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arxiv: 2605.27640 · v2 · pith:KB2THPSBnew · submitted 2026-05-26 · 🪐 quant-ph

Additive binding energies in asphalt on a quantum processor via quantum-selected configuration interaction (QSCI)

Karim Elgammal , Marc Mau{\ss}ner This is my paper

Pith reviewed 2026-06-29 16:43 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum computingasphalt binderbinding energyquantum-selected configuration interactionhydrogen bondingoxidative ageingquantum-centric supercomputing
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The pith

A quantum processor computes the binding energy of an asphalt hydrogen-bond model exactly matching the classical active-space reference.

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

The paper presents QuantumPave, a hybrid workflow that combines machine-learning geometry optimization with quantum-selected configuration interaction on a 54-qubit device to calculate additive binding energies in a model for asphalt binder. Using a 24-atom pyridine-phenol complex in a (10e,10o) active space, the method yields -3.52 kcal/mol on hardware, reproducing the CASCI reference exactly without noise extrapolation. This establishes that quantum-centric supercomputing can deliver chemically relevant results for an industrially important process like oxidative ageing in road infrastructure.

Core claim

In the QuantumPave workflow, machine-learning interatomic potentials first optimize the geometry of the 24-atom pyridine-phenol hydrogen-bonded complex; quantum-selected configuration interaction (QSCI, or SQD) then samples configurations on the 54-qubit IQM Emerald processor in a (10e,10o) active space and classical resources complete the diagonalization. On hardware this reproduces the active-space CASCI binding energy of -3.52 kcal/mol exactly, capturing static correlation within the chosen orbitals while underbinding relative to a counterpoise-corrected CCSD(T) benchmark of -8.5 to -9.5 kcal/mol.

What carries the argument

Quantum-selected configuration interaction (QSCI), also called sample-based quantum diagonalization (SQD), in which the quantum processor samples the dominant electronic configurations and classical high-performance computing resources perform the subsequent diagonalization.

If this is right

  • Device noise broadens sampling across the active space, eliminating any need for zero-noise extrapolation.
  • The active-space result captures static correlation but remains lower than full CCSD(T) benchmarks and experimental enthalpy values once thermal and solvent effects are considered.
  • The workflow integrates classical machine-learning potentials for geometry with quantum computation for the electronic structure problem.

Where Pith is reading between the lines

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

  • Scaling the active space or model size could allow the same hardware sampling approach to treat more complex asphalt components.
  • The method opens a route to quantum-assisted calculations on other hydrogen-bonded materials systems where static correlation matters.
  • Adding perturbative or density-functional corrections on top of the active-space QSCI result might close the gap to higher-level benchmarks without increasing quantum resources.

Load-bearing premise

The 24-atom pyridine-phenol complex and chosen (10e,10o) active space are representative enough of the hydrogen bonding that governs oxidative ageing in real asphalt binder.

What would settle it

Running the same (10e,10o) active-space calculation on the quantum processor and obtaining a binding energy that deviates from the classical CASCI reference would falsify the claim of exact reproduction on current hardware.

Figures

Figures reproduced from arXiv: 2605.27640 by Karim Elgammal, Marc Mau{\ss}ner.

Figure 1
Figure 1. Figure 1: Molecular structure of the pyridine-phenol complex. The system consists of 24 atoms (C [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Three-dimensional visualisation of the optimised pyridine-phenol complex structure obtained from ORB v3 calculations (Energy: -148.936 eV, 103 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: QSCI workflow showing the active space selection, configuration sampling, and energy convergence procedures. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Quantum-centric supercomputing (in which a quantum processor samples the dominant electronic configurations and classical high-performance computing resources perform the diagonalisation) is emerging as a practical route to correlated electronic-structure calculations. We present QuantumPave, a hybrid quantum-classical workflow for computing additive binding energies in asphalt binder, a quantity central to the oxidative ageing of road infrastructure. Using a 24-atom pyridine-phenol hydrogen-bonded complex as a representative model, we couple machine-learning interatomic potentials (ORB v3) for geometry optimisation with quantum-selected configuration interaction (QSCI), also referred to as sample based quantum diagonalisation (SQD), in a (10e, 10o) active space run on the 54-qubit IQM Emerald processor. On hardware, SQD reproduces the active-space CASCI reference exactly, giving a binding energy of -3.52 kcal/mol (-0.153 eV); the device noise broadens the sampling to span the active space, so no zero-noise extrapolation is required. This active-space value captures the static correlation within the chosen orbitals and underbinds the full hydrogen bond: a counterpoise-corrected CCSD(T) benchmark gives -8.5 to -9.5 kcal/mol, while the calorimetric enthalpy of about -6.25 kcal/mol is consistent with this once zero-point, thermal, and solvent contributions are included. We show that chemically meaningful binding energies for an industrially relevant materials problem are attainable on current quantum hardware within a quantum-centric supercomputing workflow.

