What is it about?
Dimension reduction is a technique to reduce the size of data in large-scale applications. In this work we investigate dimension reduction using a quantum algorithm, the quantum Wavelet transform. The quantum wavelet transform takes advantage of the principles of quantum mechanics to achieve reductions in computation time while processing exponentially larger amount of information. We develop emulation hardware architectures to demonstrate and evaluate our proposed methodology. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. Experimental results show that the proposed concepts represent a feasible approach to reducing dimensionality of high spatial resolution data generated by applications such as particle tracking in high-energy physics.
Photo by david latorre romero on Unsplash
Why is it important?
The high resolution of multidimensional space-time measurements and enormity of data readout counts in applications such as particle tracking in high-energy physics (HEP) is becoming nowadays a major challenge. In this work, we propose combining dimension reduction techniques with quantum information processing for application in domains that generate large volumes of data such as HEP. More specifically, we propose using quantum wavelet transform (QWT) to reduce the dimensionality of high spatial resolution data.
Read the Original
This page is a summary of: Dimension Reduction Using Quantum Wavelet Transform on a High-Performance Reconfigurable Computer, International Journal of Reconfigurable Computing, November 2019, Hindawi Publishing Corporation, DOI: 10.1155/2019/1949121.
You can read the full text:
The following have contributed to this page