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Register a new userA quantum spin transducer based on nano electro-mechancial resonator arrays
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Implementation of quantum information processing faces the contradicting requirements of combining excellent isolation to avoid decoherence with the ability to control coherent interactions in a many-body quantum system. For example, spin degrees of freedom of electrons and nuclei provide a good quantum memory due to their weak magnetic interactions with the environment. However, for the same reason it is difficult to achieve controlled entanglement of spins over distances larger than tens of nanometers. Here we propose a universal realization of a quantum data bus for electronic spin qubits where spins are coupled to the motion of magnetized mechanical resonators via magnetic field gradients. Provided that the mechanical system is charged, the magnetic moments associated with spin qubits can be effectively amplified to enable a coherent spin-spin coupling over long distances via Coulomb forces. Our approach is applicable to a wide class of electronic spin qubits which can be localized near the magnetized tips and can be used for the implementation of hybrid quantum computing architectures.
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We give a quantum master equation description of the measurement scheme based on a coplanar microwave cavity capacitively coupled to nano mechanical resonator. The system exhibits a rich bifurcation structure that is analogous to sub/second harmonic generation in nonlinear optics. We show how it may be configured as a bifurcation amplifier transducer for weak force detection.
Microfludic channels are now a well established platform for many purposes, including bio-medical research and Lab on a Chip applications. However, the nature of flow within these channels is still unclear. There is evidence that the mean drift velocity in these channels deviates from the regular Navier-Stokes solution with `no slip boundary conditions. Understanding these effects, is not only of value for fundamental fluid mechanics interest, but it also has practical importance for the future development of microfluidic and nanofludic infrastructures. We propose a nano-NMR based setup for measuring the drift velocity near the surface of a microfludic channel in a non-intrusive fashion. We discuss different possible protocols, and provide a detailed analysis of the measurements sensitivity in each case. We show that the nano-NMR scheme outperforms current fluorescence based techniques.
Modern neuroscience is currently limited in its capacity to perform long term, wide-field measurements of neuron electromagnetics with nanoscale resolution. Quantum microscopy using the nitrogen vacancy centre (NV) can provide a potential solution to this problem with electric and magnetic field sensing at nano-scale resolution and good biocompatibility. However, the performance of existing NV sensing technology does not allow for studies of small mammalian neurons yet. In this paper, we propose a solution to this problem by engineering NV quantum sensors in diamond nanopillar arrays. The pillars improve light collection efficiency by guiding excitation/emission light, which improves sensitivity. More importantly, they also improve the size of the signal at the NV by removing screening charges as well as coordinating the neuron growth to the tips of the pillars where the NV is located. Here, we provide a growth study to demonstrate coordinated neuron growth as well as the first simulation of nano-scopic neuron electric and magnetic fields to assess the enhancement provided by the nanopillar geometry.
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing big data could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] was proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of 2-, 4-, and 8-dimensional vectors to different clusters using a small-scale photonic quantum computer, which is then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can in principle be scaled to a larger number of qubits, and may provide a new route to accelerate machine learning.
The resonance frequency of an InAs quantum dot strongly coupled to a GaAs photonic crystal cavity was electrically controlled via quantum confined Stark effect. Stark shifts up to 0.3meV were achieved using a lateral Schottky electrode that created a local depletion region at the location of the quantum dot. We report switching of a probe laser coherently coupled to the cavity up to speeds as high as 150MHz, limited by the RC constant of the transmission line. The coupling rate and the magnitude of the Stark shift with electric field were investigated while coherently probing the system.
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