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Like other disciplines, machine learning is currently facing a reproducibility crisis that hinders the advancement of scientific research. Researchers face difficulties reproducing key results due to the lack of critical details, including the disconnection between publications and associated models, data, parameter settings, and experimental results. To promote transparency and trust in research, solutions that improve the accessibility of models and data, facilitate experiment tracking, and allow audit of experimental results are needed. Blockchain technology, characterized by its decentralization, data immutability, cryptographic hash functions, consensus algorithms, robust security measures, access control mechanisms, and innovative smart contracts, offers a compelling pathway for the development of such solutions. To address the reproducibility challenges in machine learning, we present a novel concept of a blockchain-based platform that operates on a peer-to-peer network. This network comprises organizations and researchers actively engaged in machine learning research, seamlessly integrating various machine learning research and development frameworks. To validate the viability of our proposed concept, we implemented a blockchain network using the Hyperledger Fabric infrastructure and conducted experimental simulations in several scenarios to thoroughly evaluate its effectiveness. By fostering transparency and facilitating collaboration, our proposed platform has the potential to significantly improve reproducible research in machine learning and can be adapted to other domains within artificial intelligence. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the potential of quantum computing for enhancing the security and efficiency of blockchain technology. While quantum blockchain protocols offer improved security over their classical counterparts, implementing such protocols on present-day quantum computers poses difficulties due to the limited number of qubits and quantum gates and the significant effects of noise. In this paper, we propose a set of improvements for implementing a quantum blockchain protocol based on hypergraphs that aim to reduce the required resources and operations and increase noise tolerance. Specifically, we focus on enhancing the state-of-the-art quantum circuits that underpin the quantum blockchain by optimizing the so-called T-count and T-depth, which represent the number of quantum gates and the circuit depth, respectively. Our proposed implementations also leverage proven error detection and correction codes to improve noise tolerance. To evaluate the effectiveness of our proposed improvements, we tested them on real quantum devices. Our results demonstrate a significant reduction in the T-count and T-depth. Overall, our proposed improvements provide a promising direction for the practical implementation of quantum blockchain protocols on current quantum computers and lay a foundation for further research in this area. [ABSTRACT FROM AUTHOR]

Blockchain is one of the most disruptive technologies introduced in Bitcoin, which engaged great attention from the industry and academia and determined a rapid growth of other Distributed Ledger Technologies (DLTs). In the complex architecture of a DLT system, a consensus protocol plays a key role by ensuring that all participants agree on the data integrity without any central authority. A wide range of consensus protocols have been designed with different concepts and properties (e.g., lower energy consumption, better scalability, smaller latency, higher throughput, etc.). The key requirements for consensus protocols passing from one blockchain system to another often differ significantly, and there is no one-fit-all protocol. Therefore, selecting the most suitable consensus protocol for a particular DLT system is essential, but at the same time a challenging step, as decision-makers need to make a trade-off between conflicting requirements. This paper introduces a framework for selecting the most suitable consensus protocols depending on the identified criteria, priorities, and other requirements by incorporating Multi-Criteria Decision-Making (MCDM) techniques. We demonstrate its potential by identifying the preferable consensuses for the three most common types of existing blockchain systems and on an actual application for bike renting. Moreover, the collected data and tools are freely available, ensuring full replicability, reusability, and further development. • A comprehensive literature review dedicated to selecting consensus protocols. • A new open-source data collection reflecting 18 state-of-the-art consensus protocols. • The first MCDM-based framework to identify preferable consensus protocols. • Demonstration on three main types of blockchains and bike renting application. [ABSTRACT FROM AUTHOR]