### 14513 results for qubit oscillator frequency

Contributors: M. Hebbache

Date: 2014-01-01

(Color online) NV−1 Rabi oscillations. Control **qubit** down: blue, red and green lines correspond, respectively, to the time evolution of |A|2, |B|2 and |C|2, i.e., the probabilities of finding the spin system in the state |⇓↓〉, |⇑↓〉 and |0↓〉. Control **qubit** up: red, blue and green lines represent, respectively, |A′|2, |B′|2 and |C′|2, i.e., the probabilities of finding the spin system in the state |⇓↑〉, |⇑↑〉 and |0↑〉, i.e., |A′|2=|B|2, |B′|2=|A|2 and |C′|2=|C|2 (see text). Fig. 4 gives details in the interval 60–120ns. They can also be revealed by a zoom in.
...Quantum computing requires a set of universal quantum gates. The standard set includes single quantum bit (**qubit**) gates and the controlled-NOT (CNOT) which is the analog of the classical XOR gate. It flips the state of the target **qubit** conditioned on the state of the control **qubit**. We investigated the possibility of implementing a CNOT logic gate using magnetically coupled impurity spins of diamond, namely the electron spin-1 carried by the nitrogen-vacancy color center and the electron spin-12 carried by a nearby nitrogen atom in substitutional position (P1 center). It is shown that a 96ns gate time with a high-fidelity can be realized by means of pulsed electron spin resonance spectroscopy....(Color online) |A|2 is the probability of finding the spin system in the state |⇓↓〉. It oscillates at the high frequency D (=2.88GHz). The frequency of the beats is χ/2 (=16.7MHz). The amplitude of oscillations is also modulated by an additional cosine wave signal of frequency χ (see text). |C|2 is the probability of finding the spin system in the state |0↓〉. It oscillates at the low frequency χ. It is almost zero in the time interval 90–100ns. The probability of finding spin system in the state |⇑↓〉, |B|2, has the same oscillations than |A|2 but it is anti-phase (see Fig. 3).
...(Color online) |A|2 is the probability of finding the spin system in the state |⇓↓〉. It **oscillates** at the high **frequency** D (=2.88GHz). The **frequency** of the beats is χ/2 (=16.7MHz). The amplitude of **oscillations** is also modulated by an additional cosine wave signal of **frequency** χ (see text). |C|2 is the probability of finding the spin system in the state |0↓〉. It **oscillates** at the low **frequency** χ. It is almost zero in the time interval 90–100ns. The probability of finding spin system in the state |⇑↓〉, |B|2, has the same **oscillations** than |A|2 but it is anti-phase (see Fig. 3).
...Ideal truth table and schematic representation of a two-**qubit** CNOT gate irradiated by a sequence of two microwave π/2-pulses of equal width t and a variable waiting time between pulses τ. In the text, x and y are the states of two impurity spins of diamond, namely the spin-12 carried by the P1 center and the spin-1 carried by the NV−1 color center. The symbol ⊕ is the addition modulo 2, or equivalently the XOR operation.
...(Color online) NV−1 Rabi **oscillations**. Control **qubit** down: blue, red and green lines correspond, respectively, to the time evolution of |A|2, |B|2 and |C|2, i.e., the probabilities of finding the spin system in the state |⇓↓〉, |⇑↓〉 and |0↓〉. Control **qubit** up: red, blue and green lines represent, respectively, |A′|2, |B′|2 and |C′|2, i.e., the probabilities of finding the spin system in the state |⇓↑〉, |⇑↑〉 and |0↑〉, i.e., |A′|2=|B|2, |B′|2=|A|2 and |C′|2=|C|2 (see text). Fig. 4 gives details in the interval 60–120ns. They can also be revealed by a zoom in.
... Quantum computing requires a set of universal quantum gates. The standard set includes single quantum bit (**qubit**) gates and the controlled-NOT (CNOT) which is the analog of the classical XOR gate. It flips the state of the target **qubit** conditioned on the state of the control **qubit**. We investigated the possibility of implementing a CNOT logic gate using magnetically coupled impurity spins of diamond, namely the electron spin-1 carried by the nitrogen-vacancy color center and the electron spin-12 carried by a nearby nitrogen atom in substitutional position (P1 center). It is shown that a 96ns gate time with a high-fidelity can be realized by means of pulsed electron spin resonance spectroscopy.

Files:

Contributors: Kouichi Ichimura

Date: 2001-09-01

A simple **frequency**-domain quantum computer with ions in a crystal coupled to a cavity mode...A quantum computer where quantum bits (**qubits**) are defined in **frequency** domain and interaction between **qubits** is mediated by a single cavity mode is proposed. In this quantum computer, **qubits** can be individually addressed regardless of their positions. Therefore, randomly distributed systems in space can be directly employed as **qubits**. An application of nuclear spins in rare-earth ions in a crystal for the quantum computer is quantitatively analyzed....**Qubits** in solids...Schematic diagram of **qubits** addressed in a **frequency** domain. The ions whose 3H4(1)±
3
2–1D2(1) transitions are resonant with a common cavity mode are employed as **qubits**.
...Basic scheme of the concept of the **frequency**-domain quantum computer. The atoms are coupled to a single cavity mode. Lasers with **frequencies** of νk and νl are directed onto the set of atoms and interact with the kth and lth atoms selectively.
...Schematic diagram of qubits addressed in a **frequency** domain. The ions whose 3H4(1)±
3
2–1D2(1) transitions are resonant with a common cavity mode are employed as qubits.
... A quantum computer where quantum bits (**qubits**) are defined in **frequency** domain and interaction between **qubits** is mediated by a single cavity mode is proposed. In this quantum computer, **qubits** can be individually addressed regardless of their positions. Therefore, randomly distributed systems in space can be directly employed as **qubits**. An application of nuclear spins in rare-earth ions in a crystal for the quantum computer is quantitatively analyzed.

