Electrically driven spin resonance in a bent disordered carbon nanotube

Resonant manipulation of carbon nanotube valley-spin qubits by an electric field is investigated theoretically. We develop a new analysis of electrically driven spin resonance exploiting fixed physical characteristics of the nanotube: a bend and inhomogeneous disorder. The spectrum is simulated for an electron valley-spin qubit coupled to a hole valley-spin qubit and an impurity electron spin, and features that coincide with a recent measurement are identified. We show that the same mechanism allows resonant control of the full four-dimensional spin-valley space.Comment: 11 pages, 7 figure

Theoretical calculations of excited states and fluorescence spectroscopy using density functional theory

Absorption and emission spectra from the lowest energy transition in BODIPY have been simulated in the gas and water phase using a quantum mechanics/molecular mechanics approach, with DFT and the maximum overlap method (MOM). A post-SCF spin-purification to MOM yields transition energies in agreement with experimental data. Spectral bands were simulated using structures from ab initio molecular dynamics simulations, in which the solvent water molecules are treated classically and DFT is used for BODIPY. The resulting spectra are consistent with experimental data, and demonstrate how absorption and emission spectra in solution can be simulated using a quantum mechanical treatment of the solute. The electronic structure and photoinduced electron transfer (PET) processes in a fluorescent K+ sensor have been studied using DFT and TDDFT to rationalise its function. Absorption and emission energies of the fluorophore-localised intense excitation are more accurately described using MOM than TDDFT. Analysis of molecular orbital energies from DFT calculations in different phases cannot account for the sensors function. It is necessary to consider the relative energies of the electronic states. The inclusion of implicit solvent lowers the energy of the charge transfer state making a reductive PET possible in the absence of K+, while no such process is possible when the sensor is bound to K+. Binding within the ethene–argon and formaldehyde–methane complexes in ground and electronically excited states is studied with equations of motion coupled-cluster theory (EOM-CCSD), MP2 theory and dispersion-corrected DFT (DFT-D). MP2/MOM potential energy curves are in good agreement with EOM-CCSD calculations for the Rydberg and valence states studied. B3LYP-D3 calculations are in agreement with EOM-CCSD for ground and valence excited states, however for Rydberg states significant deviation is observed for a variety of DFT-D methods. Varying D2 dispersion parameters results in closer agreement with EOM-CCSD for Rydberg states

High-pressure melting behavior of tin up to 105 GPa

The melting curve of Sn initially rises steeply as a function of pressure but exhibits a decrease in slope (dTm/dP) above 40 GPa to become nearly flat above 50 GPa. Previous studies have argued that a body-centered tetragonal (bct) to cubic (bcc) phase transition occurs in this range at room temperature. However, our investigations have shown that the phase behavior is more complex in this region with orthorhombic (bco) splitting of reflections occurring in the x-ray diffraction pattern above 32 GPa and coexisting diffraction signatures of bco and bcc structures are observed between 40 and 70 GPa. Here we have documented the simultaneous presence of bco and bcc reflections up to the melting point, negating the possibility that their coexistence might indicate a kinetically hindered first-order phase transformation. In this paper we have extended the observation of Sn melting relations into the megabar (P>100 GPa) range using the appearance of liquid diffuse scattering in x-ray diffraction patterns and discontinuities during thermal signal processing to diagnose the occurrence of melting. Both techniques yield consistent results that indicate the melting line maintains the same low slope up to the highest pressure examined and does not flatten. The results below approximately 40 GPa agree well with the melting relations produced recently using a multiphase equation of state fitted to available or assumed data. Above this pressure the experimental melting points lie increasingly below the predicted crystal-liquid phase boundary, but above the flat melting from past studies, indicating that the thermodynamic properties of the body-centered “γ”-Sn structure remain to be clarified

Conditioned spin and charge dynamics of a single electron quantum dot

In this article we describe the incoherent and coherent spin and charge dynamics of a single electron quantum dot. We use a stochastic master equation to model the state of the system, as inferred by an observer with access to only the measurement signal. Measurements obtained during an interval of time contribute, by a past quantum state analysis, to our knowledge about the system at any time t within that interval. Such analysis permits precise estimation of physical parameters, and we propose and test a modification of the classical Baum-Welch parameter re-estimation method to systems driven by both coherent and incoherent processes

SELECTED METABOLIC AND HEMODYNAMIC RESPONSES TO REPEATED STEADY-STATE BOUTS OF INDOOR CYCLING, UTILISING MARGINAL INCREASES IN MECHANICAL POWER OUTPUT: CONSIDERATIONS FOR THE EVALUATION OF INDIVIDUAL COMPETITIVE ROAD CYCLISTS USING A PORTABLE ON-BICYCLE C

Introduction It has been demonstrated by Sanderson, Cavanaugh et a1. (1985), and the authors, (1987 , that impul e and average net power distributions (W) generated about the pedal spindle and crank arms, vary with individual cyclists, either creating a mechanically desirable circular cycling pattern where the impulse is 'smoothed', or a 'butterfly' distribution indicating unequal force distribution(s throughout each pedaling cycle. Based on research performed indoors by Cavanaugh (1985), and Anderson (1986), and this group outdoors at the United States Cycling Federation Camp in Colorado in 1987 and 1988, it appears that techniques employed to reduce the counter-propulsive tangential crank arm forces could possible improve average net power magnitudes produced by individual elite cyclists outdoors during competition, and thus improve their overall time(s) recorded for selected events

Measuring the thermodynamic cost of timekeeping

All clocks, in some form or another, use the evolution of nature toward higher entropy states to quantify the passage of time. Because of the statistical nature of the second law and corresponding entropy flows, fluctuations fundamentally limit the performance of any clock. This suggests a deep relation between the increase in entropy and the quality of clock ticks. Indeed, minimal models for autonomous clocks in the quantum realm revealed that a linear relation can be derived, where for a limited regime every bit of entropy linearly increases the accuracy of quantum clocks. But can such a linear relation persist as we move toward a more classical system? We answer this in the affirmative by presenting the first experimental investigation of this thermodynamic relation in a nanoscale clock. We stochastically drive a nanometer-thick membrane and read out its displacement with a radio-frequency cavity, allowing us to identify the ticks of a clock. We show theoretically that the maximum possible accuracy for this classical clock is proportional to the entropy created per tick, similar to the known limit for a weakly coupled quantum clock but with a different proportionality constant. We measure both the accuracy and the entropy. Once nonthermal noise is accounted for, we find that there is a linear relation between accuracy and entropy and that the clock operates within an order of magnitude of the theoretical bound

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Radio-frequency characterization of a supercurrent transistor made of a carbon nanotube

A supercurrent transistor is a superconductor–semiconductor hybrid device in which the Josephson supercurrent is switched on and off using a gate voltage. While such devices have been studied using DC transport, radio-frequency measurements allow for more sensitive and faster experiments. Here a supercurrent transistor made from a carbon nanotube is measured simultaneously via DC conductance and radio-frequency reflectometry. The radio-frequency measurement resolves all the main features of the conductance data across a wide range of bias and gate voltage, and many of these features are seen more clearly. These results are promising for measuring other kinds of hybrid superconducting devices, in particular for detecting the reactive component of the impedance, which a DC measurement can never detect

Efficiently measuring a quantum device using machine learning

Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices