Richard Padbury | Searcharxiv

arXiv2026

Circuit Depth Reduction for Executable Hamiltonian Dynamics of Covalent Inhibitor Reactivity on Quantum Hardware

Quantum chemistry applications in the noisy intermediate-scale quantum era require end-to-end approaches that balance algorithmic fidelity with practical executability on existing hardware. We present an end-to-end Hamiltonian dynamics case study for predicting the reactivity of pharmaceutically relevant covalent inhibitors containing sulfonyl fluoride warheads, using a quantum-centric data-driven research and development framework that combines Hamiltonian time evolution with classical machine learning. To make such simulations executable on current quantum processors, we introduce a systematic circuit reduction strategy based on Hamiltonian term truncation with observable error bounds, Clifford Decomposition and Transformation, and hardware-aware transpilation. Across representative molecular fragments, this approach achieves circuit depth reductions of up to 28.5x under all-to-all connectivity assumptions and up to 15.5x on IBM Heron-class architectures. For an eight-qubit Hamiltonian dynamics simulation, a transpiled instruction set architecture (ISA) circuit depth of 1330 is rendered executable through middleware-enabled circuit decomposition, enabling the execution of sub-circuits with depths up to 371 and containing up to 216 two-qubit gates on real hardware. We evaluate the impact of circuit reduction on downstream reactivity prediction accuracy and show that chemically meaningful predictions can be retained despite aggressive circuit simplifications, clarifying the trade-offs that govern practical quantum chemistry workflows on near-term quantum systems.

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arXiv2024

Hybrid Quantum Graph Neural Network for Molecular Property Prediction

To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated ability to predict various properties of materials with the level of accuracy similar to explicit calculation of quantum mechanical theories, but with significantly reduced run time and computational resources. Within ML, graph neural networks have emerged as an important algorithm within the field of machine learning, since they are capable of predicting accurately a wide range of important physical, chemical and electronic properties due to their higher learning ability based on the graph representation of material and molecular descriptors through the aggregation of information embedded within the graph. In parallel with the development of state of the art classical machine learning applications, the fusion of quantum computing and machine learning have created a new paradigm where classical machine learning model can be augmented with quantum layers which are able to encode high dimensional data more efficiently. Leveraging the structure of existing algorithms, we developed a unique and novel gradient free hybrid quantum classical convoluted graph neural network (HyQCGNN) to predict formation energies of perovskite materials. The performance of our hybrid statistical model is competitive with the results obtained purely from a classical convoluted graph neural network, and other classical machine learning algorithms, such as XGBoost. Consequently, our study suggests a new pathway to explore how quantum feature encoding and parametric quantum circuits can yield drastic improvements of complex ML algorithm like graph neural network.

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