aiqbench analysis
This research presents a measurement-efficient algorithm that drastically reduces the sampling overhead required to calculate correlated energy differences for complex photodynamic materials on quantum hardware. By combining sparse multiconfiguration references with compute-uncompute techniques, the method achieves high precision with significantly fewer shots than traditional approaches, validated on both emulators and trapped-ion systems. The inclusion of AI-assisted state preparation further demonstrates a practical pathway toward solving challenging quantum chemistry problems with near-term devices.
Measurement-efficient energy estimator for quantum chemistry of photodynamic materials
arXiv:2610.26689v1 Announce Type: new 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 H$_2$DBB--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.
Authors:Connor Lenihan, Kripa Panchagnula, Luca Erhart, Jem Guhit, Carlo A. Gaggioli, Gabriel Greene-Diniz, Gabriel Laude, Megan Cerys Rowe, Fabian Finger, Pranav Kalidindi, Enrico Rinaldi, Mikko Muuronen, Chandrika Mulakala, Thomas M. Soini, Elvira Shishenina, David Muñoz Ramo, David Zsolt Manrique
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 H$_2$DBB--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.
Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph)
arXiv:2610.26689 [quant-ph]
(or arXiv:2610.26689v1 [quant-ph] for this version)
Full text under the publisher's open licence · 208 words