arXiv subjects

Karim Elgammal

Publications and source records attributed to Karim Elgammal.

6 recordsLinked to original sources

arXiv2026

Scaling a CUDA-Q GQE + QSCI pipeline to 40 qubits for EUV photoresist chemistry

We scale a CUDA-Q-native pipeline coupling a generative quantum eigensolver (GQE) to quantum-selected configuration interaction (QSCI) across active spaces of 14 to 44 qubits, applied to the extreme-ultraviolet (EUV) photoresist chemistry of monoalkyltin oxo-hydroxides. A GPT-2 policy emits UCCSD operator sequences; sampled bitstrings become determinants, diagonalised classically, and a cross-circuit generalised-eigenvalue refinement makes every reported GQE+QSCI energy a variational upper bound. Every rung from 14 to 40 qubits carries an exact CASCI or FCI reference, up to 166 million determinants for SnO at 32 qubits. The pipeline is chemically accurate, below 1.6 mHa, through 30 qubits on methyltin trihydroxide and through 32 on SnO, on the best seed at the top rungs. Circuit depth rather than training length is the scaling lever; the refined subspace grows near-linearly with the operator count while staying a vanishing fraction of the determinant space, 0.017% at the 32-qubit SnO rung. It also runs on the 54-qubit IQM Emerald processor, at the shallow depths its routed two-qubit gates allow, reaching +0.330 mHa for SnO at 14 qubits from a CCSD-amplitude-ordered pool prefix and +3.92 mHa for the industrial n-butyltin ligand at 22 qubits from depth-truncated trained circuits under per-circuit readout self-calibration, 81% of the active-space correlation; classical configuration recovery on those counts tightens the 22-qubit result to +0.18 to 0.21 mHa. For the methyl resist, ionisation collapses the classical UCCSD(T) Sn-C bond dissociation energy from 72.6 to 21.2 kcal/mol, the switch that flips solubility on exposure. Against that, the 40-qubit result is support-limited at 22.8 mHa, the full trained ansatz on hardware awaits better fidelities, and classical subspace expansion reaches the 32 to 40-qubit spaces with no quantum sampler, so that boundary is mapped, not beaten.

quant-ph↗

arXiv2026

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

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.

quant-ph↗

arXiv2024

A Quantum Computing Approach to Simulating Corrosion Inhibition

This work demonstrates a systematic implementation of hybrid quantum-classical computational methods for investigating corrosion inhibition mechanisms on aluminum surfaces. We present an integrated workflow combining density functional theory (DFT) with quantum algorithms through an active space embedding scheme, specifically applied to studying 1,2,4-Triazole and 1,2,4-Triazole-3-thiol inhibitors on Al111 surfaces. Our implementation leverages the ADAPT-VQE algorithm with benchmarking against classical DFT calculations, achieving binding energies of -0.386 eV and -1.279 eV for 1,2,4-Triazole and 1,2,4-Triazole-3-thiol, respectively. The enhanced binding energy of the thiol derivative aligns with experimental observations regarding sulfur-functionalized inhibitors' improved corrosion protection. The methodology employs the orb-d3-v2 machine learning potential for rapid geometry optimizations, followed by accurate DFT calculations using CP2K with PBE functional and Grimme's D3 dispersion corrections. Our benchmarking on smaller systems reveals that StatefulAdaptVQE implementation achieves a 5-6x computational speedup while maintaining accuracy. This work establishes a workflow for quantum-accelerated materials science studying periodic systems, demonstrating the viability of hybrid quantum-classical approaches for studying surface-adsorbate interactions in corrosion inhibition applications. In which, can be transferable to other applications such as carbon capture and battery materials studies.

cond-mat.mtrl-sci↗

arXiv2019

Performance-Enhanced Non-Enzymatic Glucose Sensor Based on Graphene-Heterostructure

This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of Graphene (G)/Platinum Oxide (PtO)/n-Silicon (Si) heterostructure. The sensor was tested with different glucose concentrations and interfering solutions to investigate its sensitivity and selectivity. Different structures of the device were studied by adjusting the platinum oxide film thickness to investigate its catalytic activity. It was found that the film thickness plays a significant role in the efficiency of glucose oxidation and hence in overall device sensitivity. 0.8-2 uA output current was obtained in the case of 4-10 mM with a sensitivity of 0.2 uA/mM.cm2. Besides, results have shown that 0.8 uA and 15 uA were obtained by testing 4 mM glucose on two different PtO thicknesses, 30 nm, and 50 nm, respectively. The sensitivity of the device was enhanced by 150% (i.e., up to 30 uA/mM.cm2) by increasing the PtO layer thickness. This was attributed to both the increase of the number of active sites for glucose oxidation as well as the increase in the graphene layer thickness, which leads to enhanced charge carriers concentration and mobility. Moreover, theoretical investigations were conducted using the Density Function Theory (DFT) to understand the detection method and the origins of selectivity better. The working principle of the sensors puts it in a competitive position with other non-enzymatic glucose sensors. DFT calculations provided a qualitative explanation of the charge distribution across the graphene sheet within a system of a platinum substrate with D-glucose molecules above. The proposed G/PtO/n-Si heterostructure has proven to satisfy these factors, which opens the door for further developments of more reliable non-enzymatic glucometers for continuous glucose monitoring systems.

physics.app-ph↗

arXiv2015

Resistive Graphene Humidity Sensors with Rapid and Direct Electrical Readout

We demonstrate humidity sensing using a change of electrical resistance of a single- layer chemical vapor deposited (CVD) graphene that is placed on top of a SiO2 layer on a Si wafer. To investigate the selectivity of the sensor towards the most common constituents in air, its signal response was characterized individually for water vapor (H2O), nitrogen (N2), oxygen (O2), and argon (Ar). In order to assess the humidity sensing effect for a range from 1% relative humidity (RH) to 96% RH, devices were characterized both in a vacuum chamber and in a humidity chamber at atmospheric pressure. The measured response and recovery times of the graphene humidity sensors are on the order of several hundred milliseconds. Density functional theory simulations are employed to further investigate the sensitivity of the graphene devices towards water vapor. Results from the interaction between the electrostatic dipole moment of the water and the impurity bands in the SiO2 substrate, which in turn leads to electrostatic doping of the graphene layer. The proposed graphene sensor provides rapid response direct electrical read out and is compatible with back end of the line (BEOL) integration on top of CMOS-based integrated circuits.

cond-mat.mes-hall↗

arXiv2015

Experimental and ab initio studies of the novel piperidine-containing acetylene glycols

Synthesis routes of novel piperidine-containing diacetylene are presented. The new molecules are expected to exhibit plant growth stimulation properties. In particular, the yield in a situation of drought is expected to increase. The synthesis makes use of the Favorskii reaction between cycloketones/piperidone and triple-bond containing glycols. The geometries of the obtained molecules were determined using nuclear magnetic resonance (NMR). The electronic structure and geometries of the molecules were studied theoretically using first-principles calculations based on density functional theory. The calculated geometries agree very well with the experimentally measured ones, and also allow us to determine bond lengths, angles and charge distributions inside the molecules. The stability of the OH-radicals located close to the triple bond and the piperidine/cyclohexane rings was proven by both experimental and theoretical analyses. The HOMO/LUMO analysis was done in order to characterize the electron density of the molecule. The calculations show that triple bond does not participate in intermolecular reactions which excludes the instability of novel materials as a reason for low production rate.

physics.chem-ph↗