Modeling Non-Covalent Interatomic Interactions on a Photonic Quantum Computer

Non-covalent interactions are a key ingredient to determine the structure, stability, and dynamics of materials, molecules, and biological complexes. However, accurately capturing these interactions is a complex quantum many-body problem, with no efficient solution available on classical computers. A widely used model to accurately and efficiently model non-covalent interactions is the Coulomb-coupled quantum Drude oscillator (cQDO) many-body Hamiltonian, for which no exact solution is known. We show that the cQDO model lends itself naturally to simulation on a photonic quantum computer, and we calculate the binding energy curve of diatomic systems by leveraging Xanadu's Strawberry Fields photonics library. Our study substantially extends the applicability of quantum computing to atomistic modeling, by showing a proof-of-concept application to non-covalent interactions, beyond the standard electronic-structure problem of small molecules. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond. In addition, our study suggests efficient functional forms for cQDO wavefunctions that can be optimized on classical computers, and capture the bonded-to-noncovalent transition for increasing interatomic distances. Remarkably, we find that two coupled bosonic QDOs exhibit a stable bond. In addition, our study suggests efficient functional forms for cQDO wavefunctions that can be optimized on classical computers, and capture the bonded-to-noncovalent transition for increasing interatomic distances.Comment: 12 pages, 6 figures; published version, added various comments and a figur

Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properties for a specific application. In particular, quantum-mechanical (QM) methods combined with machine learning (ML) techniques have accelerated the estimation of accurate molecular properties, providing a direct mapping from 3D molecular structures to their properties. However, the development of reliable and efficient methodologies to enable \emph{inverse mapping} in chemical space is a long-standing challenge that has not been accomplished yet. Here, we address this challenge by demonstrating the possibility of parametrizing a given chemical space with a finite set of extensive and intensive QM properties. In doing so, we develop a proof-of-concept implementation that combines a Variational Auto-Encoder (VAE) trained on molecular structures with a property encoder designed to learn the latent representation from a set of QM properties. The result of this joint architecture is a common latent space representation for both structures and properties, which enables property-to-structure mapping for small drug-like molecules contained in the QM7-X dataset. We illustrate the capabilities of our approach by conditional generation of \emph{de novo} molecular structures with targeted properties, transition path interpolation for chemical reactions as well as insights into property-structure relationships. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.Comment: 17 pages, 8 figures, 1 tabl

Learning feedback control strategies for quantum metrology

We consider the problem of frequency estimation for a single bosonic field evolving under a squeezing Hamiltonian and continuously monitored via homodyne detection. In particular, we exploit reinforcement learning techniques to devise feedback control strategies achieving increased estimation precision. We show that the feedback control determined by the neural network greatly surpasses in the long time limit the performances of both the "no-control" and the "standard open-loop control" strategies, that we considered as benchmarks. We indeed observe how the devised strategy is able to optimize the nontrivial estimation problem by preparing a large fraction of trajectories corresponding to more sensitive quantum conditional states.Comment: 11 pages, 8 figure

Beyond the Concepts of Elder and Marginal in DCD Liver Transplantation: A Prospective Observational Matched-Cohort Study in the Italian Clinical Setting

Donation after circulatory determination of death (DCD) is a valuable strategy to increase the availability of grafts for liver transplantation (LT). As the average age of populations rises, the donor pool is likely to be affected by a potential increase in DCD donor age in the near future. We conducted a prospective cohort study to evaluate post-transplantation outcomes in recipients of grafts from elderly DCD donors compared with younger DCD donors, and elderly donors after brainstem determination of death (DBD). From August 2020 to May 2022, consecutive recipients of deceased donor liver-only transplants were enrolled in the study. DCD recipients were propensity score matched 1:3 to DBD recipients. One-hundred fifty-seven patients were included, 26 of whom (16.6%) were transplanted with a DCD liver graft. After propensity score matching and stratification, three groups were obtained: 15 recipients of DCD donors & GE;75 years, 11 recipients of DCD donors <75 years, and 28 recipients of DBD donors & GE;75 years. Short-term outcomes, as well as 12 months graft survival rates (93.3%, 100%, and 89.3% respectively), were comparable among the groups. LT involving grafts retrieved from very elderly DCD donors was feasible and safe in an experienced high-volume center, with outcomes comparable to LTs from younger DCD donors and age-matched DBD donors

