EniQmA - Enabling Hybrid Quantum Applications

Characterization of a Catalytic Ligand Bridging Metal Ions in Phosphodiesterases 4 and 5 by Molecular Dynamics Simulations and Hybrid Quantum Mechanical/Molecular Mechanical Calculations

Hybrid quantum mechanics/molecular mechanics (QM/MM) methods were employed to study electronic effects of 5,5'-substituents on the enantioselectivity of (salen)Mn catalysts. These methods were shown to effectively capture electronic effects of substituents if the substituents were treated with a simple electrostatic coupling using point charges. Correlations between calculated properties of (salen)Mn=O indicative of its reactivity and the modified Hammett constant σ + para were applied to study electronic framework effects on enantioselectivity for (salen)Mn heterogenized in a metal—organic framework (MOF). The results suggest that these effects are too small to account for all of the decrease in enantioselectivity observed experimentally and that steric constraints of the framework likely play a role. The reactivity correlations also enabled fast ligand screening for the prediction of highly enantioselective (salen)Mn catalysts that could be used in MOFs. A modified version of the DREIDING force field was also developed to allow QM/MM calculations of (salen)Mn species.

The pulmonary surfactant protein A (SP-A) is a constitutively expressed immune-protective collagenous lectin (collectin) in the lung. It binds to the cell membrane of immune cells and opsonizes infectious agents such as bacteria, fungi, and viruses through glycoprotein binding. SARS-CoV-2 enters airway epithelial cells by ligating the Angiotensin Converting Enzyme 2 (ACE2) receptor on the cell surface using its Spike glycoprotein (S protein). We hypothesized that SP-A binds to the SARS-CoV-2 S protein and this binding interferes with ACE2 ligation. To study this hypothesis, we used a hybrid quantum and classical in silico modeling technique that utilized protein graph pruning. This graph pruning technique determines the best binding sites between amino acid chains by utilizing the Quantum Approximate Optimization Algorithm (QAOA)-based MaxCut (QAOA-MaxCut) program on a Near Intermediate Scale Quantum (NISQ) device. In this, the angles between every neighboring three atoms were Fourier-transformed into microwave frequencies and sent to a quantum chip that identified the chemically irrelevant atoms to eliminate based on their chemical topology. We confirmed that the remaining residues contained all the potential binding sites in the molecules by the Universal Protein Resource (UniProt) database. QAOA-MaxCut was compared with GROMACS with T-REMD using AMBER, OPLS, and CHARMM force fields to determine the differences in preparing a protein structure docking, as well as with Goemans-Williamson, the best classical algorithm for MaxCut. The relative binding affinity of potential interactions between the pruned protein chain residues of SP-A and SARS-CoV-2 S proteins was assessed by the ZDOCK program. Our data indicate that SP-A could ligate the S protein with a similar affinity to the ACE2-Spike binding. Interestingly, however, the results suggest that the most tightly-bound SP-A binding site is localized to the S2 chain, in the fusion region of the SARS-CoV-2 S protein, that is responsible for cell entry Based on these findings we speculate that SP-A may not directly compete with ACE2 for the binding site on the S protein, but interferes with viral entry to the cell by hindering necessary conformational changes or the fusion process.

MurD (UDP-N-acetylmuramoyl-L-alanine:D-glutamate ligase), a three-domain bacterial protein, catalyses a highly specific incorporation of D-glutamate to the cytoplasmic intermediate UDP-N-acetyl-muramoyl-L-alanine (UMA) utilizing ATP hydrolysis to ADP and P(i). This reaction is part of a biosynthetic path yielding bacterial peptidoglycan. On the basis of structural studies of MurD complexes, a stepwise catalytic mechanism was proposed that commences with a formation of the acyl-phosphate intermediate, followed by a nucleophilic attack of D-glutamate that, through the formation of a tetrahedral reaction intermediate and subsequent phosphate dissociation, affords the final product, UDP-N-acetyl-muramoyl-L-alanine-D-glutamate (UMAG). A hybrid quantum mechanical/molecular mechanical (QM/MM) molecular modeling approach was utilized, combining the B3LYP QM level of theory with empirical force field simulations to evaluate three possible reaction pathways leading to tetrahedral intermediate formation. Geometries of the starting structures based on crystallographic experimental data and tetrahedral intermediates were carefully examined together with a role of crucial amino acids and water molecules. The replica path method was used to generate the reaction pathways between the starting structures and the corresponding tetrahedral reaction intermediates, offering direct comparisons with a sequential kinetic mechanism and the available structural data for this enzyme. The acquired knowledge represents new and valuable information to assist in the ongoing efforts leading toward novel inhibitors of MurD as potential antibacterial drugs.

