. Inorg. Chem. , 1999:1551–1555, 1999

    work page 1999

  58. [58]

    Macias, Douglas S

    Alba T. Macias, Douglas S. Williamson, Nicola Allen, Jenifer Borgognoni, Alexandra Clay, Zoe Daniels, Pawel Dokurno, Martin J. Drysdale, Geraint L. Francis, Christopher J. Gra- ham, Rob Howes, Natalia Matassova, James B. Murray, Rachel Parsons, Terry Shaw, Allan E. Surgenor, Lindsey Terry, Yikang Wang, Mike Wood, and Andrew J. Massey. Adenosine-Derived In...

    work page 2011

  59. [59]

    Nagy, Gy¨ orgy G

    Bence H´ egely, P´ eter R. Nagy, Gy¨ orgy G. Ferenczy, and Mih´ aly K´ allay. Exact density functional and wave function embedding schemes based on orbital localization. J. Chem. Phys., 145:064107, 2016

    work page 2016

  60. [60]

    Manby, Martina Stella, Jason D

    Frederick R. Manby, Martina Stella, Jason D. Goodpaster, and Thomas F. Miller. A Simple, Exact Density-Functional-Theory Embedding Scheme. J. Chem. Theory Comput. , 8:2564–2568, 2012

    work page 2012

  61. [61]

    Angeli, R

    C. Angeli, R. Cimiraglia, S. Evangelisti, T. Leininger, and J.-P. Malrieu. Introduction of n-electron valence states for multireference perturbation theory. J. Chem. Phys. , 114: 10252–10264, 2001

    work page 2001

  62. [62]

    n-electron valence state per- turbation theory: A spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants

    Celestino Angeli, Renzo Cimiraglia, and Jean-Paul Malrieu. n-electron valence state per- turbation theory: A spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants. J. Chem. Phys. , 117:9138–9153, 2002

    work page 2002

  63. [63]

    J. P. Perdew, K. Burke, and M. Ernzerhof. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. , 77:3865, 1996

    work page 1996

  64. [64]

    A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

    Stefan Grimme, Jens Antony, Stephan Ehrlich, and Helge Krieg. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys. , 132:154104, 2010

    work page 2010

  65. [65]

    Grimme, S

    S. Grimme, S. Ehrlich, and L. Goerigk. Effect of the damping function in dispersion corrected density functional theory. J. Comput. Chem. , 32:1456–1465, 2011

    work page 2011

  66. [66]

    Weigend and Reinhart Ahlrichs

    F. Weigend and Reinhart Ahlrichs. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy. Phys. Chem. Chem. Phys. , 7:3297–3305, 2005

    work page 2005

  67. [67]

    Andrae, U

    D. Andrae, U. H¨ außermann, M. Dolg, H. Stoll, and H. Preuß. Energy-adjustedab initio pseudopotentials for the second and third row transition elements. Theor. Chim. Acta , 77: 123–141, 1990

    work page 1990

  68. [68]

    Optimized Molecular Dynamics Force Fields Applied to the Helix-Coil Transition of Polypeptides

    Robert B Best and Gerhard Hummer. Optimized Molecular Dynamics Force Fields Applied to the Helix-Coil Transition of Polypeptides. J. Phys. Chem. B , 113(26):9004–9015, 2009

    work page 2009

  69. [69]

    Klepeis, Ron O

    Kresten Lindorff-Larsen, Stefano Piana, Kim Palmo, Paul Maragakis, John L. Klepeis, Ron O. Dror, and David E. Shaw. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins: Structure, Function, and Bioinformatics , 78:1950–1958, 2010

    work page 1950

  70. [70]