Files:

Contributors: Gholamhossein Shahgoli, John Fielke, Jacky Desbiolles, Chris Saunders

Date: 2010-01-01

...Experimental PTO torque signal variation over time with increasing frequencies.
...Average PTO power as a function of **oscillating** **frequency** for straight (♦: solid line) and bent leg (□: broken line) tines (**oscillation** angle β=+27°).
...Based on the published benefits of oscillatory tillage, a subsoiler was developed at the University of South Australia, which had two deep working oscillatory tines and could be fitted with four shallow leading tines for increased loosening efficiency. A series of field trials were conducted in a sandy-loam soil to determine the most efficient setting of the tine's oscillatory motion and to compare the effect of using straight or bentleg tines. The tines were oscillated with an amplitude at the tip of ±69mm and an oscillation angle of 27° using a forward speed of 3km/h. The **frequency** of oscillation was varied from 1.9 to 8.8Hz. Analysis showed that the underside of the oscillating tine pushed rearward on the soil during part of the oscillation cycle, this decreased the draft in comparison to rigid tillage from 25.8 to 9.3kN. Increasing oscillation **frequency**, increased the PTO power requirement from 2.5kW at 1.9Hz to 26.3kW at 8.8Hz. The peaks and troughs in draft and torque were able to be aligned with the various phases of the oscillating tillage. An optimum oscillation **frequency** of 3.3Hz (velocity ratio of 1.5) was observed for minimum power to operate the oscillating subsoiler. Whilst at this setting, the combined draft and PTO power was similar to the draft power of rigid tillage, but when considering the higher losses due to tractive efficiency and lower PTO power losses, the oscillating tillage would be expected to require around 27% less engine power than rigid tillage....Optimising oscillation **frequency** in oscillatory tillage...Dominant frequency of torque signal over the oscillating frequency range.
...Dominant frequency of draft signal over the oscillating frequency range.
...ANOVA st...Subsoiler draft signals with time for the control and the range of **oscillating** **frequencies**.
...Dominant **frequency** of draft signal over the **oscillating** **frequency** range.
...Proportion of cycle time for cutting and compaction phases versus **oscillating** **frequency** (**oscillation** angle β=+27°).
...Dominan...Dominant **frequency** of torque signal over the **oscillating** **frequency** range.
...Synopsis of subsoiler tine motion parameters for different frequencies (a=±69mm, β=27°, V0=0.8m/s).
...To...**Frequency**...Based on the published benefits of oscillatory tillage, a subsoiler was developed at the University of South Australia, which had two deep working oscillatory tines and could be fitted with four shallow leading tines for increased loosening efficiency. A series of field trials were conducted in a sandy-loam soil to determine the most efficient setting of the tine's oscillatory motion and to compare the effect of using straight or bentleg tines. The tines were **oscillated** with an amplitude at the tip of ±69mm and an **oscillation** angle of 27° using a forward speed of 3km/h. The **frequency** of **oscillation** was varied from 1.9 to 8.8Hz. Analysis showed that the underside of the **oscillating** tine pushed rearward on the soil during part of the **oscillation** cycle, this decreased the draft in comparison to rigid tillage from 25.8 to 9.3kN. Increasing **oscillation** **frequency**, increased the PTO power requirement from 2.5kW at 1.9Hz to 26.3kW at 8.8Hz. The peaks and troughs in draft and torque were able to be aligned with the various phases of the **oscillating** tillage. An optimum **oscillation** **frequency** of 3.3Hz (velocity ratio of 1.5) was observed for minimum power to operate the **oscillating** subsoiler. Whilst at this setting, the combined draft and PTO power was similar to the draft power of rigid tillage, but when considering the higher losses due to tractive efficiency and lower PTO power losses, the **oscillating** tillage would be expected to require around 27% less engine power than rigid tillage....**Oscillating** tine ... Based on the published benefits of oscillatory tillage, a subsoiler was developed at the University of South Australia, which had two deep working oscillatory tines and could be fitted with four shallow leading tines for increased loosening efficiency. A series of field trials were conducted in a sandy-loam soil to determine the most efficient setting of the tine's oscillatory motion and to compare the effect of using straight or bentleg tines. The tines were **oscillated** with an amplitude at the tip of ±69mm and an **oscillation** angle of 27° using a forward speed of 3km/h. The **frequency** of **oscillation** was varied from 1.9 to 8.8Hz. Analysis showed that the underside of the **oscillating** tine pushed rearward on the soil during part of the **oscillation** cycle, this decreased the draft in comparison to rigid tillage from 25.8 to 9.3kN. Increasing **oscillation** **frequency**, increased the PTO power requirement from 2.5kW at 1.9Hz to 26.3kW at 8.8Hz. The peaks and troughs in draft and torque were able to be aligned with the various phases of the **oscillating** tillage. An optimum **oscillation** **frequency** of 3.3Hz (velocity ratio of 1.5) was observed for minimum power to operate the **oscillating** subsoiler. Whilst at this setting, the combined draft and PTO power was similar to the draft power of rigid tillage, but when considering the higher losses due to tractive efficiency and lower PTO power losses, the **oscillating** tillage would be expected to require around 27% less engine power than rigid tillage.

