Circuit Quantum Electrodynamics with a Spin Qubit

Circuit quantum electrodynamics allows spatially separated superconducting qubits to interact via a "quantum bus", enabling two-qubit entanglement and the implementation of simple quantum algorithms. We combine the circuit quantum electrodynamics architecture with spin qubits by coupling an InAs nanowire double quantum dot to a superconducting cavity. We drive single spin rotations using electric dipole spin resonance and demonstrate that photons trapped in the cavity are sensitive to single spin dynamics. The hybrid quantum system allows measurements of the spin lifetime and the observation of coherent spin rotations. Our results demonstrate that a spin-cavity coupling strength of 1 MHz is feasible.Comment: Related papers at http://pettagroup.princeton.edu

Room-temperature cavity quantum electrodynamics with strongly coupled Dicke states

Quantum Engineering: ambient solid-state quantum optics Creating practical solid-state, quantum computers that operate at room-temperature is a challenging task. This is because stored information is readily destroyed by thermal noise. A signature of a physical system’s ability to function as a quantum computer is the observation of quantum Rabi oscillations since they represent the possibility of “reading” and “writing” quantum information. Thus far, these have been observed reliably and consistently only at milli-Kelvin temperatures. Jonathan Breeze at Imperial College London and collaborators have used an organic molecular crystal, a dielectric resonator and pulses of laser light to produce pronounced quantum Rabi oscillations at microwave frequencies, lasting up to 10 microseconds at room-temperature. This discovery paves the way for room-temperature quantum information processing devices such as spin memories and quantum-enhanced technologies for metrology, sensing, communications and ultimately – quantum computing

Room-temperature cavity quantum electrodynamics with strongly-coupled Dicke states

The strong coupling regime is essential for efficient transfer of excitations between states in different quantum systems on timescales shorter than their lifetimes. The coupling of single spins to microwave photons is very weak but can be enhanced by increasing the local density of states by reducing the magnetic mode volume of the cavity. In practice, it is difficult to achieve both small cavity mode volume and low cavity decay rate, so superconducting metals are often employed at cryogenic temperatures. For an ensembles of N spins, the spin–photon coupling can be enhanced by N−−√N through collective spin excitations known as Dicke states. For sufficiently large N the collective spin–photon coupling can exceed both the spin decoherence and cavity decay rates, making the strong-coupling regime accessible. Here we demonstrate strong coupling and cavity quantum electrodynamics in a solid-state system at room-temperature. We generate an inverted spin-ensemble with N ~ 1015 by photo-exciting pentacene molecules into spin-triplet states with spin dephasing time T∗2~3T2*~3 μs. When coupled to a 1.45 GHz TE01δ mode supported by a high Purcell factor strontium titanate dielectric cavity (Vm~0.25Vm~0.25 cm3, Q ~ 8,500), we observe Rabi oscillations in the microwave emission from collective Dicke states and a 1.8 MHz normal-mode splitting of the resultant collective spin–photon polariton. We also observe a cavity protection effect at the onset of the strong-coupling regime which decreases the polariton decay rate as the collective coupling increases

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control

BackgroundControlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models.MethodsPerformances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time.ResultsResults in both the linear and the artificial neural network models demonstrated that Netlab’s implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec.ConclusionsIt is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).