Measurement-efficient energy estimator for quantum chemistry of photodynamic materials

Abstract

Accurate modelling of chemical reaction rates requires precise correlated energy differences. Quantum computing offers a potential route to their determination, but extracting energies from quantum-prepared states can demand prohibitive sampling. We demonstrate measurement-efficient correlated energy estimation using an electronically challenging H₂DBB--Ru model derived from a Ru-functionalized hafnium metal-organic framework relevant to radiodynamic and photodynamic therapies. The mixed estimator combines a quantum-prepared target with sparse multiconfiguration references and recovers ground-state corrections using magnitude-only compute-uncompute measurements. Analytical variance estimates predict 1.6 mHa precision with a few hundred shots, about 100 times fewer than the single-determinant formulation and three orders of magnitude fewer than optimized Hamiltonian-term sampling. With approximate targets, it reaches comparable accuracy using simpler state preparation than direct expectation-value estimation. Emulator and trapped-ion experiments on Quantinuum Helios validate the approach up to (10,10), including a 50-qubit circuit combining AI-assisted state preparation with error detection, providing a measurement-efficient route to correlated energy differences for challenging chemical problems.

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Cite

BibTeX RIS

BibTeX

@misc{lenihan2026measurementefficient,
  title = {{Measurement-efficient energy estimator for quantum chemistry of photodynamic materials}},
  author = {Connor Lenihan and Kripa Panchagnula and Luca Erhart and Jem Guhit and Carlo A. Gaggioli and Gabriel Greene-Diniz and Gabriel Laude and Megan Cerys Rowe and Fabian Finger and Pranav Kalidindi and Enrico Rinaldi and Mikko Muuronen and Chandrika Mulakala and Thomas M. Soini and Elvira Shishenina and David Muñoz Ramo and David Zsolt Manrique},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.26689},
  eprint = {2610.26689},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.26689},
}

RIS

TY  - UNPB
TI  - Measurement-efficient energy estimator for quantum chemistry of photodynamic materials
AU  - Connor Lenihan
AU  - Kripa Panchagnula
AU  - Luca Erhart
AU  - Jem Guhit
AU  - Carlo A. Gaggioli
AU  - Gabriel Greene-Diniz
AU  - Gabriel Laude
AU  - Megan Cerys Rowe
AU  - Fabian Finger
AU  - Pranav Kalidindi
AU  - Enrico Rinaldi
AU  - Mikko Muuronen
AU  - Chandrika Mulakala
AU  - Thomas M. Soini
AU  - Elvira Shishenina
AU  - David Muñoz Ramo
AU  - David Zsolt Manrique
PY  - 2026
DA  - 2026/10/19
PB  - arXiv
DO  - 10.48550/arxiv.2610.26689
UR  - https://arxiv.org/abs/2610.26689
AB  - Accurate modelling of chemical reaction rates requires precise correlated energy differences. Quantum computing offers a potential route to their determination, but extracting energies from quantum-prepared states can demand prohibitive sampling. We demonstrate measurement-efficient correlated energy estimation using an electronically challenging H_2DBB--Ru model derived from a Ru-functionalized hafnium metal-organic framework relevant to radiodynamic and photodynamic therapies. The mixed estimator combines a quantum-prepared target with sparse multiconfiguration references and recovers ground-state corrections using magnitude-only compute-uncompute measurements. Analytical variance estimates predict 1.6 mHa precision with a few hundred shots, about 100 times fewer than the single-determinant formulation and three orders of magnitude fewer than optimized Hamiltonian-term sampling. With approximate targets, it reaches comparable accuracy using simpler state preparation than direct expectation-value estimation. Emulator and trapped-ion experiments on Quantinuum Helios validate the approach up to (10,10), including a 50-qubit circuit combining AI-assisted state preparation with error detection, providing a measurement-efficient route to correlated energy differences for challenging chemical problems.
ER  -

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