Files:

Contributors: Dong-Qi Liu, Gang-Qin Liu, Yan-Chun Chang, Xin-Yu Pan

Date: 2014-01-01

Characterization of lifetime of NV center spins. (a) Ramsey interference of NV S1 (circle) and NV S2 (diamond). The oscillation in Ramsey signal originates from the beating among different transitions corresponding to the host three 14N nuclear spin states. The oscillation frequency of Ramsey signal is equal to microwave detuning from spin resonance. Solid lines ~exp[−(t/T2⁎)m] fit the experimental data points, where m is a free parameter. (b) Comparison of Hahn echo coherence time T2 of NV S1 (circle) and NV S2 (diamond). The solid lines are fits to ~exp[−(t/T2)p], in which p is a fit parameter.
...Detection and manipulation of the **qubit**. (a) Fluorescence image of nanodiamond prepared on the CPW transmission line. NV S1 is circled. The inset is a photo of CPW with 20μm gaps fabricated on a silica glass. (b) CW ODMR spectrum for NV S1. The inset is energy levels of NV center. A 532nm laser is used to excite and initialize the NV center. Fluorescence is collected by a confocal microscope. (c) Rabi **oscillation** of NV S1. Rabi **oscillation** period is about 62ns. (d) Hahn echo and CPMG control pulse sequences. πx (πy) implies the direction of microwave magnetic fields parallel to x (y).
...Spectral density of the spin bath. (a) NV S1, (b) NV S2. All values of spectral density S(ω) of the spin bath are extracted from the CPMG data (blue points). Each blue data point represents a specific probed **frequency** ω=πn/t, in which n is the number of control pulses and t is the specific duration. The red points are the average values at a certain **frequency**. The mean spectral density is fit to the Lorentzian function (Eq. (3)) (green line). (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.)
...Overcoming the spin **qubit** decoherence is a challenge for quantum science and technology. We investigate the decoherence process in nanodiamonds by Carr–Purcell–Meiboom–Gill (CPMG) technique at room temperature. We find that the coherence time T2 scales as nγ. The elongation effect of coherence time can be represented by a constant power of the number of pulses n. Considering the filter function of CPMG decoupling sequence as a δfunction, the spectrum density of noise has been reconstructed directly from the coherence time measurements and a Lorentzian noise power spectrum model agrees well with the experiment. These results are helpful for the application of nanodiamonds to nanoscale magnetic imaging....Characterization of lifetime of NV center spins. (a) Ramsey interference of NV S1 (circle) and NV S2 (diamond). The **oscillation** in Ramsey signal originates from the beating among different transitions corresponding to the host three 14N nuclear spin states. The **oscillation** **frequency** of Ramsey signal is equal to microwave detuning from spin resonance. Solid lines ~exp[−(t/T2⁎)m] fit the experimental data points, where m is a free parameter. (b) Comparison of Hahn echo coherence time T2 of NV S1 (circle) and NV S2 (diamond). The solid lines are fits to ~exp[−(t/T2)p], in which p is a fit parameter.
...Detection and manipulation of the qubit. (a) Fluorescence image of nanodiamond prepared on the CPW transmission line. NV S1 is circled. The inset is a photo of CPW with 20μm gaps fabricated on a silica glass. (b) CW ODMR spectrum for NV S1. The inset is energy levels of NV center. A 532nm laser is used to excite and initialize the NV center. Fluorescence is collected by a confocal microscope. (c) Rabi oscillation of NV S1. Rabi oscillation period is about 62ns. (d) Hahn echo and CPMG control pulse sequences. πx (πy) implies the direction of microwave magnetic fields parallel to x (y).
...Spectral density of the spin bath. (a) NV S1, (b) NV S2. All values of spectral density S(ω) of the spin bath are extracted from the CPMG data (blue points). Each blue data point represents a specific probed frequency ω=πn/t, in which n is the number of control pulses and t is the specific duration. The red points are the average values at a certain frequency. The mean spectral density is fit to the Lorentzian function (Eq. (3)) (green line). (For interpretation of the references to color in this figure caption, the reader is referred to the web version of this article.)
... Overcoming the spin **qubit** decoherence is a challenge for quantum science and technology. We investigate the decoherence process in nanodiamonds by Carr–Purcell–Meiboom–Gill (CPMG) technique at room temperature. We find that the coherence time T2 scales as nγ. The elongation effect of coherence time can be represented by a constant power of the number of pulses n. Considering the filter function of CPMG decoupling sequence as a δfunction, the spectrum density of noise has been reconstructed directly from the coherence time measurements and a Lorentzian noise power spectrum model agrees well with the experiment. These results are helpful for the application of nanodiamonds to nanoscale magnetic imaging.

