The cryptocurrency application Bitcoin, proposed back in 2009, availed the potential of Blockchain (BC) as a promising technology for hundreds of foundational applications, including Smart Systems, IoT, e-Health, supply chain, digital identity. One of the solid properties of BC is its high security guarantees in classical fully distributed systems, which can be challenged by Machine Learning (ML) solutions. However, the recent advances in the field of Quantum Computing (QC), derived basically from the concepts of quantum mechanics revealed several security risks on BC-based solutions. Many proposals attempted to address these risks by improving BC methods, including infrastructure, data structure, cryptography, networking, etc. targeting the so-called post-quantum blockchains. On the other hand, QC can also be used for the optimization of BC methods and configurations, leading to quantum blockchain principles that can see the light once quantum computers are commercialized. The aim of this research topic is to study and investigate ML and QC aspects and potential for strengthening, attacking, and protecting BC-based solutions. The successful candidate needs a strong background in distributed computing, cryptography, algorithms and probability theory.
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