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 presents QuantumPave, a hybrid quantum-classical workflow that couples machine-learning interatomic potentials (ORB v3) for geometry optimization with quantum-selected configuration interaction (QSCI/SQD) on a (10e,10o) active space executed on the 54-qubit IQM Emerald processor. Using a 24-atom pyridine-phenol hydrogen-bonded complex as a model for hydrogen bonding in asphalt binder, the hardware SQD calculation exactly reproduces the CASCI reference, producing a binding energy of -3.52 kcal/mol (-0.153 eV). This value is compared to counterpoise-corrected CCSD(T) benchmarks (-8.5 to -9.5 kcal/mol) and experimental calorimetric enthalpy (~-6.25 kcal/mol), with the claim that chemically meaningful binding energies for an industrially relevant materials problem are attainable on current quantum hardware within a quantum-centric supercomputing workflow.

Significance. If the central assumptions hold, the work provides a concrete demonstration that current quantum processors can deliver exact active-space results for a materials-relevant binding-energy problem without zero-noise extrapolation, advancing quantum-centric supercomputing workflows that combine ML potentials with quantum sampling. The exact hardware-CASCI match is a clear technical strength.

major comments (2)
  1. [Abstract] Abstract: The 24-atom pyridine-phenol complex and (10e,10o) active space are presented as a 'representative model' for the hydrogen bonding that governs oxidative ageing in asphalt, yet no supporting evidence (binding-energy convergence with larger fragments, structural metrics, or direct comparison to experimental asphalt data) is supplied. This assumption is load-bearing for the central claim, especially since the reported active-space binding energy underbinds the CCSD(T) benchmark by a factor of ~2.5.
  2. [Abstract] Abstract: No test is provided showing that enlarging the active space or model size to recover the missing dynamic correlation (needed to approach the CCSD(T) or experimental values) would remain feasible on the available hardware, leaving open whether the demonstrated workflow scales to chemically accurate asphalt binding energies.
minor comments (1)
  1. The abstract would benefit from an explicit statement of the active-space limitations and the precise definition of 'additive binding energies' used in the workflow.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We respond point-by-point below, agreeing where the manuscript requires clarification or additional discussion.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The 24-atom pyridine-phenol complex and (10e,10o) active space are presented as a 'representative model' for the hydrogen bonding that governs oxidative ageing in asphalt, yet no supporting evidence (binding-energy convergence with larger fragments, structural metrics, or direct comparison to experimental asphalt data) is supplied. This assumption is load-bearing for the central claim, especially since the reported active-space binding energy underbinds the CCSD(T) benchmark by a factor of ~2.5.

    Authors: The pyridine-phenol complex was selected because pyridine and phenol fragments appear in asphalt oxidation chemistry and form a clean hydrogen-bonded dimer amenable to active-space treatment. We acknowledge that the manuscript supplies no convergence data with larger fragments, no structural metrics against asphalt models, and no direct experimental asphalt comparison, so the representativeness claim rests on chemical analogy alone. The underbinding relative to CCSD(T) is already stated in the text as a direct consequence of restricting to static correlation in (10e,10o). We will revise the abstract and introduction to describe the system explicitly as an illustrative model chosen for computational tractability rather than a validated proxy, thereby reducing the load-bearing weight of the assumption. revision: yes

  2. Referee: [Abstract] Abstract: No test is provided showing that enlarging the active space or model size to recover the missing dynamic correlation (needed to approach the CCSD(T) or experimental values) would remain feasible on the available hardware, leaving open whether the demonstrated workflow scales to chemically accurate asphalt binding energies.

    Authors: We agree that the manuscript contains no explicit test of larger active spaces or molecular fragments. The (10e,10o) space was the largest for which exact SQD sampling and diagonalization could be performed on the 54-qubit IQM Emerald device with the chosen qubit mapping. The quantum-centric workflow is constructed so that larger problems map onto additional qubits or improved sampling; however, demonstrating this scaling on current hardware lies outside the scope of the present demonstration. We will add a concise paragraph in the discussion section outlining the qubit and sampling requirements for recovering dynamic correlation and the expected path to chemical accuracy with next-generation processors. revision: yes

Circularity Check

0 steps flagged

No circularity: direct hardware computation of active-space energy compared to independent classical benchmarks

full rationale

The paper computes the binding energy by running QSCI/SQD on the IQM Emerald processor for the fixed (10e,10o) active space of the 24-atom model, obtaining a value that exactly reproduces the classical CASCI reference within the active space. This result is then compared to separate CCSD(T) calculations and experimental calorimetry; neither the active-space energy nor the binding energy is obtained by fitting parameters to the target quantity or by any self-referential definition. The workflow (ML geometry optimization + quantum sampling + classical diagonalization) contains no load-bearing self-citation chain or ansatz that reduces the reported number to an input by construction. The choice of model system and active space is an assumption whose representativeness can be debated, but that choice does not create a circular derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on the abstract alone, the calculation rests on standard quantum-chemistry assumptions (Born-Oppenheimer, active-space truncation) with no new free parameters, axioms, or invented entities introduced by the authors.

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