Noise suppression and long-range exchange coupling for gallium arsenide spin qubits

This thesis presents the results of the experimental study performed on spin qubits realized in gate-defined gallium arsenide quantum dots, with the focus on noise suppression and long-distance coupling.Comment: PhD thesis, supervised by Charles M. Marcus and Ferdinand Kuemmeth, submitted to the PhD School of the Faculty of Science, University of Copenhagen in June 2017, 223 pages, 92 figure

Methods for variational computation of molecular properties on near term quantum computers

In this thesis we explore the near term applications of quantum computing to Quantum Chemistry problems, with a focus on electronic structure calculations. We begin by discussing the core subroutine of near-term quantum computing methods: the variational quantum eigensolver (VQE). By drawing upon the literature, we discuss the relevance of the method in computing electronic structure properties, compare it to alternative conventional or quantum methods and outline best practices. We then discuss the key limitations of this method, namely: the exploding number of measurements required, showing that parallelisation will be relevant for VQE to compete with conventional methods; the barren plateau problem; and the management of errors through error mitigation - we present a light touch error mitigation technique which is used to improve the results of experiments presented later in the thesis. From this point, we propose three methods for near term applications of quantum computing, with a focus on limiting the requirements on quantum resources. The first two methods concern the computation of ground state energy. We adapt the conventional methods of complete active space self consistent field (CASSCF) and energy-weighted density matrix embedding theory (EwDMET) by integrating a VQE subroutine to compute the electronic wavefunctions from which reduced density matrices are sampled. These method allow recovering additional electron correlation energy for a given number of qubits and are tested on quantum devices. The last method is focused on computing excited electronic states and uses techniques inspired from the generative adversarial machine learning literature. It is a fully variational method, which is shown to work on current quantum devices

Forum Bildverarbeitung 2022

Bildverarbeitung verknüpft das Fachgebiet die Sensorik von Kameras – bildgebender Sensorik – mit der Verarbeitung der Sensordaten – den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge

Device-independent key distribution between trapped-ion quantum network nodes

Hybrid quantum systems, combining the advantages of matter-based carriers of quantum information with those of light, have potential applications across many domains of quantum science and technology. In this thesis, we present a high-fidelity, high-rate interface between trapped ions and polarisation-encoded photonic qubits, based on the spontaneous emission of 422 nm photons from ⁸⁸Sr⁺, entangled in polarisation with the resulting electronic state of the ion. We show that photons can be efficiently collected perpendicular to the ambient magnetic field without loss of polarisation purity by exploiting the symmetry properties of single-mode optical fibres, and analyse the impact of a number of common experimental imperfections, including in the heralded entanglement swapping step used to probabilistically generate entanglement between remote ion qubits. Our experimental platform consists of two ⁸⁸Sr⁺–⁴³Ca⁺ mixed-species quantum network nodes, linked by 2 × 1.75 m of single-mode optical fibre. We measure an ion–photon entanglement fidelity of 97.7(1) %, generated at an attempt rate of 1 MHz and up to 2.3 % overall collection/detection efficiency. Bell states between remote ⁸⁸Sr⁺ ions are generated at a fidelity of 96.0(1) % and rate of 100 s⁻¹. This is the highest fidelity for optically mediated entanglement between distant qubits reported across all matter qubit platforms, and the highest rate among those with fidelities >70 %. To compensate stray electric fields that would cause a periodic modulation of the ion position, we introduce a versatile method which relies on the synchronous detection of parametrically excited motion through time-stamped detection of photons scattered during laser cooling. Crucially, only a single laser beam is required to resolve fields in multiple directions; we achieve a stray field sensitivity of 0.1 V m⁻¹ / √Hz. Finally, we present the first experimental demonstration of device-independent quantum key distribution, by which two distant parties can share an information-theoretically secure private key even in the presence of an arbitrarily powerful eavesdropper without placing any trust in the quantum behaviour of their devices. This is enabled by a record-high detection-loophole-free CHSH inequality violation of 2.677(6) and low quantum bit error rate of 1.44(2) %, stable across millions of Bell pairs, and an improved security analysis and post-processing pipeline. We implement the complete end-to-end protocol in a realistic setting, allowing Alice and Bob to obtain a 95 884-bit key across 8.5 hours that is secure against the most general quantum attacks. Our results establish trapped ions as a state-of-the-art platform for photonic entanglement distribution at algorithmically relevant speeds and error rates. The link performance nevertheless remains far from fundamental limits; further improvements are discussed from the perspective of large-scale modular quantum computation as well as from that of long-distance quantum networking applications

Managing extreme cryptocurrency volatility in algorithmic trading: EGARCH via genetic algorithms and neural networks.

Política de acceso abierto tomada de: https://www.aimspress.com/index/news/solo-detail/openaccesspolicyThe blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell” decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.This research was funded by the Universitat de Barcelona, under the grant UB-AE-AS017634