Hybrid quantum-classical machine learning for generative chemistry and drug design.

Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.

Efficient realization of quantum primitives for Shor's algorithm using PennyLane library.

Efficient realization of quantum algorithms is among main challenges on the way towards practical quantum computing. Various libraries and frameworks for quantum software engineering have been developed. Here we present a software package containing implementations of various quantum gates and well-known quantum algorithms using PennyLane library. Additoinally, we used a simplified technique for decomposition of algorithms into a set of gates which are native for trapped-ion quantum processor and realized this technique using PennyLane library. The decomposition is used to analyze resources required for an execution of Shor's algorithm on the level of native operations of trapped-ion quantum computer. Our original contribution is the derivation of coefficients needed for implementation of the decomposition. Templates within the package include all required elements from the quantum part of Shor's algorithm, specifically, efficient modular exponentiation and quantum Fourier transform that can be realized for an arbitrary number of qubits specified by a user. All the qubit operations are decomposed into elementary gates realized in PennyLane library. Templates from the developed package can be used as qubit-operations when defining a QNode.

Driven-Dissipative Time Crystalline Phases in a Two-Mode Bosonic System with Kerr Nonlinearity.

For the driven-dissipative system of two coupled bosonic modes in a nonlinear cavity resonator, we demonstrate a sequence of phase transitions from a trivial steady state to two distinct dissipative time crystalline phases. These effects are already anticipated at the level of the semiclassical analysis of the Lindblad equation using the theory of bifurcations and are further supported by the full quantum treatment. The system is predicted to exhibit different dynamical phases characterized by an oscillating nonequilibrium steady state with nontrivial periodicity, which is a hallmark of time crystals. We expect that these phases can be directly probed in various cavity QED experiments.

Optimizing the deployment of quantum key distribution switch-based networks.

Quantum key distribution (QKD) networks provide an infrastructure for establishing information-theoretic secure keys between legitimate parties via quantum and authentic classical channels. The deployment of QKD networks in real-world conditions faces several challenges, which are related in particular to the high costs of QKD devices and the condition to provide reasonable secret key rates. In this work, we present a QKD network architecture that provides a significant reduction in the cost of deploying QKD networks by using optical switches and reducing the number of QKD receiver devices, which use single-photon detectors. We describe the corresponding modification of the QKD network protocol. We also provide estimations for a network link of a total of 670 km length consisting of 8 nodes and demonstrate that the switch-based architecture achieves significant resource savings of up to 28%, while the throughput is reduced by 8% only.

Genome assembly using quantum and quantum-inspired annealing.

Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for de novo genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the [Formula: see text]X 174 bacteriophage. Our results pave a way for a significant increase in the efficiency of solving bioinformatics problems with the use of quantum computing technologies and, in particular, quantum annealing might be an effective method. We expect that the new generation of quantum annealing devices would outperform existing techniques for de novo genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.

Towards practical applications in quantum computational biology.

Fascinating progress in understanding our world at the smallest scales moves us to the border of a new technological revolution governed by quantum physics. By taking advantage of quantum phenomena, quantum computing devices allow a speedup in solving diverse tasks. In this Perspective, we discuss the potential impact of quantum computing on computational biology. Bearing in mind the limitations of existing quantum computing devices, we attempt to indicate promising directions for further research in the emerging area of quantum computational biology.

The first experience of the use of hydro-surgical technologies in the treatment of children with pulmatic-pleural complications of destructive pneumonia. / Pervyi opyt primeneniia gidrokhirurgicheskikh tekhnologii v lechenii detei s legochno-plevral'nymi oslozhneniiami destruktivnoi pnevmonii.

Drainage and endoscopic methods of sanitation of the pleural cavity do not always allow to achieve effective debridement of pathological contents. AIM: To development and introduction into clinical practice of hydrosurgical technologies for debridement of the pleural cavity. MATERIAL AND METHODS: From 423 children with acute community-acquired pneumonia 88 (20.80%) children destructive pneumonia were diagnosed. Of the 88 patients with destructive pneumonia, 28 patients did not have pleural complications and were excluded from the study. 60 patients were divided into 2 groups depending on the method of surgical treatment. In the first group (n=30), two additional subgroups were formed: IA group (main n=15) - they carried out drainage and washing the pleural cavity with saline; IB group (control n=15) - only drainage of the pleural cavity. The second group (n=30) were also divided into 2 subgroups; Group IIA (main n=15) children operated according to the method of video-assisted thoracoscopic sanitations of the pleural cavity developed by us using hydrosurgical technologies; Group IIB (control n=15) - children are operated on by the method of traditional video-assisted thoracoscopic sanitations of the pleural cavity. A prospective, non-randomized, single-center study was conducted to evaluate the effectiveness of various treatments. The treatment plan was determined on the basis of a combination of anamnesis, clinical and instrumental studies and laboratory parameters. RESULTS: All studied in the comparison groups were homogeneous by sex, weight and height. The results of applying the Kruskal-Wallis test revealed statistically significant differences between the groups for the periods of relief of the intoxication syndrome (p<0.001) and the periods of relief of the pain syndrome (p=0.012) in favor of the main group. Summarizing all analyzing the parameters in the comparison groups allowed us to prove the advantage of the proposed treatment methods over the treatment methods used in the control groups. CONCLUSION: Hydrosurgical methods of treatment demonstrate obvious clinical and economic efficacy, which leads to the rapid reexpantion of the affected lung.

Novel p-wave superfluids of fermionic polar molecules.

Recently suggested subwavelength lattices offer remarkable prospects for the observation of novel superfluids of fermionic polar molecules. It becomes realistic to obtain a topological p-wave superfluid of microwave-dressed polar molecules in 2D lattices at temperatures of the order of tens of nanokelvins, which is promising for topologically protected quantum information processing. Another foreseen novel phase is an interlayer p-wave superfluid of polar molecules in a bilayer geometry.

Note: Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems.

We propose a novel approach to the recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain-reflectometry-based sensors. Our algorithmic solution has two main features: filtering aimed at the de-nosing of signals and a Gaussian mixture model to cluster them. We test the proposed algorithm using experimentally measured signals. The results show that two classes of events can be distinguished with the best-case recognition probability close to 0.9 at sufficient numbers of training samples.