### 2199 results for qubit oscillator frequency

Contributors: Eugene Grichuk, Margarita Kuzmina, Eduard Manykin

Date: 2010-09-26

**qubit** simulates the behavior of electric field of
polarized light beam...**qubit** model has been designed as a
stochastic **oscillator** formed by a pair...**qubits** that is
exploited as a computation resource in one-way quantum
...**qubit** cluster, is designed, and system of equations for
network dynamics...one-**qubit**
gates are suggested. Changing of cluster entanglement degree...**oscillators** is
proposed for modeling of a cluster of entangled **qubits** ...**oscillators** with chaotically modulated limit cycle radii and
**frequencies**...**oscillators**...**qubit** model has been designed as a
stochastic oscillator formed by a pair ... A network of coupled stochastic **oscillators** is
proposed for modeling of a cluster of entangled **qubits** that is
exploited as a computation resource in one-way quantum
computation schemes. A **qubit** model has been designed as a
stochastic **oscillator** formed by a pair of coupled limit cycle
**oscillators** with chaotically modulated limit cycle radii and
**frequencies**. The **qubit** simulates the behavior of electric field of
polarized light beam and adequately imitates the states of two-level
quantum system. A cluster of entangled **qubits** can be associated
with a beam of polarized light, light polarization degree being
directly related to cluster entanglement degree. Oscillatory network,
imitating **qubit** cluster, is designed, and system of equations for
network dynamics has been written. The constructions of one-**qubit**
gates are suggested. Changing of cluster entanglement degree caused
by measurements can be exactly calculated.

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Contributors: Weidinger, Daniel, Gruebele, Martin

Date: 2007-07-10

**Qubits** are encoded in 2n vibrational computing states on the ground electronic ... Accurate rotation-vibration energy levels and transition dipoles of the molecule thiophosgene are used to model the execution of quantum gates with shaped laser pulses. **Qubits** are encoded in 2n vibrational computing states on the ground electronic surface of the molecule. Computations are carried out by cycling amplitude between these computing states and a gateway state with a shaped laser pulse. The shaped pulse that performs the computation in the computing states is represented by a physical model of a 128-1024 channel pulse shaper. Pulse shapes are optimized with a standard genetic algorithm, yielding experimentally realizable computing pulses. We study the robustness of optimization as a function of the vibrational states selected, rotational level structure, additional vibrational levels not assigned to the computation, and compensation for laser power variation across a molecular ensemble.

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Contributors: Owen, Edmund Thomas, Barnes, Crispin H. W.

Date: 2013-09-04

**oscillator** is proposed. The robustness of this technique is demonstrated...**qubit** to the electron's momentum. In order to incorporate this e ffect...**qubits** is the spin of an electron. However, in semiconductors, the spin-orbit...**qubits** are initially in the same state, no entanglement is generated as...**qubit**-**qubit**
interaction. Therefore, for an arbitrary initial state, this...two-**qubit** states using a pair of interacting particles in a one-dimensional ... Quantum states can contain correlations which are stronger than is possible in classical systems. Quantum information technologies use these correlations, which are known as entanglement, as a resource for implementing novel protocols in a diverse range of fields such as cryptography, teleportation and computing. However, current methods for generating the required entangled states are not necessarily robust against perturbations in the proposed systems. In this thesis, techniques will be developed for robustly generating the entangled states needed for these exciting new technologies.
The thesis starts by presenting some basic concepts in quantum information proccessing. In Ch. 2, the numerical methods which will be used to generate solutions for the dynamic systems in this thesis are presented. It is argued that using a GPU-accelerated staggered leapfrog technique provides a very efficient method for propagating the wave function.
In Ch. 3, a new method for generating maximally entangled two-**qubit** states using a pair of interacting particles in a one-dimensional harmonic **oscillator** is proposed. The robustness of this technique is demonstrated both analytically and numerically for a variety of interaction potentials. When the two **qubits** are initially in the same state, no entanglement is generated as there is no direct **qubit**-**qubit**
interaction. Therefore, for an arbitrary initial state, this process implements a root-of-swap entangling quantum gate. Some possible physical implementations of this proposal for low-dimensional semiconductor
systems are suggested.
One of the most commonly used **qubits** is the spin of an electron. However, in semiconductors, the spin-orbit interaction can couple this **qubit** to the electron's momentum. In order to incorporate this e ffect
into our numerical simulations, a new discretisation of this interaction is presented in Ch. 4 which is signi ficantly more accurate than traditional methods. This technique is shown to be similar to the standard discretisation for magnetic fields.
In Ch. 5, a simple spin-precession model is presented to predict the eff ect of the spin-orbit interaction on the entangling scheme of Ch. 3. It is shown that the root-of-swap quantum gate can be restored by introducing an additional constraint on the system. The robustness of the gate to perturbations in this constraint is demonstrated by presenting numerical solutions using the methods of Ch. 4.