Files:

Contributors: Fang Yuan, Daotong Chong, Quanbin Zhao, Weixiong Chen, Junjie Yan

Date: 2016-07-01

Bubble diameters at the steam mass flux of 250kgm−2s−1.
...Dominant **frequencies** of 10mm nozzle.
...Condensation regime map by Cho et al. [1] (C–chugging, TC—transitional region from chugging to CO, CO—condensation **oscillation**, SC—stable condensation, BCO—bubble condensation **oscillation**, IOC—interfacial **oscillation** condensation).
...Condensation **oscillation**...**Frequencies** at different test conditions—250kgm−2s−1.
...Test conditions.
...FFT results at pool temperature 35°C and steam mass **flux 300kgm**−2s−1.
...Prediction accuracy of simultaneous equations for **oscillation** **frequency**.
...The condensation **oscillation** of submerged steam was investigated theoretically and experimentally at the condensation **oscillation** regime. It was found that pressure **oscillation** **frequency** was consistent with the bubble **oscillating** **frequency** and there was a quasi-steady stage when bubble diameters remained constant. A thermal-hydraulic model for the condensation **oscillation** regime was proposed based on potential flow theory, taking into account the effects of interface condensation and translatory flow. Theoretical derivations indicated that **oscillation** **frequencies** were mainly determined by bubble diameters and translatory velocity. A force balance model was applied to the calculation of bubble diameters at quasi-steady stage, and the **oscillation** **frequencies** were predicted with the calculated diameters. Theoretical analysis and experimental results turned out that **oscillation** **frequencies** at the condensation **oscillation** regime decreased with the increasing steam mass flux and pool temperature. The predicted **frequencies** corresponded to the experimental data well with the discrepancies of ±21.7%....The condensation oscillation of submerged steam was investigated theoretically and experimentally at the condensation oscillation regime. It was found that pressure oscillation **frequency** was consistent with the bubble oscillating **frequency** and there was a quasi-steady stage when bubble diameters remained constant. A thermal-hydraulic model for the condensation oscillation regime was proposed based on potential flow theory, taking into account the effects of interface condensation and translatory flow. Theoretical derivations indicated that oscillation **frequencies** were mainly determined by bubble diameters and translatory velocity. A force balance model was applied to the calculation of bubble diameters at quasi-steady stage, and the oscillation **frequencies** were predicted with the calculated diameters. Theoretical analysis and experimental results turned out that oscillation **frequencies** at the condensation oscillation regime decreased with the increasing steam mass flux and pool temperature. The predicted **frequencies** corresponded to the experimental data well with the discrepancies of ±21.7%....**Frequency**...**Frequencies** at different test conditions—300kgm−2s−1.
...Prediction accuracy of simultaneous equations for oscillation frequency.
... The condensation **oscillation** of submerged steam was investigated theoretically and experimentally at the condensation **oscillation** regime. It was found that pressure **oscillation** **frequency** was consistent with the bubble **oscillating** **frequency** and there was a quasi-steady stage when bubble diameters remained constant. A thermal-hydraulic model for the condensation **oscillation** regime was proposed based on potential flow theory, taking into account the effects of interface condensation and translatory flow. Theoretical derivations indicated that **oscillation** **frequencies** were mainly determined by bubble diameters and translatory velocity. A force balance model was applied to the calculation of bubble diameters at quasi-steady stage, and the **oscillation** **frequencies** were predicted with the calculated diameters. Theoretical analysis and experimental results turned out that **oscillation** **frequencies** at the condensation **oscillation** regime decreased with the increasing steam mass flux and pool temperature. The predicted **frequencies** corresponded to the experimental data well with the discrepancies of ±21.7%.

Files:

Contributors: Mina Amiri, Jean-Marc Lina, Francesca Pizzo, Jean Gotman

Date: 2016-01-01

Examples of a spike without HFOs (left) and a spike with HFOs (right), as defined with the Analytic Morse wavelet in the time–**frequency** domain.
...High **Frequency** **Oscillations**...To demonstrate and quantify the occurrence of false High **Frequency** Oscillations (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal....Parameter selection for the Analytic Morse Wavelet; top: time–**frequency** presentation for different values of n (m=40), bottom: raw signal and filtered signal (80–250Hz). Blue lines represent HFO interval marked visually.
...Examples of detection errors. Left: HFO without isolated blob but having **oscillation** in the raw signal. Right: HFO without visible **oscillation** in the raw signal but representing an isolated peak. Blue lines show the HFO interval marked by reviewers.
...Parameter selection for the Analytic Morse **Wavelet; **top: time–**frequency** presentation for different values of n (m=40), bottom: raw signal and filtered signal (80–250Hz). Blue lines represent HFO interval marked visually.
...Time–**frequency**...High **Frequency** Oscillations and spikes: Separating real HFOs from false oscillations...High **Frequency** Oscillations...To demonstrate and quantify the occurrence of false High **Frequency** **Oscillations** (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal. ... To demonstrate and quantify the occurrence of false High **Frequency** **Oscillations** (HFOs) generated by the filtering of sharp events. To distinguish real HFOs from spurious ones using analysis of the raw signal.