This article proposes a driving condition recognition (DCR)-based hybrid quantum deep deterministic policy gradient (HQDDPG) method for energy management in multistack fuel cell vehicle hybrid power systems (MFCV HPSs) and its quantum simulation setup on digital signal processors (DSPs). Driving conditions are initially segmented into microtrips and clustered into three types. The DCR method, using a learning vector quantization neural network (LVQNN), is then developed, thus accurately and efficiently identifying driving condition types. Subsequently, quantum reinforcement learning (RL) is proposed to achieve optimal energy management of MFCV HPSs, i.e., power allocation among the multiple fuel cells to minimize the economic metrics based on the DCR results. Compared to classical large-scale neural networks, quantum RL reduces parameters by combining a parameterized quantum circuit (PQC) with a single-layer classical neural network. The PQC encodes and processes state information through quantum mechanics for enhanced computational expressiveness, while the classical neural network transforms the quantum measurement expectations into actionable outputs for energy management. The trained hybrid quantum circuits are implemented on DSPs through quantum simulations. The method is validated through controller hardware-in-the-loop (CHIL) experiments, demonstrating superior performance in optimizing economic metrics compared to conventional methods.

Hybrid quantum mechanics/molecular mechanics calculations using Austin Model 1 system-specific parameters were performed to study the S(N)2 displacement reaction of chloride from 1,2-dichloroethane (DCE) by nucleophilic attack of the carboxylate of acetate in the gas phase and by Asp-124 in the active site of haloalkane dehalogenase from Xanthobacter autotrophicus GJ10. The activation barrier for nucleophilic attack of acetate on DCE depends greatly on the reactants having a geometry resembling that in the enzyme or an optimized gas-phase structure. It was found in the gas-phase calculations that the activation barrier is 9 kcal/mol lower when dihedral constraints are used to restrict the carboxylate nucleophile geometry to that in the enzyme relative to the geometries for the reactants without dihedral constraints. The calculated quantum mechanics/molecular mechanics activation barriers for the enzymatic reaction are 16.2 and 19.4 kcal/mol when the geometry of the reactants is in a near attack conformer from molecular dynamics and in a conformer similar to the crystal structure (DCE is gauche), respectively. This haloalkane dehalogenase lowers the activation barrier for dehalogenation of DCE by 2-4 kcal/mol relative to the single point energies of the enzyme's quantum mechanics atoms in the gas phase. S(N)2 displacements of this sort in water are infinitely slower than in the gas phase. The modest lowering of the activation barrier by the enzyme relative to the reaction in the gas phase is consistent with mutation experiments.

Quantum computing is conceived as a promising and powerful next-generation platform for information processing and it has been shown that it could bring significant accelerations to certain tasks, compared to its classical counterparts. With recent advances in noisy intermediate-scale quantum (NISQ) devices, we can process classical data from real-world problems using hybrid quantum systems. In this work, we investigate the critical problem of designing a gate-based hybrid quantum neuron under NISQ constraints to enable the construction of scalable hybrid quantum deep neural networks (HQDNNs). We explore and characterize diverse quantum circuits for hybrid quantum neurons and discuss related critical components of HQDNNs. We also utilize a new schema to infer multiple predictions from a single hybrid neuron. We further compose a highly customizable platform for simulating HQDNNs via Qiskit and test them on diverse classification problems including the iris and the wheat seed datasets. The results show that even HQDNNs with the simplest neurons could lead to superior performance on these tasks. Finally, we show that the HQDNNs are robust to certain levels of noise, making them preferred on NISQ devices. Our work provides a comprehensive investigation of building scalable near-term gate-based HQDNNs and paves the way for future studies of quantum deep learning via both simulations on classical computers and experiments on accessible NISQ devices.

A quantum walks assisted algorithm for peptide and protein folding prediction

Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This study introduces a novel hybrid classical and quantum computing model to distinguish LGGs from HGGs using data from The Cancer Genome Atlas (TCGA). In the classical part, an ensemble feature selection method was employed to identify the most informative molecular markers and clinical features in the TCGA. In the quantum component, six variational quantum classifier (VQC) models with varying hyperparameters were evaluated. These classifiers utilize selected features to differentiate LGGs from HGGs. Among these, the VQC-1 model, which employs [Formula: see text] and CX gates in the feature map and [Formula: see text] [Formula: see text]and CY gates in the parameterized quantum circuit, achieved the highest classification accuracy of 0.74 using the AQCD optimization method. Additionally, VQC-1 identified IDH1, age at diagnosis, PTEN, EGFR, and ATRX, in descending order of importance, as the most informative features distinguishing LGGs from HGGs. Compared to classical machine learning models, VQC-1 demonstrated performance comparable to that of XGBoost and GBM, while outperformed KNN, SVC, DTC, and RFC in five-fold cross-validation experiments. This study provides a novel perspective on glioma classification by integrating classical and quantum computing, offering valuable insights into hybrid computational approaches.