Contributors: Zou, Xudong, Seshia, Ashwin Arunkumar

Date: 2015-04-28

**frequency** stability of non-linear MEMS **oscillators** has not been previously...**frequency** stability of a nonlinear MEMS **oscillator** under variable damping...**oscillator** noise predict an improvement in **frequency** stability with increasing...**oscillator**. The random walk **frequency** noise and flicker **frequency** noise...**Oscillators**...**frequency** but also the phase/**frequency** noise of a nonlinear MEMS square ... Linear models for **oscillator** noise predict an improvement in **frequency** stability with increasing Quality factor. Although it is well known that this result does not apply to non-linear **oscillators**, systematic experimental investigations of the impact of damping on **frequency** stability of non-linear MEMS **oscillators** has not been previously reported. This paper studies the **frequency** stability of a nonlinear MEMS **oscillator** under variable damping conditions. Analytical and experimental investigation of a MEMS square-wave **oscillator** embedding a double-ended tuning fork resonator driven into the non-linear regime is introduced. The experimental results indicate that for a pre-set drive level, the variation of air-damping changes the onset of nonlinear behaviour in the resonator, which not only impacts the output **frequency** but also the phase/**frequency** noise of a nonlinear MEMS square wave **oscillator**. The random walk **frequency** noise and flicker **frequency** noise levels are strongly correlated with the non-linear operating point of the resonator, whereas the white phase and white **frequency** noise levels are impacted both by the output power and by operative nonlinearities.

Contributors: Martin Vreugdenhil, John G R Jefferys, Peter D Ward, Olaleke O Oke, Andor Magony, Himashi Anver, Premysl Jiruska

Date: 2010-01-01

high-**frequency** gamma **oscillations** (fast-gamma; peak **frequency** approximately...**oscillations** generated in the deep layers. Fast-gamma was spatially less...low-**frequency** gamma **oscillations** (slow-gamma; peak **frequency** approximately...high-**frequency** gamma **oscillations** remain unknown. In rat visual cortex...high-**frequency** gamma **oscillations**. ... Synchronization of neuronal activity in the visual cortex at low (30-70 Hz) and high gamma band frequencies (> 70 Hz) has been associated with distinct visual processes, but mechanisms underlying high-**frequency** gamma **oscillations** remain unknown. In rat visual cortex slices, kainate and carbachol induce high-**frequency** gamma **oscillations** (fast-gamma; peak **frequency** approximately 80 Hz at 37 degrees C) that can coexist with low-**frequency** gamma **oscillations** (slow-gamma; peak **frequency** approximately 50 Hz at 37 degrees C) in the same column. Current-source density analysis showed that fast-gamma was associated with rhythmic current sink-source sequences in layer III and slow-gamma with rhythmic current sink-source sequences in layer V. Fast-gamma and slow-gamma were not phase-locked. Slow-gamma power fluctuations were unrelated to fast-gamma power fluctuations, but were modulated by the phase of theta (3-8 Hz) **oscillations** generated in the deep layers. Fast-gamma was spatially less coherent than slow-gamma. Fast-gamma and slow-gamma were dependent on gamma-aminobutyric acid (GABA)(A) receptors, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and gap-junctions, their frequencies were reduced by thiopental and were weakly dependent on cycle amplitude. Fast-gamma and slow-gamma power were differentially modulated by thiopental and adenosine A(1) receptor blockade, and their frequencies were differentially modulated by N-methyl-D-aspartate (NMDA) receptors, GluK1 subunit-containing receptors and persistent sodium currents. Our data indicate that fast-gamma and slow-gamma both depend on and are paced by recurrent inhibition, but have distinct pharmacological modulation profiles. The independent co-existence of fast-gamma and slow-gamma allows parallel processing of distinct aspects of vision and visual perception. The visual cortex slice provides a novel in vitro model to study cortical high-**frequency** gamma **oscillations**.

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Contributors: Drausin Wulsin, Brian Litt, Allison Pearce, Justin A Blanco, Abba Krieger, William C Stacey

Date: 2013-01-01

**frequency** events. These changes in relative rate occurred in pre- and ...High-**frequency** (100-500 Hz) **oscillations** (HFOs) recorded from intracranial...**frequency** events has also been identified. We hypothesize that temporal...high-**frequency** **oscillations** (HFOs) in each time epoch for dispersion analysis ... High-**frequency** (100-500 Hz) **oscillations** (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100-250 Hz) or fast ripples (250-500 Hz), and a third class of mixed **frequency** events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed **frequency** events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

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Contributors: Abdul-Niby, M., Alameen, M., Baitie, H.

Date: 2016-12-18

**frequency** synthesis, quadrature signal generation and phase locked loops...**oscillator** (ILO) of 35MHz **frequency**. Phase shifters at high **frequencies**...**oscillator**...**Oscillating** systems, locking of the **oscillators** can take place for injected...**frequency** to nth harmonics of the free-running **frequency**. In this paper ... In Self **Oscillating** systems, locking of the **oscillators** can take place for injected signals close in **frequency** to nth harmonics of the free-running **frequency**. In this paper, we present a simple design for digital phase shift control by using a harmonically injection locked **oscillator** (ILO) of 35MHz **frequency**. Phase shifters at high **frequencies** are essential in many communication system applications such as **frequency** synthesis, quadrature signal generation and phase locked loops (PLLs).

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Contributors: Rajib Bordoloi, Ranjana Bora Bordoloi, Gauranga Dhar Baruah 1 Women’s

Date: 2016-08-20

**oscillators**.
Key words: Coupled pendulum, Mode **frequency**, Phase diffusion...**frequency**...**oscillators** the mode **frequencies** of the
**oscillators** are far apart if ...**frequency** difference. The
equations of motion for the classical **oscillators**...**oscillators** and the **oscillations** that take place in a laser cavity. Our...**oscillators** and to explore
possibilities of any relationship between ... In this work we have presented an analogy between the coupled vibrations of two classical
**oscillators** and the **oscillations** that take place in a laser cavity. Our aim is to understand classically
the causes that lead to the phase diffusion in a system of coupled classical **oscillators** and to explore
possibilities of any relationship between phase fluctuation and the **frequency** difference. The
equations of motion for the classical **oscillators** have been derived and solved, for different values of
coupling coefficients, to obtain the expressions for the mode frequencies1. The solutions, while plotted
graphically have led us to the conclusion that in classical **oscillators** the mode **frequencies** of the
**oscillators** are far apart if their oscillation is heavily coupling dependent and consequently the phase
relationship of the **oscillators** fluctuate vigorously and frequently, which is the converse of what
happens in a laser cavity consisting atomic **oscillators**.
Key words: Coupled pendulum, Mode **frequency**, Phase diffusion

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Contributors: de Cheveigné, Alain, Arzounian, Dorothée

Date: 2015-09-27

**Oscillations** are an important aspect of brain activity, but they often...**frequency** bands. Approach. Here, we propose a methodology that reveals...**oscillations**, leading to uncertainty as to the true oscillatory nature...**frequency** analysis kernels have a temporal extent that blurs the time ...**frequency** analysis methods with which it remains complementary.
... Objective. **Oscillations** are an important aspect of brain activity, but they often have a low signal- to-noise ratio (SNR) due to source-to-electrode mixing with competing brain activity and noise. Filtering can improve the SNR of narrowband signals, but it introduces ringing effects that may masquerade as genuine **oscillations**, leading to uncertainty as to the true oscillatory nature of the phenomena. Likewise, time–**frequency** analysis kernels have a temporal extent that blurs the time course of narrowband activity, introducing uncertainty as to timing and causal relations between events and/or **frequency** bands. Approach. Here, we propose a methodology that reveals narrowband activity within multichannel data such as electroencephalography, magnetoencephalography, electrocorticography or local field potential. The method exploits the between-channel correlation structure of the data to suppress competing sources by joint diagonalization of the covariance matrices of narrowband filtered and unfiltered data. Main results. Applied to synthetic and real data, the method effectively extracts narrowband components at unfavorable SNR. Significance. Oscillatory components of brain activity, including weak sources that are hard or impossible to observe using standard methods, can be detected and their time course plotted accurately. The method avoids the temporal artifacts of standard filtering and time–**frequency** analysis methods with which it remains complementary.

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Contributors: Farzan Nadim, Hua-an Tseng

Date: 2010-01-01

**frequency** and thus the network **oscillation** **frequency**....**oscillations** changed the network **frequency**, consistent with the predictions...**oscillation** voltage range and waveforms (sine waves and realistic **oscillation**...**frequency** depends on the voltage range of the **oscillating** voltage waveform...**frequency**; and (3) correlations between parameters of the PD neuron **oscillation** ... Many oscillatory networks involve neurons that exhibit intrinsic rhythmicity but possess a large variety of voltage-gated currents that interact in a complex fashion, making it difficult to determine which factors control **frequency**. Yet these neurons often have preferred (resonance) frequencies that can be close to the network **frequency**. Because the preferred **frequency** results from the dynamics of ionic currents, it can be assumed to depend on parameters that determine the neuron's oscillatory waveform shape. The pyloric network **frequency** in the crab Cancer borealis is correlated with the preferred **frequency** of its bursting pacemaker neurons anterior burster and pyloric dilator (PD). We measured the preferred **frequency** of the PD neuron in voltage clamp, which allows control of the **oscillation** voltage range and waveforms (sine waves and realistic **oscillation** waveforms), and showed that (1) the preferred **frequency** depends on the voltage range of the **oscillating** voltage waveform; (2) the slope of the waveform near its peak has a strongly negative correlation with the preferred **frequency**; and (3) correlations between parameters of the PD neuron **oscillation** waveform and its preferred **frequency** can be used to predict shifts in the network **frequency**. As predicted by these results, dynamic clamp shifts of the upper or lower voltage limits of the PD neuron waveform during ongoing **oscillations** changed the network **frequency**, consistent with the predictions from the preferred **frequency**. These results show that the voltage waveform of oscillatory neurons can be predictive of their preferred **frequency** and thus the network **oscillation** **frequency**.

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