Files:

Contributors: Ludovic Righetti, Jonas Buchli, Auke Jan Ijspeert

Date: 2006-04-15

Nonlinear **oscillators** are widely used in biology, physics and engineering for modeling and control. They are interesting because of their synchronization properties when coupled to other dynamical systems. In this paper, we propose a learning rule for **oscillators** which adapts their **frequency** to the **frequency** of any periodic or pseudo-periodic input signal. Learning is done in a dynamic way: it is part of the dynamical system and not an offline process. An interesting property of our model is that it is easily generalizable to a large class of **oscillators**, from phase **oscillators** to relaxation **oscillators** and strange attractors with a generic learning rule. One major feature of our learning rule is that the **oscillators** constructed can adapt their **frequency** without any signal processing or the need to specify a time window or similar free parameters. All the processing is embedded in the dynamics of the adaptive **oscillator**. The convergence of the learning is proved for the Hopf **oscillator**, then numerical experiments are carried out to explore the learning capabilities of the system. Finally, we generalize the learning rule to non-harmonic **oscillators** like relaxation **oscillators** and strange attractors....Adaptive **frequency** **oscillator**...Dynamic Hebbian learning in adaptive **frequency** **oscillators**...The left plot of this figure represents **the** evolution of ω(t) when **the** adaptive Hopf **oscillator** is coupled to **the** z variable of **the** Lorenz attractor. The right plot represents **the** z variable of **the** Lorenz attractor. We clearly see that **the** adaptive Hopf oscillators can correctly learn **the** pseudo-**frequency** of **the** Lorenz attractor. See **the** text for more details.
...We show **the** adaptation of **the** Van der Pol **oscillator** to **the** **frequencies** of various input signals: (**a**) **a** simple sinusoidal input (F=sin(40t)), (b) **a** sinusoidal input with uniformly distributed noise (F=sin(40t)+uniform noise in [−0.5,0.5]), (c) **a** square input (F=square(40t)) and (d) **a** sawtooth input (F=sawtooth(40t)). For each experiment, we set ϵ=0.7 and α=100 and we show three plots. The right one shows **the** evolution of ω(t). The upper left graph is **a** plot of **the** oscillations, x, of **the** system, at **the** beginning of **the** learning. The lower graph shows **the** oscillations at **the** end of learning. In both graphs, we also plotted **the** input signal (dashed). In each experiment, ω converges to ω≃49.4, which corresponds to oscillations with **a** **frequency** of 40 rad s−1 like **the** input and thus **the** **oscillator** correctly adapts **its** **frequency** to **the** **frequency** of **the** input.
...Plots of **the** **frequency** of **the** oscillations of **the** Van der Pol **oscillator** according to ω. Here α=50. There are two plots, for **the** dotted line **the** **oscillator** is not coupled and for **the** plain line **the** **oscillator** is coupled to F=sin30t. The strength of coupling is ϵ=2. We clearly see basins of phase-locking, **the** main one for **frequency** of oscillations 30. The other major basins appear each 30n (dotted horizontal lines). We also notice small entrainment basins for some **frequencies** of **the** form 30pq. For **a** more detailed discussion of these results refer to **the** text.
...This figure shows **the** convergence of ω for several initial **frequencies**. The Van der Pol **oscillator** is perturbed by F=sin(30t), with coupling ϵ=0.7, α=50. We clearly see that **the** convergence directly depends on **the** initial conditions and as expected **the** different kinds of convergence correspond to **the** several entrainment basins of Fig. 7.
...The left plot of this figure represents the evolution of ω(t) when the adaptive Hopf **oscillator** is coupled to the z variable of the Lorenz attractor. The right plot represents the z variable of the Lorenz attractor. We clearly see that the adaptive Hopf **oscillators** can correctly learn the pseudo-**frequency** of the Lorenz attractor. See the text for more details.
...Frequency spectra of **the** Van der Pol **oscillator**, both plotted with ω=10. The left figure is an **oscillator** with α=10 and on **the** right **the** nonlinearity is higher, α=50. On **the** y-axis we plotted **the** square root of **the** power intensity, in order to be able to see smaller **frequency** components.
...Plots of the **frequency** of the **oscillations** of the Van der Pol **oscillator** according to ω. Here α=50. There are two plots, for the dotted line the **oscillator** is not coupled and for the plain line the **oscillator** is coupled to F=sin30t. The strength of coupling is ϵ=2. We clearly see basins of phase-locking, the main one for **frequency** of **oscillations** 30. The other major basins appear each 30n (dotted horizontal lines). We also notice small entrainment basins for some **frequencies** of the form 30pq. For a more detailed discussion of these results refer to the text.
...We show the adaptation of the Van der Pol **oscillator** to the **frequencies** of various input signals: (a) a simple sinusoidal input (F=sin(40t)), (b) a sinusoidal input with uniformly distributed noise (F=sin(40t)+uniform noise in [−0.5,0.5]), (c) a square input (F=square(40t)) and (d) a sawtooth input (F=sawtooth(40t)). For each experiment, we set ϵ=0.7 and α=100 and we show three plots. The right one shows the evolution of ω(t). The upper left graph is a plot of the **oscillations**, x, of the system, at the beginning of the learning. The lower graph shows the **oscillations** at the end of learning. In both graphs, we also plotted the input signal (dashed). In each experiment, ω converges to ω≃49.4, which corresponds to **oscillations** with a **frequency** of 40 rad s−1 like the input and thus the **oscillator** correctly adapts its **frequency** to the **frequency** of the input.
...**Frequency** spectra of the Van der Pol **oscillator**, both plotted with ω=10. The left figure is an **oscillator** with α=10 and on the right the nonlinearity is higher, α=50. On the y-axis we plotted the square root of the power intensity, in order to be able to see smaller **frequency** components.
...This figure shows the convergence of ω for several initial **frequencies**. The Van der Pol **oscillator** is perturbed by F=sin(30t), with coupling ϵ=0.7, α=50. We clearly see that the convergence directly depends on the initial conditions and as expected the different kinds of convergence correspond to the several entrainment basins of Fig. 7.
... Nonlinear **oscillators** are widely used in biology, physics and engineering for modeling and control. They are interesting because of their synchronization properties when coupled to other dynamical systems. In this paper, we propose a learning rule for **oscillators** which adapts their **frequency** to the **frequency** of any periodic or pseudo-periodic input signal. Learning is done in a dynamic way: it is part of the dynamical system and not an offline process. An interesting property of our model is that it is easily generalizable to a large class of **oscillators**, from phase **oscillators** to relaxation **oscillators** and strange attractors with a generic learning rule. One major feature of our learning rule is that the **oscillators** constructed can adapt their **frequency** without any signal processing or the need to specify a time window or similar free parameters. All the processing is embedded in the dynamics of the adaptive **oscillator**. The convergence of the learning is proved for the Hopf **oscillator**, then numerical experiments are carried out to explore the learning capabilities of the system. Finally, we generalize the learning rule to non-harmonic **oscillators** like relaxation **oscillators** and strange attractors.

