Dispersion cancellation and non-classical noise reduction for large photon-number states

Nonlocal dispersion cancellation is generalized to frequency-entangled states with large photon number N. We show that the same entangled states can simultaneously exhibit a factor of 1/sqrt(N) reduction in noise below the classical shot noise limit in precise timing applications, as was previously suggested by Giovannetti, Lloyd and Maccone (Nature v412 (2001) p417). The quantum-mechanical noise reduction can be destroyed by a relatively small amount of uncompensated dispersion and entangled states of this kind have larger timing uncertainties than the corresponding classical states in that case. Similar results were obtained for correlated states, anti-correlated states, and frequency-entangled coherent states, which shows that these effects are a fundamental result of entanglement.Comment: 8 pages, 4 figures, REVTeX 4, submitted to Phys. Rev. A, v2: minor changes in response to referee report, fig3 fixe

An evaluation of the post-ignition unblocking behavior of solid propellant aft-end ignition systems Final report

Determining postignition interactions between igniter and main motor flow by aft-end heated air simulation of solid propellant exhaus

Microwave spectroscopy of a carbon nanotube charge qubit

Carbon nanotube quantum dots allow accurate control of electron charge, spin and valley degrees of freedom in a material which is atomically perfect and can be grown isotopically pure. These properties underlie the unique potential of carbon nanotubes for quantum information processing, but developing nanotube charge, spin, or spin-valley qubits requires efficient readout techniques as well as understanding and extending quantum coherence in these devices. Here, we report on microwave spectroscopy of a carbon nanotube charge qubit in which quantum information is encoded in the spatial position of an electron. We combine radio-frequency reflectometry measurements of the quantum capacitance of the device with microwave manipulation to drive transitions between the qubit states. This approach simplifies charge-state readout and allows us to operate the device at an optimal point where the qubit is first-order insensitive to charge noise. From these measurements, we are able to quantify the degree of charge noise experienced by the qubit and obtain an inhomogeneous charge coherence of 5 ns. We use a chopped microwave signal whose duty-cycle period is varied to measure the decay of the qubit states, yielding a charge relaxation time of 48 ns

Heralded Two-Photon Entanglement from Probabilistic Quantum Logic Operations on Multiple Parametric Down-Conversion Sources

An ideal controlled-NOT gate followed by projective measurements can be used to identify specific Bell states of its two input qubits. When the input qubits are each members of independent Bell states, these projective measurements can be used to swap the post-selected entanglement onto the remaining two qubits. Here we apply this strategy to produce heralded two-photon polarization entanglement using Bell states that originate from independent parametric down-conversion sources, and a particular probabilistic controlled-NOT gate that is constructed from linear optical elements. The resulting implementation is closely related to an earlier proposal by Sliwa and Banaszek [quant-ph/0207117], and can be intuitively understood in terms of familiar quantum information protocols. The possibility of producing a ``pseudo-demand'' source of two-photon entanglement by storing and releasing these heralded pairs from independent cyclical quantum memory devices is also discussed.Comment: 5 pages, 4 figures; submitted to IEEE Journal of Selected Topics in Quantum Electronics, special issue on "Quantum Internet Technologies

Variable temperature study of the crystal and magnetic structures of the giant magnetoresistant materials LMnAsO (L=La, Nd)

Peer reviewedPublisher PD

Enhanced Heat Transfer from Arrays of Jets Impinging on Plates

Multiple jets of various shapes, orientation and configuration are used regularly in a wide range of engineering applications to provide heating or cooling with impingement on a plate being one of the most common configurations due to the improved heat transfer rates. Design optimization has largely relied on empirical correlations that are limited by the range over which they were originally generated. Computational Fluid Mechanics is now sufficiently advanced to be used as an alternative method for obtaining optimal designs. This project uses the commercial Fluent package to compute heat transfer from a bank of jets impinging on a plate. Results for a single jet are validated against experimental data. The use of advanced turbulence modeling and appropriate boundary layer formulations are key ingredients for obtaining reliable calculations. The heat transfer resulting form the use of multi-jet configurations will be discussed in the paper

Inferring Coupling of Distributed Dynamical Systems via Transfer Entropy

In this work, we are interested in structure learning for a set of spatially distributed dynamical systems, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this framework due to the hidden variables, however we can exploit the properties of certain dynamical systems to formulate exact methods based on state space reconstruction. We approach the problem by using reconstruction theorems to analytically derive a tractable expression for the KL-divergence of a candidate DAG from the observed dataset. We show this measure can be decomposed as a function of two information-theoretic measures, transfer entropy and stochastic interaction. We then present two mathematically robust scoring functions based on transfer entropy and statistical independence tests. These results support the previously held conjecture that transfer entropy can be used to infer effective connectivity in complex networks