Quantum machine intelligence
- PDF / 311,320 Bytes
- 3 Pages / 595.224 x 790.955 pts Page_size
- 34 Downloads / 290 Views
EDITORIAL
Quantum machine intelligence Launching the first journal in the area of quantum artificial intelligence Giovanni Acampora1 Published online: 23 May 2019 © Springer Nature Switzerland AG 2019
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 (Biamonte et al. 2017; Schuld et al. 2014; Dunjko and Briegel 2018; Schuld and Petruccione 2018). The big advantage of quantum computing applied
Giovanni Acampora
[email protected] 1
Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy
to machine learning is due to the exponential increase in the number of dimensions it can process with respect to classical machine learning algorithms (Havl´ıcek et al. 2019). As an example, while a classical artificial neuron can process an input of N dimensions, a quantum perceptron can process 2N dimensions, and it can tremendously speed up the running time of both training and classification algorithms (Tacchino et al. 2019). Moreover, from a different point of view, particularly exciting is the prospect of using classical algorithms of machine learning for the discovery and design of quantum materials, devices, algorithms, and circuits. In this context, the current skills and expertise gained in classical machine learning will enable a rapid growth in developing new approaches for the automatic design of “quantum things.” As a consequence, the scientific community agrees that artificial intelligence could be a killer application for future generations of computational devices based o
Data Loading...
