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We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96) and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions., 10 pages, 6 figures

Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved usefull to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github., Comment: 15 pages, 11 figures

openaire: EC/H2020/766900/EU//TEQ Funding Information: Original content from this work may be used under the terms of the . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. EU H2020 framework through Collaborative Projects TEQ 766900 COST Action CA15220 International Mobility Programme DfE-SFI Investigator Programme 15/IA/2864 Royal Society Wolfson Research Fellowship scheme RSWF\R3\183013 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/T028106/1 Academy of Finland https://doi.org/10.13039/501100002341 QuantERA grant SiUCs 731473 QuantERA Leverhulme Trust Research Project Grant UltraQute RGP-2018-266 Foundational Questions Institute Fund(“Exploring the fundamental limits set by thermodynamics in the quantum regime”) FQXi-IAF19-06 yes � 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft Creative Commons Attribution 4.0 licence Publisher Copyright: © 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer in a multi-level system. We constrain our strategy to the case of fixed coupling rates but time-varying detunings, a situation that would simplify considerably the implementation of population transfer in relevant experimental platforms, such as semiconducting and superconducting ones. Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols that, remarkably, differ from (and can be superior to) standard Raman, stimulated Raman adiabatic passage or other adiabatic schemes. The new protocols that we identify are robust against both energy losses and dephasing.

The use of volatile production patterns produced by bacterial contaminants in urine samples were examined using electronic nose technology. In two experiments 25 and 45 samples from patients were analysed for specific bacterial contaminants using agar culture techniques and the major UTI bacterial species identified. These samples were also analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. In the first experiment analysis of the data using a neural network (NN) enabled identification of all but one of the samples correctly when compared to the culture information. Four groups could be distinguished, i.e. normal urine, Escherichia coli infected, Proteus spp. and Staphylococcus spp. In the second experiment it was again possible to use NN systems to examine the volatile production patterns and identify 18 of 19 unknown UTI cases. Only one normal patient sample was mis-identified as an E. coli infected sample. Discriminant function analysis also differentiated between normal urine samples, that infected with E. coli and with Staphylococcus spp. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology for the first time. These findings will have implications for the development of rapid systems for use in clinical practice.

The degree of solid solubility between yttria and calcia was measured, in the yttria-rich region, over the temperature range 1200 to 2000°C. X-ray diffraction results indicate that as much as 12.4 mol% calcia can be dissolved in yttria at 2000°C. No solid solution between the two oxides was observed below 1400°C. In addition exsolution kinetics was measured. Kinetic data were compared to a number of solid state models with the three-dimensional nucleation model providing the best correlation. The Hulbert model was further used to characterize the growth mechanism. It suggested that growth is phase-boundary controlled between 1200 and 1500°C and that the nucleation rate either drops to zero or that growth becomes two-dimensional above 1600°C. Although undoped yttria is both a p- and n-type semiconductor, yttria-calcia solutions are ionic conductors below about 10−10 atm oxygen (ti>0.99) with no trace of n-type conductivity being observed. The activation energy for ionic conduction in yttria-5 mol% calcia is 125 kJ/mol.