Simulating lattice gauge theories within quantum technologies

Abstract: Lattice gauge theories, which originated from particle physics in the context of Quantum Chromodynamics (QCD), provide an important intellectual stimulus to further develop quantum information technologies. While one long-term goal is the reliable quantum simulation of currently intractable aspects of QCD itself, lattice gauge theories also play an important role in condensed matter physics and in quantum information science. In this way, lattice gauge theories provide both motivation and a framework for interdisciplinary research towards the development of special purpose digital and analog quantum simulators, and ultimately of scalable universal quantum computers. In this manuscript, recent results and new tools from a quantum science approach to study lattice gauge theories are reviewed. Two new complementary approaches are discussed: first, tensor network methods are presented – a classical simulation approach – applied to the study of lattice gauge theories together with some results on Abelian and non-Abelian lattice gauge theories. Then, recent proposals for the implementation of lattice gauge theory quantum simulators in different quantum hardware are reported, e.g., trapped ions, Rydberg atoms, and superconducting circuits. Finally, the first proof-of-principle trapped ions experimental quantum simulations of the Schwinger model are reviewed. Graphical abstract

An exploratory study to assess patterns of influenza- and pneumonia-related mortality among the Italian elderly

Older adults are at disproportionately high risk of severe influenza-related outcomes and represent the main target of the annual influenza vaccination. The protective effect of seasonal influenza vaccination on the observed mortality indicators is controversial. In this ecological study, spatiotemporal patterns of pneumonia- and influenza-related mortality registered in the Italian elderly over seven (2011–2017) consecutive seasons were explored and the epidemiological association between the observed local pneumonia- and influenza-related mortality and influenza vaccination campaign features were modeled by using both fixed- and random-effects panel regression models. The descriptive spatiotemporal analysis showed a clear North–South gradient, where northern regions tended to report more pneumonia- and influenza-related deaths. After adjustment for potential confounders, it was found that each 1% increase in influenza vaccination coverage rate would be associated (P < .001) with a 1.6–1.9% decrease in pneumonia- and influenza-related mortality. Moreover, each 1% increase in the use of MF59®-adjuvanted trivalent influenza vaccine would be associated (P < .05) with a further 0.4% decrease in pneumonia- and influenza-related mortality. This study supports the increase in annual influenza vaccination in Italy and suggests that a higher level of use of the adjuvanted influenza vaccine in the elderly may be beneficial

Decreased brain network global efficiency after attachment memories retrieval in individuals with unresolved/disorganized attachment-related state of mind

International audienceThe main aim of the study was to examine how brain network metrics change after retrieval of attachment memories in individuals with unresolved/disorganized (U/D) attachment-related state of mind and those with organized/resolved (O/R) state of mind. We focused on three main network metrics associated with integration and segregation: global (E glob ) efficiency for the first function, local (E loc ) efficiency and modularity for the second. We also examined assortativity and centrality metrics. Electroencephalography (EEG) recordings were performed before and after the Adult Attachment Interview (AAI) in a sample of 50 individuals previously assessed for parenting quality. Functional connectivity matrices were constructed by means of the exact Low-Resolution Electromagnetic Tomography (eLORETA) software and then imported into MATLAB to compute brain network metrics. Compared to individuals with O/R attachment-related state of mind, those with U/D show a significant decrease in beta E glob after AAI. No statistically significant difference among groups emerged in E loc and modularity metrics after AAI, neither in assortativity nor in betweenness centrality. These results may help to better understand the neurophysiological patterns underlying the disintegrative effects of retrieving traumatic attachment memories in individuals with disorganized state of mind in relation to attachment

Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean:50.9), and containing up to 54 (mean:28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and \textit{de novo} generation of large (solvated) molecules with pharmaceutical and biological relevance