Flexible resources for quantum metrology

Quantum metrology offers a quadratic advantage over classical approaches to parameter estimation problems by utilizing entanglement and nonclassicality. However, the hurdle of actually implementing the necessary quantum probe states and measurements, which vary drastically for different metrological scenarios, is usually not taken into account. We show that for a wide range of tasks in metrology, 2D cluster states (a particular family of states useful for measurement-based quantum computation) can serve as flexible resources that allow one to efficiently prepare any required state for sensing, and perform appropriate (entangled) measurements using only single qubit operations. Crucially, the overhead in the number of qubits is less than quadratic, thus preserving the quantum scaling advantage. This is ensured by using a compression to a logarithmically sized space that contains all relevant information for sensing. We specifically demonstrate how our method can be used to obtain optimal scaling for phase and frequency estimation in local estimation problems, as well as for the Bayesian equivalents with Gaussian priors of varying widths. Furthermore, we show that in the paradigmatic case of local phase estimation 1D cluster states are sufficient for optimal state preparation and measurement.Comment: 9+18 pages, many figure

Speeding-up the decision making of a learning agent using an ion trap quantum processor

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.Comment: 21 pages, 7 figures, 2 tables. Author names now spelled correctly; sections rearranged; changes in the wording of the manuscrip

The pollination of Tritoniopsis parviflora (Iridaceae) by the oil-collecting bee Rediviva gigas (Hymenoptera: Melittidae): the first record of oil-secretion in African Iridaceae

The Western Cape geophyte Tritoniopsis parviflora (Iridaceae: Crocoideae) has been found to secrete floral oils as well as nectar. The oils are secreted from epithelial elaiophores over much of the proximal parts of the perianth. This is the first report of oil-secretion in the subfamily Crocoideae and the first record of oil-secretion in the Old World representatives of the Iridaceae. The species is pollinated by the large oil-collecting bee Rediviva gigas (Hymenoptera: Melittidae) and is part of a guild of yellow-flowered, often fragrant species that flower in late spring and early summer, usually only after a fire the previous season. Tritoniopsis parviflora will not self-pollinate and the provision of both oil and nectar may be a strategy for ensuring pollination in populations in areas where R. gigas is not presen

A Framework for Evaluating Security in the Presence of Signal Injection Attacks

Sensors are embedded in security-critical applications from medical devices to nuclear power plants, but their outputs can be spoofed through electromagnetic and other types of signals transmitted by attackers at a distance. To address the lack of a unifying framework for evaluating the effects of such transmissions, we introduce a system and threat model for signal injection attacks. We further define the concepts of existential, selective, and universal security, which address attacker goals from mere disruptions of the sensor readings to precise waveform injections. Moreover, we introduce an algorithm which allows circuit designers to concretely calculate the security level of real systems. Finally, we apply our definitions and algorithm in practice using measurements of injections against a smartphone microphone, and analyze the demodulation characteristics of commercial Analog-to-Digital Converters (ADCs). Overall, our work highlights the importance of evaluating the susceptibility of systems against signal injection attacks, and introduces both the terminology and the methodology to do so.Comment: This article is the extended technical report version of the paper presented at ESORICS 2019, 24th European Symposium on Research in Computer Security (ESORICS), Luxembourg, Luxembourg, September 201