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The variational quantum eigensolver (VQE) is a widely employed method to solve electronic structure problems in the current noisy intermediate-scale quantum (NISQ) devices. However, due to inherent noise in the NISQ devices, VQE results on NISQ devices often deviate significantly from the results obtained on noiseless statevector simulators or traditional classical computers. The iterative nature of VQE further amplifies the errors in each loop. Recent works have explored ways to integrate deep neural networks (DNN) with VQE to mitigate iterative errors, albeit primarily limited to the noiseless statevector simulators. In this work, we trained DNN models across various quantum circuits and examined the potential of two DNN-VQE approaches, DNN1 and DNNF, for predicting the ground state energies of small molecules in the presence of device noise. We carefully examined the accuracy of the DNN1, DNNF, and VQE methods on both noisy simulators and real quantum devices by considering different ansatzes of varying qubit counts and circuit depths. Our results illustrate the advantages and limitations of both VQE and DNN-VQE approaches. Notably, both DNN1 and DNNF methods consistently outperform the standard VQE method in providing more accurate ground state energies in noisy environments. However, despite being more accurate than VQE, the energies predicted using these methods on real quantum hardware remain meaningful only at reasonable circuit depths (depth = 15, gates = 21). At higher depths (depth = 83, gates = 112), they deviate significantly from the exact results. Additionally, we find that DNNF does not offer any notable advantage over VQE in terms of speed. Consequently, our study recommends DNN1 as the preferred method for obtaining quick and accurate ground state energies of molecules on current quantum hardware, particularly for quantum circuits with lower depth and fewer qubits., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Enzymes are extremely complex catalytic structures with immense biological and technological importance. Nevertheless, their widespread environmental implementation faces several challenges, including high production costs, low operational stability, and intricate recovery and reusability. Therefore, the de novo design of minimalistic biomolecular nanomaterials that can efficiently mimic the biocatalytic function (bionanozymes) and overcome the limitations of natural enzymes is a critical goal in biomolecular engineering. Here, we report an exceptionally simple yet highly active and robust single amino acid bionanozyme that can catalyze the rapid oxidation of environmentally toxic phenolic contaminates and serves as an ultrasensitive tool to detect biologically important neurotransmitters similar to the laccase enzyme. While inspired by the laccase catalytic site, the substantially simpler copper-coordinated bionanozyme is ∼5400 times more cost-effective, four orders more efficient, and 36 times more sensitive compared to the natural protein. Furthermore, the designed mimic is stable under extreme conditions (pH, ionic strength, temperature, storage time), markedly reusable for several cycles, and displays broad substrate specificity. These findings hold great promise in developing efficient bionanozymes for analytical chemistry, environmental protection, and biotechnology., (© 2022. The Author(s).)

We apply direct nonadiabatic dynamics simulations to investigate photoinduced charge transfer reactions. Our approach is based on the mixed quantum-classical fewest switches surface hopping (FSSH) method that treats the transferring electron through time-dependent density functional theory and the nuclei classically. The photoinduced excited state is modeled as a transferring single-electron that initially occupies the LUMO of the donor molecule/moiety. This single-particle electronic wave function is then propagated quantum mechanically by solving the time-dependent Schrödinger equation in the basis of the instantaneous molecular orbitals (MOs) of the entire system. The nonadiabatic transitions among electronic states are modeled using the FSSH approach within the classical-path approximation. We apply this approach to simulate the photoinduced charge transfer dynamics in a few well-characterized molecular systems. Our results are in excellent agreement with both the experimental measurements and high-level (yet expensive) theoretical results.