arXiv subjects

Peter Pogány

Publications and source records attributed to Peter Pogány.

3 recordsLinked to original sources

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.

quant-ph↗

arXiv2026

Practical Scalability of Tensor Network Quantum Emulators for Molecular Hamiltonian Simulation

Quantum computing holds promise for computational chemistry, but near-term quantum hardware remains limited by noise and scale, motivating classical bridging technologies such as quantum emulators. We present an application-specific systems-level benchmark of matrix product state (MPS) tensor-network emulation for real-time Hamiltonian evolution of a density matrix embedding theory (DMET)-embedded sulfonyl-fluoride pharmaceutical fragment, using state-vector simulation as reference. We evaluate runtime, accuracy, resource requirements, and entanglement growth across active spaces from 4 to 24 qubits for a one-body temporal observable used as a quantum fingerprint for reactivity prediction. The results identify a practical boundary for this workflow. At fixed bond dimension, MPS emulation retains favorable scaling, but the bond dimension required to estimate the observable within a 1.6 mHa chemical-accuracy threshold grows rapidly with active-space size. At 20-24 qubits, it approaches the maximum available MPS representation, eliminating the runtime advantage over state-vector simulation. Entanglement entropy analysis shows that increasing bipartite entanglement in the time-evolved state drives this cost growth, consistent with a mismatch between a one-dimensional MPS ansatz and the non-local correlations generated by molecular electronic dynamics. We do not claim a universal crossover across molecules, observables, mappings, or tensor-network geometries. Rather, this study measures where MPS emulation ceases to be an efficient classical surrogate for this chemically motivated Hamiltonian-simulation workflow. The results motivate entanglement-aware algorithm design, orbital-ordering and mapping optimization, alternative tensor-network geometries, and ultimately fault-tolerant quantum hardware for regimes where accurate molecular dynamics generate non-compressible entanglement.

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arXiv2023

Data-driven reactivity prediction of targeted covalent inhibitors using computed quantum features for drug discovery

We present an approach to combine novel molecular features with experimental data within a data-driven pipeline. The method is applied to the challenge of predicting the reactivity of a series of sulfonyl fluoride molecular fragments used for drug discovery of targeted covalent inhibitors. We demonstrate utility in predicting reactivity using features extracted from a workflow which employs quantum embedding of the reactive warhead using density matrix embedding theory, followed by Hamiltonian simulation of the resulting fragment model from an initial reference state. These predictions are found to improve when studying both larger active spaces and longer evolution times. The calculated features form a `quantum fingerprint' which allows molecules to be clustered with regard to warhead properties. We identify that the quantum fingerprint is well suited to scalable calculation on future quantum computing hardware, and explore approaches to capture results on current quantum hardware using error mitigation and suppression techniques. We further discuss how this general framework may be applied to a wider range of challenges where the potential for future quantum utility exists.

quant-ph↗