Dilhan Manawadu

Proton transfer reactions are central to chemical and biological systems, where quantum effects—such as tunneling, delocalization, and zero-point motion—critically influence reaction kinetics. Classical methods that capture these phenomena scale poorly with system size, limiting their applicability. Here, we extend and benchmark a quantum computing framework based on the Nuclear–Electronic Orbital formalism, treating the transferring proton quantum mechanically, to assess the feasibility of computing accurate energy barriers on current quantum devices. Using malonaldehyde as a prototypical system, we construct deep initial circuits via ADAPT-VQE combined with the frozen natural orbital approximation and apply adaptive approximate quantum compiling to balance circuit depth and fidelity. Transpiling these circuits for the ibm_pittsburgh device and simulating with realistic noise models, we compute barrier heights and delocalized proton densities along the reaction pathway. Circuit refinement and compression yield compact representations that preserve essential quantum features of the transfer process. Notably, our shallowest circuits (AQC-low) reproduce key qualitative features, such as proton density localization, and are near the frontier of feasibility for current hardware. In contrast, deeper circuits (AQC-high) retain higher fidelity to reference barrier height, reducing the error to 1.6 mHa (13%) while still yielding a 98% underestimation of the rate constant at 120 K.

Quantum machine learning (QML) has great potential for the analysis of chemical datasets. However, conventional quantum data-encoding schemes, such as fingerprint encoding, are generally unfeasible for the accurate representation of chemical moieties in such datasets. In this contribution, we introduce the quantum molecular structure encoding (QMSE) scheme, which encodes the molecular bond orders and interatomic couplings expressed as a hybrid Coulomb-adjacency matrix, directly as one- and two-qubit rotations within parametrised circuits. We show that this strategy provides an efficient and interpretable method in improving state separability between encoded molecules compared to other fingerprint encoding methods, which is especially crucial for the success in preparing feature maps in QML workflows. To benchmark our method, we train a parametrised ansatz on molecular datasets to perform classification of state phases and regression on boiling points, demonstrating the competitive trainability and generalisation capabilities of QMSE. We further prove a fidelity-preserving chain-contraction theorem that reuses common substructures to cut qubit counts, with an application to long-chain fatty acids. We expect this scalable and interpretable encoding framework to greatly pave the way for practical QML applications of molecular datasets.

Modelling and predicting protein configurations is crucial for advancing drug discovery, enabling the design of treatments for life-threatening diseases. A critical aspect of this challenge is rotamer optimisation - the determination of optimal side-chain conformations given a fixed protein backbone. This problem, involving the internal degrees of freedom of amino acid side-chains, significantly influences the protein's overall structure and function. In this work, we develop a resource-efficient optimisation algorithm to compute the ground state energy of protein structures, with a focus on side-chain configuration. We formulate the rotamer optimisation problem as a Quadratic Unconstrained Binary Optimisation problem and map it to an Ising model, enabling efficient quantum encoding. Building on this formulation, we propose a quantum algorithm based on the Quantum Approximate Optimisation Algorithm to explore the conformational space and identify low-energy configurations. To benchmark our approach, we conduct a classical study using custom-built libraries tailored for structural characterisation and energy optimisation. Our quantum method demonstrates a reduction in computational cost compared to classical simulated annealing techniques, offering a scalable and promising framework for protein structure optimisation in the quantum era.

Proton transfer reactions are fundamental to many chemical and biological systems, where quantum effects such as tunneling, delocalization, and zero-point motion play key kinetic control roles. However, classical methods capable of accurately capturing these phenomena scale prohibitively with system size. Here, we develop and demonstrate quantum computing algorithms based on the Nuclear-Electronic Orbital framework, treating the transferring proton quantum mechanically. We assess the potential of current quantum devices for simulating proton transfer kinetics with high accuracy. We first construct a deep initial ansätze within a truncated orbital space by employing the frozen natural orbital approximation. Then, to balance circuit depth against state fidelity, we implement an adaptive form of approximate quantum compiling. Using resulting circuits at varying compression levels transpiled for the ibm_fez device, we compute barrier heights and delocalised proton densities along the proton transfer pathway using a realistic hardware noise model. We find that, although current quantum hardware introduces significant noise relative to the demanding energy tolerances involved, our approach allows substantial circuit simplification while maintaining energy barrier estimates within 13% of the reference value. Despite present hardware limitations, these results offer a practical means of approximating key circuit segments in near-term devices and early fault-tolerant quantum computing systems.

In this work, we consider the imaginary time evolution of matrix product states. We present a quantum-inspired classical method that, when combined with time evolving block decimation (TEBD), is able to potentially speed up the convergence to a ground state compared to TEBD alone. Our method, referred to as boosted imaginary time evolution, relies on the use of reflections to boost to lower energy states. Interleaving TEBD steps with boosts reduces the total number of TEBD steps and potentially the computational cost required to imaginary time evolve a matrix product state to a ground state. We give the mathematical details of the method followed by an algorithmic implementation and finally some results for a simple test case. Published by the American Physical Society 2024

A series of heteroditopic hydrogen- and halogen bonding [2]rotaxanes display strong and selective recognition of lithium chloride ion-pairs.

