Experimental implementation of high-fidelity unconventional geometric quantum gates using NMR interferometer

Following a key idea of unconventional geometric quantum computation developed earlier [Phys. Rev. Lett. 91, 197902 (2003)], here we propose a more general scheme in such an intriguing way: $\gamma_{d}=\alpha_{g}+\eta \gamma _{g}$, where $\gamma_{d}$ and $\gamma_{g}$ are respectively the dynamic and geometric phases accumulated in the quantum gate operation, with $\eta$ as a constant and $\alpha_{g}$ being dependent only on the geometric feature of the operation. More arrestingly, we demonstrate the first experiment to implement a universal set of such kind of generalized unconventional geometric quantum gates with high fidelity in an NMR system.Comment: 4 pages, 3 figure

Motion state recognition of debris ejected in vehicular collision after contact with the ground

The motion state of debris ejected from the vehicle involved in vehicular collision is important for finding out the vehicle collision speed. This research developed an analytical model to recognise the debris motion state. With the model, analyses were conducted, which reveal that if α, which is the contact angle between the debris and the ground at the moment when the debris collides the ground, is within the range from 0° to its boundary value, then the debris slides; if α is within the range from its boundary value to 90°, then the debris bounces. With debris' initial angular velocity ω = 0, the boundary value is 11.8° for sphere debris and 7.8° for rectangular debris; with ω ≠ 0, the boundary value for rectangular debris is arcsin(g/Rω2) where g represents the acceleration due to gravity and R is the distance from the debris centre to the point of its contact with the ground. Experiment tests were conducted for debris motion states with ω = 0, which confirmed the analytical results

Effects of microbus front structure on pedestrian head injury

In order to study the effects of the microbus front structure on pedestrian head injury happened in pedestrian-microbus collisions, the mathematic models of the impact angle and microbus front configuration are developed, which illustrate the relationship between the impact angle, pedestrian size, and oblique angles of the engine hood and windscreen. The mathematic models are then verified by simulating experiments using LY-Dyna. The impact angle α, which is measured between the contact surface and the pedestrian head's impact direction at the contact point, is an important parameter indicating the relationship of pedestrian head injury with the microbus front structure. The analysis and simulation results reveal that (1) in the case of collision with the windscreen, the pedestrian head injury increases while α increases; (2) in the case of collision with the engine hood, the pedestrian head incurs the most serious injury when α = 90o, the pedestrian head injury increases while α increases when α 90o. Six microbus models are taken as examples to verify the results obtained

Noise-Resilient Group Testing: Limitations and Constructions

We study combinatorial group testing schemes for learning $d$-sparse Boolean vectors using highly unreliable disjunctive measurements. We consider an adversarial noise model that only limits the number of false observations, and show that any noise-resilient scheme in this model can only approximately reconstruct the sparse vector. On the positive side, we take this barrier to our advantage and show that approximate reconstruction (within a satisfactory degree of approximation) allows us to break the information theoretic lower bound of $\tilde{\Omega}(d^2 \log n)$ that is known for exact reconstruction of $d$-sparse vectors of length $n$ via non-adaptive measurements, by a multiplicative factor $\tilde{\Omega}(d)$. Specifically, we give simple randomized constructions of non-adaptive measurement schemes, with $m=O(d \log n)$ measurements, that allow efficient reconstruction of $d$-sparse vectors up to $O(d)$ false positives even in the presence of $\delta m$ false positives and $O(m/d)$ false negatives within the measurement outcomes, for any constant $\delta < 1$. We show that, information theoretically, none of these parameters can be substantially improved without dramatically affecting the others. Furthermore, we obtain several explicit constructions, in particular one matching the randomized trade-off but using $m = O(d^{1+o(1)} \log n)$ measurements. We also obtain explicit constructions that allow fast reconstruction in time \poly(m), which would be sublinear in $n$ for sufficiently sparse vectors. The main tool used in our construction is the list-decoding view of randomness condensers and extractors.Comment: Full version. A preliminary summary of this work appears (under the same title) in proceedings of the 17th International Symposium on Fundamentals of Computation Theory (FCT 2009