We demonstrate a new mechanical transduction platform for individual spin qubits. In our approach, single micro-magnets are trapped using a type-II superconductor in proximity of spin qubits, enabling direct magnetic coupling between the two systems. Controlling the distance between the magnet and the superconductor during cooldown, we demonstrate three dimensional trapping with quality factors around one million and kHz trapping frequencies. We further exploit the large magnetic moment to mass ratio of this mechanical oscillator to couple its motion to the spin degree of freedom of an individual nitrogen vacancy center in diamond. Our approach provides a new path towards interfacing individual spin qubits with mechanical motion for testing quantum mechanics with mesoscopic objects, realization of quantum networks, and ultra-sensitive metrology.

Atomic comagnetometers are widely used in precision measurements searching for spin interactions beyond the standard model. We describe a new (3)He-(129)Xe comagnetometer probed by Rb atoms and use it to identify two general classes of systematic effects in gas comagnetometers, one associated with diffusion in second-order magnetic-field gradients and another due to temperature gradients. We also develop and confirm experimentally a general and practical approach for calculating spin relaxation and frequency shifts due to arbitrary magnetic-field gradients.

The Paul Trap Simulator Experiment (PTSX) is a compact laboratory experiment that places the physicist in the frame-of-reference of a long, charged-particle bunch coasting through a kilometers-long magnetic alternating-gradient (AG) transport system. The transverse dynamics of particles in both systems are described by the same set of equations, including nonlinear space-charge effects. The time-dependent voltages applied to the PTSX quadrupole electrodes in the laboratory frame are equivalent to the spatially periodic magnetic fields applied in the AG system. The transverse emittance of the charge bunch, which is a measure of the area in the transverse phase space that the beam distribution occupies, is an important metric of beam quality. Maintaining low emittance is an important goal when defining AG system tolerances and when designing AG systems to perform beam manipulations such as transverse beam compression. Results are reviewed from experiments in which white noise and colored noise of various amplitudes and durations have been applied to the PTSX electrodes. This noise is observed to drive continuous emittance growth and increase in root-mean-square beam radius over hundreds of lattice periods. Additional results are reviewed from experiments that determine the conditions necessary to adiabatically reduce the charge bunch’s transverse size and simultaneously maintain high beam quality. During adiabatic transitions, there is no change in the transverse emittance. The transverse compression can be achieved either by a gradual change in the PTSX voltage waveform amplitude or frequency. Results are presented from experiments in which low emittance is achieved by using focusing-off-defocusing-off waveforms.

The continued emergence of large social network applications has introduced a scale of data and query volume that challenges the limits of existing data stores. However, few benchmarks accurately simulate these request patterns, leaving researchers in short supply of tools to evaluate and improve upon these systems. In this paper, we present a new benchmark, TAOBench, that captures the social graph workload at Meta. We open source workload configurations along with a benchmark that leverages these request features to both accurately model production workloads and generate emergent application behavior. We ensure the integrity of TAOBench's workloads by validating them against their production counterparts. We also describe several benchmark use cases at Meta and report results for five popular distributed database systems to demonstrate the benefits of using TAOBench to evaluate system tradeoffs as well as identify and address performance issues. Our benchmark fills a gap in the available tools and data that researchers and developers have to inform system design decisions.