We present a dynamical simulation scheme to model the highly correlated excited state dynamics of linear polyenes. We apply it to investigate the internal conversion processes of carotenoids following their photoexcitation. We use the extended Hubbard-Peierls model, HUVP, to describe the pi-electronic system coupled to nuclear degrees of freedom. This is supplemented by a Hamiltonian, H , that explicitly breaks both the particle-hole and two-fold rotation symmetries of idealized carotenoid structures. The electronic degrees of freedom are treated quantum mechanically by solving the time-dependent Schro''dinger equation using the adaptive time-dependent DMRG (tDMRG) method, while nuclear dynamics are treated via the Ehrenfest equations of motion. By defining adiabatic excited states as the eigenstates of the full Hamiltonian, H = HUVP + H, and diabatic excited states as eigenstates of HUVP, we present a computational framework to monitor the internal conversion process from the initial photoexcited 11Bu+ state to the singlet triplet-pair states of carotenoids. We further incorporate Lanczos-DMRG to the tDMRG-Ehrenfest method to calculate transient absorption spectra from the evolving photoexcited state. We describe in detail the accuracy and convergence criteria for DMRG, and show that this method accurately describes the dynamical processes of carotenoid excited states. We also discuss the effect of the symmetry-breaking term, H , on the internal conversion process, and show that its effect on the extent of internal conversion can be described by a Landau-Zener-type transition. This methodological paper is a companion to our more explanatory discussion of carotenoid excited state dynamics in Manawadu, D.; Georges, T. N.; Barford, W. Photoexcited State Dynamics and Singlet Fission in Carotenoids. J. Phys. Chem. A 2023, 127, 1342.

In this study, we present the investigation of the curcumin loading and release properties of four different Metal-Organic Frameworks (MOFs) with varying metal centres and organic ligands. Through our research, we have identified HKUST-1 and MIL-100, highly porous copper and iron-containing MOFs, that exhibit specific interactions with curcumin, leading to high encapsulation efficiencies (55%–75%) even at low concentrations as 6 ppm. The binding modes of curcumin onto MOFs have been investigated using a combined experimental and computational approach. Furthermore, our drug-releasing studies have revealed slow and prolonged release for over two days, which further indicates the specific interactions of curcumin with HKUST-1 and MIL-100. To the best of our knowledge, this is the first comparative study that investigates the drug delivery properties of curcumin using Copper, Ferrous, and Zinc MOFs. Our findings pave the way for the development of stable, highly interactive MOFs as drug carriers for curcumin, which has the potential to overcome its poor aqueous solubility and rapid metabolism, and enhance its pharmacological activities in medicine.

We describe our simulations of the excited state dynamics of the carotenoid neurosporene, following its photoexcitation into the "bright" (nominally 1¹B_u⁺) state. To account for the experimental and theoretical uncertainty in the relative energetic ordering of the nominal 1¹B_u⁺ and 2¹A_g^- states at the Franck-Condon point, we consider two parameter sets. In both cases, there is ultrafast internal conversion from the "bright" state to a "dark" singlet triplet-pair state, i.e., to one member of the "2A_g" family of states. For one parameter set, internal conversion from the 1¹B_u⁺ to 2¹A_g^- states occurs via the dark, intermediate 1¹B_u^- state. In this case, there is a cross over of the 1¹B_u⁺ and 1¹B_u^- diabatic energies within 5 fs and an associated avoided crossing of the S₂ and S₃ adiabatic energies. After the adiabatic evolution of the S₂ state from predominately 1¹B_u⁺ character to predominately 1¹B_u^- character, there is a slower nonadiabatic transition from S₂ to S₁, accompanied by an increase in the population of the 2¹A_g^- state. For the other parameter set, the 2¹A_g^- energy lies higher than the 1¹B_u⁺ energy at the Franck-Condon point. In this case, there is cross over of the 2¹A_g^- and 1¹B_u⁺ energies and an avoided crossing of the S₁ and S₂ energies, as the S₁ state evolves adiabatically from being of 1¹B_u⁺ character to 2¹A_g^- character. We make a direct connection from our predictions to experimental observables by calculating the time-resolved excited state absorption. For the case of direct 1¹B_u⁺ to 2¹A_g^- internal conversion, we show that the dominant transition at ca. 2 eV, being close to but lower in energy than the T₁ to T₁^* transition, can be attributed to the 2¹A_g^- component of S₁. Moreover, we show that it is the charge-transfer exciton component of the 2¹A_g^- state that is responsible for this transition (to a higher-lying exciton state), and not its triplet-pair component. These simulations are performed using the adaptive tDMRG method on the extended Hubbard model of π-conjugated electrons. The Ehrenfest equations of motion are used to simulate the coupled nuclei dynamics. We next discuss the microscopic mechanism of "bright" to "dark" state internal conversion and emphasize that this occurs via the exciton components of both states. Finally, we describe a mechanism relying on torsional relaxation whereby the strongly bound intrachain triplet-pairs of the "dark" state may undergo interchain exothermic dissociation.