Abstract
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. 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 simpler and optimized emulation architectures than what has been previously reported, to perform quantum wavelet transform on high-resolution data. We also implement the inverse quantum wavelet transform (IQWT) to accurately reconstruct the data without any losses. The algorithms are prototyped on an FPGA-based quantum emulator that supports double-precision floating-point computations. Experimental work has been performed using high-resolution image data on a state-of-the-art multinode high-performance reconfigurable computer. The 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.
Highlights
High-energy physics deal with advanced instruments such as particle accelerators and detectors. ese machines use electromagnetic fields to accelerate charged particles to high speeds and create collisions
By studying particle collisions and tracking collision trajectories, physicists can test the predictions of many theories of particle physics such as properties of the Higgs boson [1], discovering new particle families [2] as well as many high-energy physics problems [3]. ere are a number of high-energy physics (HEP) research centers [4]. e largest particle accelerator is the Large Hadron Collider (LHC) in Geneva, Switzerland
Large-scale general-purpose particle detectors have been developed at the LHC. e ATLAS [5] and Compact Muon Solenoid (CMS) [6] are two examples which are used for studying the properties of the Higgs boson and investigating new physics. e ATLAS has an inner detector that has been used to observe the decay products of collisions. e pixel detector [7] is one of the main components of the inner detector, having over 80 million readout channels [8], which contribute to half the total readout channels of the entire experiment
Summary
High-energy physics deal with advanced instruments such as particle accelerators and detectors. ese machines use electromagnetic fields to accelerate charged particles to high speeds and create collisions. As a feasible solution to this problem, we here propose combining wavelet-based dimension reduction techniques [13,14,15] with quantum information processing (QIP) [16] for applications in domains that generate high-dimensional data volumes such as high-energy physics (HEP). We propose using quantum wavelet transform (QWT) to reduce the dimensionality and high spatial resolution of data in HEP particle tracking. The large volume of data from domains such as high-energy particle physics, present a challenge for a classical wavelet-based method. Erefore, applying QIP techniques such as QWT for dimension reduction of HEP data will bring substantial improvements in storage and computation compared to classical signal processing techniques.
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