Files:

Contributors: S Lee, R Blowes, A.D Milner

Date: 1998-09-01

Phase analysis in high-**frequency** oscillation...high-**frequency** oscillation...Summary of resonance frequencies found in all 10 babies (1 and 2 represents first and second run, respectively)
...A screen from our phase analysis program, showing phase analysis performed at four points of the respiratory cycle: top of breath, mid-inspiration, mid-expiration and bottom of breath. Corresponding points from the driving trace and the mouth pressure trace are matched and the phase difference calculated. In this case, the phase difference at the top of breath is 0° at an oscillating frequency of 20 Hz.
...Summary of resonance **frequencies** found in all 10 babies (1 and 2 represents first and second run, respectively)
...In an oscillating system driven by a sine wave pump, the resonance **frequency** of the respiratory system can be determined using phase analysis. At resonance **frequency**, when elastance and inertance cancel out, flow becomes in-phase with resistance. In premature infants with respiratory distress syndrome, owing to surfactant deficiency, localized areas of hyperinflation and collapse develop, resulting in complex changes in overall pulmonary mechanics. We investigated the effect of measuring resonance **frequency** of the respiratory system by phase analysis at different points of the respiratory cycle: end of inspiration, end of expiration, mid-inspiration and mid-expiration. Ten ventilated premature infants with respiratory distress syndrome were studied, gestational age ranged from 24 to 30 weeks (mean 27.6 weeks) and birth weight ranged from 0.7 to 1.505 kg (mean 0.984 kg). Results: The resonance **frequency** was consistently higher when measured at the end of inspiration compared with the end of expiration. The expected trend of phase variation, that is, negative below the resonance **frequency** and positive above, was most consistently found when analysis was done at the end of inspiration. Conclusions: These findings were most likely a result of the complexity of pulmonary mechanics in the surfactant-deficient lungs, rendering the single compartment model we based our theory on inadequate. However, phase analysis performed at the end of inspiration seemed to produce the most reliable and consistent results....A screen from our phase analysis program, showing phase analysis performed at four points of the respiratory cycle: top of breath, mid-inspiration, mid-expiration and bottom of breath. Corresponding points from the driving trace and the mouth pressure trace are matched and the phase difference calculated. In this case, the phase difference at the top of breath is 0° at an **oscillating** **frequency** of 20 Hz.
...high-**frequency** **oscillation**...In an **oscillating** system driven by a sine wave pump, the resonance **frequency** of the respiratory system can be determined using phase analysis. At resonance **frequency**, when elastance and inertance cancel out, flow becomes in-phase with resistance. In premature infants with respiratory distress syndrome, owing to surfactant deficiency, localized areas of hyperinflation and collapse develop, resulting in complex changes in overall pulmonary mechanics. We investigated the effect of measuring resonance **frequency** of the respiratory system by phase analysis at different points of the respiratory cycle: end of inspiration, end of expiration, mid-inspiration and mid-expiration. Ten ventilated premature infants with respiratory distress syndrome were studied, gestational age ranged from 24 to 30 weeks (mean 27.6 weeks) and birth weight ranged from 0.7 to 1.505 kg (mean 0.984 kg). Results: The resonance **frequency** was consistently higher when measured at the end of inspiration compared with the end of expiration. The expected trend of phase variation, that is, negative below the resonance **frequency** and positive above, was most consistently found when analysis was done at the end of inspiration. Conclusions: These findings were most likely a result of the complexity of pulmonary mechanics in the surfactant-deficient lungs, rendering the single compartment model we based our theory on inadequate. However, phase analysis performed at the end of inspiration seemed to produce the most reliable and consistent results. ... In an **oscillating** system driven by a sine wave pump, the resonance **frequency** of the respiratory system can be determined using phase analysis. At resonance **frequency**, when elastance and inertance cancel out, flow becomes in-phase with resistance. In premature infants with respiratory distress syndrome, owing to surfactant deficiency, localized areas of hyperinflation and collapse develop, resulting in complex changes in overall pulmonary mechanics. We investigated the effect of measuring resonance **frequency** of the respiratory system by phase analysis at different points of the respiratory cycle: end of inspiration, end of expiration, mid-inspiration and mid-expiration. Ten ventilated premature infants with respiratory distress syndrome were studied, gestational age ranged from 24 to 30 weeks (mean 27.6 weeks) and birth weight ranged from 0.7 to 1.505 kg (mean 0.984 kg). Results: The resonance **frequency** was consistently higher when measured at the end of inspiration compared with the end of expiration. The expected trend of phase variation, that is, negative below the resonance **frequency** and positive above, was most consistently found when analysis was done at the end of inspiration. Conclusions: These findings were most likely a result of the complexity of pulmonary mechanics in the surfactant-deficient lungs, rendering the single compartment model we based our theory on inadequate. However, phase analysis performed at the end of inspiration seemed to produce the most reliable and consistent results.

