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Evaluation of quantum image processing in CUDA-based simulation

Akira Hasegawa, Yoshihiro Maede, Norishige Fukushima

Published May 28, 2024• 3 citations
Publisher

Abstract

Quantum computers are expected to be the next-generation computers in various fields. Quantum computers are different from classical computers, which store the current binary information of 0 and 1. Quantum computers can use quantum superposition, which can hold 0 to 1 probability as continuous quantities in one qubit. Therefore, existing algorithms are not necessarily efficient computational algorithms on quantum computers. GPU-based quantum simulations, such as cuQuantum, have recently been released to develop quantum algorithms. This paper focuses on quantum image processing, presents how far quantum image processing can be efficiently described today and verifies on GPUs that multiple image processing can be described using cuQuantum.

DOI
10.1117/12.3022770

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Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log(2N) quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement.
As the advent of quantum computers comes closer, research on quantum algorithms is actively being conducted in various fields. Quantum computer simulation is mainly employed for the study of quantum algorithms due to the imperfections of quantum computers. Quantum computer simulation using classical computers has encountered significant challenges in emulating quantum algorithms, where computational time and memory usage increase exponentially due to superposition and entanglement. To resolve these problems, there have been many studies to exploit GPU-based parallelism in quantum computer simulation. In this review paper, we present various studies on implementing quantum computer simulations using single or multiple GPUs. We also analyze the performance of these studies in terms of speedup and summarize the simulation methods.
Image processing plays a crucial role in our day to day life as it is helpful to solve important problems in real world. Recent progress in quantum information and computation led to the birth of a new field called Quantum Image Processing. This new field of image processing in quantum domain focuses on the development of the quantum image processing algorithms, which can run more efficiently on quantum computer. These quantum algorithms are better in terms of Image storage and retrieval with reduced computational complexity. In this article we present recent advancements in quantum image representation with different mathematical models and with their corresponding computational complexity.
Quantum computer simulators are crucial for the development of quantum computing. In this work, we investigate the suitability and performance impact of GPU and multi-GPU systems on a widely used simulation tool - the state vector simulator Qiskit Aer. In particular, we evaluate the performance of both Qiskit's default Nvidia Thrust backend and the recent Nvidia cuQuantum backend on Nvidia A100 GPUs. We provide a benchmark suite of representative quantum applications for characterization. For simulations with a large number of qubits, the two GPU backends can provide up to 14x speedup over the CPU backend, with Nvidia cuQuantum providing further 1.5-3x speedup over the default Thrust backend. Our evaluation on a single GPU identifies the most important functions in Nvidia Thrust and cuQuantum for different quantum applications and their compute and memory bottlenecks. We also evaluate the gate fusion and cache-blocking optimizations on different quantum applications. Finally, we evaluate large-number qubit quantum applications on multi-GPU and identify data movement between host and GPU as the limiting factor for the performance.
Image processing via the quantum platform is an emerging area for researchers. Researchers are more interested to move on towards quantum image processing instead of classical image processing. This chapter starts with the review of different quantum image computing-based research papers with a brief idea of the ethics which inspire quantum computing in the background and focus on the current scenario of recent trends of quantum image representation, pitfalls, and summarization of the pros and cons of it, with the limitations of the technologies used and focus on the recent work to be going on and application of it in a different field. In the next, it will focus on the different methods used by the researcher in the previous papers. The next section discussed the different methods based on quantum image representation used. Some different techniques of image storage, retrieval, and representation in a quantum system are discussed. Also, this chapter briefs the pros and cons of using different techniques in quantum systems in comparison to classical systems.