SearcharxivSearch

arXiv subjects

John McAllister

Publications and source records attributed to John McAllister.

2 recordsLinked to original sources

Efficient Parallel Compilation and Profiling of Quantum Circuits at Large Scales

Compiling quantum circuits is a major bottleneck in quantum computing, and given the scale required in a few years, is likely to become infeasibly long. Techniques to reduce compilation time for quantum circuits are sorely needed. Furthermore, resources to test acceleration techniques are similarly lacking due to the limited scale of circuits in benchmark suites and mismatches in characteristics of these circuits and those produced by random circuit generators. This paper resolves the latter of these problems by describing a random circuit generator which allows control of circuit density, width and depth parameters. This is used to derive 8000 experimental large-scale circuits and test a novel approach to compiler parallelisation. This separates a circuit into sub-circuits which are compiled in parallel and recombined to produce a compiled circuit. When the parallel approach was tested using Qiskit, a peak speedup of 15.56 was achieved with corresponding overheads of less than 1%.

cs.DC

EMG wrist-hand motion recognition system for real-time Embedded platform

Electromyography (EMG) signal analysis is a popular method for controlling prosthetic and gesture control equipment. For portable systems, such as prosthetic limbs, real-time low-power operation on embedded processors is critical, but to date, there has been no record of how existing EMG analysis approaches support such deployments. This paper presents a novel approach to time-domain classification of multi-channel EMG signals harnessed from randomly-placed sensors according to the wrist-hand movements which caused their occurrence. It shows how, by employing a very small set of time-domain features, Kernel Fisher discriminant feature projection and Radial Bias Function neural network classifiers, nine wrist-hand movements can be detected with accuracy exceeding 99% - surpassing the state-of-the-art on record. It also shows how, when deployed on ARM Cortex-A53, the processing time is not only sufficient to enable real-time processing but is also a factor 50 shorter than the leading time-frequency techniques on record.

eess.SP