mising direction.

ntum computing for biological pattern analysis

computing started in the 1980s and it implements computations

g quantum mechanics such as superposition and entanglement.

led to the development of the quantum computers. The research

um computing has led to the development of quantum machines

he quantum neural networks [Benioff, 1980; Feynman, 1982;

al., 2010; Reiher, et al., 2017].

tum computing has been implemented for machine learning in

ars. The basic component of machine learning is an information

which incorporates noise data together with a properly designed

tion. Unsupervised machine learning can be implemented by

ng a novel entangled quantum gates of the quantum bus in a

computer [Gyongyosi, 2020]. The work was introduced based on

vation that the quantum gate oscillates in a random mode which

to data noise. Quantum computing has been implemented as a

sed support vector machine. The support vector machine

m has a limitation when a feature space is too large. The quantum

g can implement the quantum-based support vector machines to

learning, where the novel idea is to use a smaller quantum space

e a kernel space [Vojtech, et al., 2019].

tum computing, as a novel pattern recognition approach, has been

o biology pattern discovery and recognition because of the

y huge computing power of the quantum computers. For instance,

um sequence pairwise alignment has been developed [Prousalis

ofaos, 2019]. It has been recognised that generating a dot-plot is

ionally very expensive. The quantum sequence alignment can

much faster. Other quantum computing applications for sequence

y alignment include the genetic algorithm enhanced quantum

g [Huo, et al., 2008]. Quantum computing can also be used for

e-genome de novo sequence assembly problem, which is one of

computational time costing process with many uncertainties