This chapter explores the diverse applications of molecular docking in computational chemistry, highlighting its central role in modern drug discovery and molecular research. Molecular docking predicts protein ligand interactions, providing insights into molecular recognition, binding affinity, and reaction mechanisms. By simulating how molecules fit and interact, docking aids in identifying potential drug candidates through virtual screening, significantly accelerating early-stage drug development. Beyond drug discovery, docking guides enzyme engineering by predicting modifications that enhance catalytic activity, enabling rational design of biocatalysts. It also supports studies of molecular recognition, helping researchers understand the structural and energetic factors underlying binding specificity and selectivity. By estimating binding energies, docking informs the prioritization of compounds for experimental testing, reducing time and costs. Recent advances have expanded docking’s capabilities. Hybrid quantum mechanics/molecular mechanics (QM/MM) approaches improve the accuracy of binding predictions by accounting for electronic effects, while integration with molecular dynamics simulations captures protein flexibility and dynamic interactions. These enhancements allow more realistic modeling of biological systems and complex chemical reactions. Overall, molecular docking serves as a versatile tool across pharmaceutical, biochemical, and chemical sciences. Its ability to efficiently model interactions in silico accelerates research, supports rational design, and guides experimental efforts, making it indispensable in contemporary molecular research.

Recent experimental duplexes of DNA stabilized by Ag cations, pairing homostrands of guanine-guanine, cytosine-cytosine, adenine-thymine, and thymine-thymine, display much higher stability than the Watson-Crick paired DNA duplexes; these broaden the range of applications for DNA nanotechnology. Here we focus on silver-stabilized guanine duplexes in water. Using hybrid quantum mechanics/molecular mechanics simulations, we propose an atomic structure for the Ag+-mediated guanine duplex with two nucleobases per strand, G2-Ag2+-G2. We then compare experimental and time-dependent density functional theory-simulated electronic circular dichroism (ECD) spectra to validate our results. Both experimental and simulated ECD share two negative peaks around 220 and 280 nm, with no positive signal in the measured wavelength range. We found that the left- or right-handed disposition of bases in the structure has a decisive effect on the signs of the ECD. We conclude that G2-Ag2+-G2 is left-hand-oriented, and extrapolation of this orientation to longer strands gives rise to a left-hand-oriented parallel helix stabilized by interplanar H bonds.

pH-dependent absorption spectra of rhodopsin mutant E113Q: On the role of counterions and protein

Solvation phenomena have been the focus of experimental and theoretical research for many decades. While the determination of solvation properties of mono-atomic, ionic species is already a challenging task, the associated difficulties are amplified in the case of molecular compounds. Theoretical approaches have the ability to yield manifold information of the investigated systems, providing that a reliable description of the interatomic energies and forces is employed. Hybrid quantum mechanical/molecular mechanical simulations constitute a particularly successful approach for the study of solvation phenomena, and have been employed in investigations of a variety of molecular compounds in aqueous solution. While an accurate description of these systems depends on the application of reliable, theoretical approaches, the analysis of the resulting dataset is a challenge of its own. It is outlined that standard means of analysis via pair correlation functions may yield an unspecific picture of hydration phenomena, and in some cases wrong conclusions on the hydration may be drawn. More advanced means of structural analysis are thus required as they yield manifold information on solvation phenomena since pair correlations are not sufficiently responsive in such cases. The theoretical approaches required to obtain a reliable description of the investigated systems as well as increasingly complex means of structural analysis ranging from two- and three-body correlations via angular-radial distributions and two-dimensional particle mapping to the so-called SLICE projections are discussed. The application of these analysis schemes is demonstrated for a variety of systems including mono- and polyatomic ionic solutes as well as molecular organic compounds, highlighting various aspects of the hydration of these species.

Short-term photovoltaic power forecasting based on hybrid quantum gated recurrent unit

Aggregation-induced emission (AIE) spectra accompanied by excited state intramolecular proton transfer (ESIPT) for two triphenylamine salicylaldehyde derivatives (namely, TS and TS-OMe) are investigated by performing molecular spectral and dynamics simulations associated with the hybrid quantum mechanics/molecular mechanics (QM/MM) at the quantum level of the time-dependent density functional theory. The simulated emission spectral peaks and Stokes' shifts are in good agreement with the experimental results for both TS and TS-OMe. Furthermore, the AIE spectral mechanisms are well explained to be associated with the ESIPT processes for both TS and TS-OMe monomers in the aggregated crystal state, while the AIE spectra mechanism for the TS-OMe (TS) dimer is accompanied by intermolecular charge-transfer excitation process. Besides, the TS dimers also contributed to the AIE mechanisms in the crystal with the intermolecular charge-transfer from one monomer to another. In addition, the TS dimers are contributed to the AIE mechanisms in the crystal with the intermolecular charge-transfer from one monomer to another. On the other hand, simulated emission spectra for both the TS and TS-OMe monomers in acetonitrile solution are involved in mixed emission with and without the ESIPT process, as interpreted by nonadiabatic molecular dynamics simulation. It is also briefly addressed that the emission spectra in the solution are weak and enhanced in the crystal. The present study provides a great physical insight into the design of highly efficient AIE compounds.