Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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The convergence of quantum computing and AI marks a transformative leap for business, redefining how organizations process information, generate insights, and innovate. This fusion unlocks scalable efficiency, sustainable computing, and advanced analytics — enabling real-time decision-making, deeper customer understanding, and accelerated R&D. As these technologies mature, they promise to reshape business capabilities across industries.
This Advisor distills key lessons from the Syn.ikia project’s implementation of digital twins in Uden, the Netherlands—an EU-funded initiative focused on positive-energy districts. It explores how predictive digital twins, combining building simulations with AI-driven user behavior models, can optimize renewable energy use. It also emphasizes the importance of ethical data management, user empowerment through intuitive design, and value chain collaboration to ensure digital twins enhance sustainability without alienating end users.
Despite AI’s transformative potential, over 80% of AI projects fail — double the rate of traditional IT initiatives. As this Advisor points out, key pitfalls include unclear objectives, poor data quality, inadequate infrastructure, and misaligned expectations. To reverse this trend, organizations must align AI capabilities with real-world needs, invest in robust systems, and build multidisciplinary expertise. Establishing clear metrics and knowing when to pause or pivot projects are also critical for long-term success.
Giuseppe Bisicchia, José Garcia-Alonso, Juan Murillo, and Antonio Brogi lay the historical and theoretical groundwork for understanding quantum software engineering (QSE) as a discipline, tracing its origins to Richard Feynman’s call for quantum simulation and following the evolution of quantum algorithms from Peter Shor’s and Lov Grover’s breakthroughs to today’s hybrid implementations. The article argues that QSE must strike a balance between importing proven classical software engineering practices and cultivating quantum-specific innovations.
Joseph Byrum examines the transformative intersection of quantum computing and AI, contending that the convergence is not merely technological. He explores five innovation vectors — from quantum-enhanced attention mechanisms and quantum compression techniques to AI-augmented quantum circuit design — demonstrating how each could dramatically reshape computation, knowledge processing, and enterprise workflows. Beyond technical sophistication, the article proposes a human-centric philosophy of computation that emphasizes integration, uncertainty as a resource, and ethical design.
Michal Baczyk delves into the pressing need for architectural rigor in quantum software development. As enterprise adoption looms, Baczyk proposes a three-layer taxonomy of patterns (design, algorithmic, and architectural) intended to address the complexity of hybrid quantum-classical systems. The article offers both a conceptual roadmap and a pragmatic toolkit for organizations seeking to build scalable, maintainable quantum systems.
Guido Peterssen and José Luis Hevia focus on the operational and organizational dimensions of quantum computing. They provide a compelling call to action: without robust governance, quantum computing projects will likely spiral into unmanageable complexity. Through a detailed case study of Bizkaia Quantum Advanced Industries (BIQAIN), the authors introduce the concept of the private quantum hub as a model for resource coordination, lifecycle management, and cost control across distributed quantum infrastructures.
This issue of Amplify explores the emerging discipline of quantum software engineering (QSE), highlighting the paradigm shift required to build, manage, and govern robust quantum software systems. Through expert contributions, it addresses foundational theory, architectural innovation, hybrid classical-quantum system design, operational governance, and the convergence of quantum computing with AI.