Files:

Contributors: Xiufeng Cao, Qing Ai, Chang-Pu Sun, Franco Nori

Date: 2012-01-09

**Qubit**...We propose a strategy to demonstrate the transition from the quantum Zeno effect (QZE) to the anti-Zeno effect (AZE) using a superconducting **qubit** coupled to a transmission line cavity, by varying the central **frequency** of the cavity mode. Our results are obtained without the rotating wave approximation (RWA), and the initial state (a dressed state) is easy to prepare. Moreover, we find that in the presence of both qubitʼs intrinsic bath and the cavity bath, the emergence of the QZE and the AZE behaviors relies not only on the match between the **qubit** energy-level-spacing and the central **frequency** of the cavity mode, but also on the coupling strength between the **qubit** and the cavity mode....The normalized effective decay rate γ(τ)/**γ0 of** the qubit for two quality factors Q when τ=5Δ−1, in the presence of both the cavity bath and the low-frequency qubitʼs intrinsic bath.
...(Color online.) (a) Superconducting circuit model of a frequency-tunable transmission line resonator, which is archived by changing the boundary condition, coupled with a qubit. (b) Superconducting circuit model (1) of the effective tunable inductors, which are consisted of a series array of SQUIDs (2).
...(Color online.) Contour plots of the normalized decay rate γ(τ)/γ0 of the **qubit** only in the cavity bath, versus the time interval τ between successive measurements, and the central **frequency** ωcav of the cavity mode. (a) The width of the cavity **frequency** is λ=10−4Δ, and accordingly the cavity quality factor Q=104. (b) The width of the cavity **frequency** λ=5×10−3Δ, corresponding to the cavity quality factor Q=2×103. The region 1⩽γ(τ)/γ0⩽1.05 is shown as light magenta. The QZE region corresponds to γ(τ)/γ01. Evidently, a transition from the QZE to the AZE is observed by varying the central **frequency** of the cavity mode at finite τ (τ>0.6Δ−1 when Q=104, and τ>2.6Δ−1 when Q=2×103).
...The normalized effective decay rate γ(τ)/γ0 of the **qubit** for two quality factors Q when τ=5Δ−1, in the presence of both the cavity bath and the low-**frequency** qubitʼs intrinsic bath.
...(Color online.) (a) Superconducting circuit model of a **frequency**-tunable transmission line resonator, which is archived by changing the boundary condition, coupled with a **qubit**. (b) Superconducting circuit model (1) of the effective tunable inductors, which are consisted of a series array of SQUIDs (2).
...(Color online.) (a) Sketch of a qubit with the spontaneous dissipation rate γ coupled to a cavity with the loss rate κ via a coupling strength g. (b) and (c) schematically show the bath density spectrum of the qubit environment: (b) the Ohmic qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid), (c) the low-frequency qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid).
...(Color online.) Time dependence of the probability for the **qubit** at its excited state. In the resonant case, the parameters are ωcav=Δ=100g and τ=0.1g−1. In the detuning case, the cavity mode **frequency** is varied to ωcav=80g. Note that the successive measurements slow down the decay rate of excited state in the resonant case, which is the QZE. While in the detuning case, the measurements speed up the **qubit** decay rate, which is the AZE.
...The transition from quantum Zeno to anti-Zeno effects for a **qubit** in a cavity by varying the cavity **frequency**...(Color online.) (a) Sketch of a **qubit** with the spontaneous dissipation rate γ coupled to a cavity with the loss rate κ via a coupling strength g. (b) and (c) schematically show the bath density spectrum of the **qubit** environment: (b) the Ohmic qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid), (c) the low-**frequency** qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid).
...(Color online.) Contour plots of the normalized decay rate γ(τ)/**γ0 of** the qubit only in the cavity bath, versus the time interval τ between successive measurements, and the central frequency ωcav of the cavity mode. (a) The width of the cavity frequency is λ=10−4Δ, and accordingly the cavity quality factor Q=104. (b) The width of the cavity frequency λ=5×10−3Δ, corresponding to the cavity quality factor Q=2×103. The region 1⩽γ(τ)/γ0⩽1.05 is shown as light magenta. The QZE region corresponds to γ(τ)/γ01. Evidently, a transition from the QZE to the AZE is observed by varying the central frequency of the cavity mode at finite τ (τ>0.6Δ−1 when Q=104, and τ>2.6Δ−1 when Q=2×103).
...(Color online.) Time dependence of the probability for the qubit at its excited state. In the resonant case, the parameters are ωcav=Δ=100g and τ=0.1g−1. In the detuning case, the cavity mode frequency is varied to ωcav=80g. Note that the successive measurements slow down the decay rate of excited state in the resonant case, which is the QZE. While in the detuning case, the measurements speed up the qubit decay rate, which is the AZE.
... We propose a strategy to demonstrate the transition from the quantum Zeno effect (QZE) to the anti-Zeno effect (AZE) using a superconducting **qubit** coupled to a transmission line cavity, by varying the central **frequency** of the cavity mode. Our results are obtained without the rotating wave approximation (RWA), and the initial state (a dressed state) is easy to prepare. Moreover, we find that in the presence of both qubitʼs intrinsic bath and the cavity bath, the emergence of the QZE and the AZE behaviors relies not only on the match between the **qubit** energy-level-spacing and the central **frequency** of the cavity mode, but also on the coupling strength between the **qubit** and the cavity mode.