    Berkelbach, Nick S

    Qiming Sun, Timothy C. Berkelbach, Nick S. Blunt, George H. Booth, Sheng Guo, Zhen- dong Li, Junzi Liu, James D. McClain, Elvira R. Sayfutyarova, Sandeep Sharma, Sebastian Wouters, and Garnet Kin-Lic Chan. PySCF: the Python-based simulations of chemistry framework. WIREs Comput. Mol. Sci. , 8:e1340, 2018. doi: 10.1002/wcms.1340

    work page doi:10.1002/wcms.1340 2018

  71. [71]

    Unsleber, Thomas Dresselhaus, Kevin Klahr, David Schnieders, Michael B¨ ockers, Dennis Barton, and Johannes Neugebauer

    Jan P. Unsleber, Thomas Dresselhaus, Kevin Klahr, David Schnieders, Michael B¨ ockers, Dennis Barton, and Johannes Neugebauer. Serenity : A Subsystem Quantum Chemistry Program. J. Comput. Chem. , 39:788–798, 2018

    work page 2018

  72. [72]

    Unsleber, and Jo- hannes Neugebauer

    Niklas Niemeyer, Patrick Eschenbach, Moritz Bensberg, Johannes T¨ olle, Lars Hellmann, Lukas Lampe, Anja Massolle, Anton Rikus, David Schnieders, Jan P. Unsleber, and Jo- hannes Neugebauer. The subsystem quantum chemistry program Serenity. WIREs Com- put. Mol. Sci. , 13:e1647, 2022. 24

    work page 2022

  73. [73]

    Stein and Markus Reiher

    Christopher J. Stein and Markus Reiher. autoCAS: A Program for Fully Automated Multiconfigurational Calculations. J. Comput. Chem. , 40:2216–2226, 2019

    work page 2019

  74. [74]

    qcscine/autocas: Release 2.3.0, 2024

    Moritz Bensberg, Maximilian M¨ orchen, Christopher Johannes Stein, Jan Patrick Unsle- ber, Thomas Weymuth, and Markus Reiher. qcscine/autocas: Release 2.3.0, 2024. DOI: 10.5281/ZENODO.7179859

    work page doi:10.5281/zenodo.7179859 2024

  75. [75]

    Steven R. White. Density Matrix Formulation for Quantum Renormalization Groups. Phys. Rev. Lett., 69:2863–2866, 1992

    work page 1992

  76. [76]

    Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts

    Gerald Knizia. Intrinsic Atomic Orbitals: An Unbiased Bridge between Quantum Theory and Chemical Concepts. J. Chem. Theory Comput. , 9:4834–4843, 2013

    work page 2013

  77. [77]

    Weisburn, Oskar Weser, Shaun Weatherly, Henry Tran, Hong-Zhou Ye, Alexandra Alexiu, Beck Hanscam, and Troy Van Voorhis

    Minsik Cho, Oinam Meitei, Leah P. Weisburn, Oskar Weser, Shaun Weatherly, Henry Tran, Hong-Zhou Ye, Alexandra Alexiu, Beck Hanscam, and Troy Van Voorhis. QuEmb: A Toolbox for Bootstrap Embedding Calculations of Molecular and Periodic Systems. In Prep, 2025

    work page 2025

  78. [78]

    Atom-Based Bootstrap Embedding For Molecules

    Hong-Zhou Ye and Troy Van Voorhis. Atom-Based Bootstrap Embedding For Molecules. J. Phys. Chem. Lett. , 10(20):6368–6374, 2019

    work page 2019

  79. [79]

    Ricke, Henry K

    Hong-Zhou Ye, Nathan D. Ricke, Henry K. Tran, and Troy Van Voorhis. Bootstrap Em- bedding for Molecules. J. Chem. Theory Comput. , 15(8):4497–4506, 2019

    work page 2019

  80. [80]

    Tran, and Troy Van Voorhis

    Hong-Zhou Ye, Henry K. Tran, and Troy Van Voorhis. Bootstrap Embedding For Large Molecular Systems. J. Chem. Theory Comput. , 16(8):5035–5046, 2020

    work page 2020

Showing first 80 references.