Quantum machine intelligence

Abstract

Quantum computing is a fascinating research area at the intersection of computer science, physics, and engineering, which is catching the attention of both the academic and corporate worlds by promising a revolution in computing performance, due to a massive and intrinsic parallelism enabled by "interfering, super-positioning, and entangling" different pieces of information. Although it was initially thought of as a way to efficiently simulate quantum mechanics on a computer, today, research on quantum computing is focusing on the so-called quantum advantage or quantum supremacy-the design of quantum algorithms offering significant speedup compared to the best possible algorithm on a classical computer-to spur the development of new breakthroughs in different application domains, such as chemistry, medicine, and financial services, just to name a few. This research is particularly significant because the quantum computation is no longer a theoretical utopia since, currently, real quantum computers can be accessed and programmed through an Internet connection, and everyone can try their hand at implementing wellestablished quantum algorithms, such as Shor's and Grover's algorithms (Shor 1994; Grover 1996), or designing completely new quantum algorithms. In this pioneering scenario, the recent implementation of quantum algorithms for machine learning has led to a flurry of increasingly sophisticated results that show how quantum computers could be efficient in solving problems in the field of artificial intelligence faster than their classical counterparts (

Survey of Promising Technologies for Quantum Drones and Networks

“…Quantum computing is considered a research area that integrates knowledge from engineering, physics, and computer science [128]. While Paul Benioff published the principles of quantum information in 1980, it is feasible to consider that the development of quantum computing began with Richard Feynman's works promoting non-classical physics studies [129].…”

“…Quantum information processing opens up new possibilities in computing, communications, and networking [130]. Timely developments of quantum algorithms in the area of ML have led to sophisticated results demonstrating how quantum computers can be applied to solve issues of AI faster than classical methods [128]. These recent developments open new research fields integrating these disruptive technologies and inaugurate the field study on QML and QAI.…”

“…In this case, the primary benefits are related to the unprecedented increase in processing dimensions. Typical potential realistic applications of ML algorithms included the development of quantum algorithms, information processing algorithms, circuits, devices, and materials [128]. Results from the literature present a cohesive framework on advances of QML in information processing.…”

“…decisive technology for the next developments of computational devices based on quantum technologies [128]. Recently, Dunjko and Briegel [153] highlight the fundamental principles and recent progress in a wide range of research on ML and AI in the computational quantum field.…”

Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

“…It is apparent to be mentioned that the success of leveraging deep learning over traditional machine learning methods have been studied along with wide area of application in the medical domain [11]. Moreover, recent developments of quantum computing, vis a vis its application of quantum algorithm in varied domains, has now opened up new research areas for further optimizing classical machine learning problems [12]. In fact, recently, researchers from Massachusetts Institute of Technology (MIT) created an algorithm to overcome the challenges of developing computationally efficient and performing algorithms in order to solve several medical imaging problems [13].…”