Files:

Contributors: B.M.R. Schneider, C. Gollub, K.-L. Kompa, R. de Vivie-Riedle

Date: 2007-09-25

Quantum gates are optimized for the IR active high **frequency** modes of MnBr(CO)5-complexes. We investigate whether the selectivity for quantum gates is conserved for energetically close lying **qubits** of different symmetry that are nevertheless simultaneously addressable with the same laser pulse. The **qubits** are encoded in different vibrational normal modes, which are separated only by 50cm−1. Furthermore the influence of additional non-**qubit** modes on the efficiency of the quantum gates optimized for the pure **qubit** system is studied. Potential perturbers are low **frequency** vibrational modes highly populated at room temperature or vibrational modes that seems predestined to interfere during the gate operation. To prevent translational motion, the possibility of spatial localization is explored by incorporation of the carbonyl complex in the unit cell of a MFI zeolite....PES of the **qubit** system (a) and total dipole surface (b). For both surfaces: −52.8 pm⩽rA1⩽+52.8pm and −37.4pm⩽rE⩽+37.4pm.
...Normal modes included in the quantum dynamical calculation. (a) Coordinates of the **qubit** modes, (b) coordinates of the non-**qubit** modes.
...Spectral analysis of the NOT (top) and CNOT (bottom) gate. The solid lines correspond to the spectra of the optimized pulses, the dashed lines to the spectra of the sub pulses. The vertical lines indicate the relevant qubit basis transition frequencies for the quantum gates.
...spectroscopical** data** of the qubit vibrational modes E** and **A1** and **the non-qubit modes, the δ-deformation mode (E) and the dissociative mode (A1)
...Robustness of quantum gates operating on the high **frequency** modes of MnBr(CO)5...Spectral analysis of the NOT (top) and CNOT (bottom) gate. The solid lines correspond to the spectra of the optimized pulses, the dashed lines to the spectra of the sub pulses. The vertical lines indicate the relevant **qubit** basis transition **frequencies** for the quantum gates.
...Normal modes included in the quantum dynamical calculation. (a) Coordinates of the qubit modes, (b) coordinates of the non-qubit modes.
...spectroscopical data of the **qubit** vibrational modes E and A1 and the non-**qubit** modes, the δ-deformation mode (E) and the dissociative mode (A1)
...PES of the qubit system (a) and total dipole surface (b). For both surfaces: −52.8 pm⩽rA1⩽+52.8pm and −37.4pm⩽rE⩽+37.4pm.
... Quantum gates are optimized for the IR active high **frequency** modes of MnBr(CO)5-complexes. We investigate whether the selectivity for quantum gates is conserved for energetically close lying **qubits** of different symmetry that are nevertheless simultaneously addressable with the same laser pulse. The **qubits** are encoded in different vibrational normal modes, which are separated only by 50cm−1. Furthermore the influence of additional non-**qubit** modes on the efficiency of the quantum gates optimized for the pure **qubit** system is studied. Potential perturbers are low **frequency** vibrational modes highly populated at room temperature or vibrational modes that seems predestined to interfere during the gate operation. To prevent translational motion, the possibility of spatial localization is explored by incorporation of the carbonyl complex in the unit cell of a MFI zeolite.

Files: