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Quantum technologies in general and quantum computing (QC) in particular is predicted to be one
of the most disruptive among the emerging technologies. Therefore translation of this technology is
happening at a neck break speed. As an example, small scale quantum computers are now available
over the cloud as a software as a service (SaaS) offered by leading corporates as well as startups.
Even though the current backend quantum hardware is too noisy to yield any useful quantum
advantage, they are the best playground to explore the potentials of a quantum computer of the
future. On the other hand, classical machine learning is taking over the mundane tasks of learning
and comprehending human languages like never before. However, as the complexity of the systems
to learn increases, classical hardware is finding it challenging to cope with the energy and computing
resource requirements.
Here, we will discuss, how a marriage between these two domains can exploit the seamless
potential of exploring a large Hilbert space. We use an ion trap-based quantum-classical hybrid
computer to implement quantum classifier. In addition, we also demonstrated more recently, the
vulnerability of such system to external cyber-attacks. |