Techsplainers by IBM

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This episode of Techsplainers delves into the intricate workings of quantum-centric supercomputing hardware and architecture. Host Ian Smalley explains the quantum processing unit (QPU) at the heart of these systems, describing how superconducting qubits function through Josephson junctions and require temperatures colder than space to maintain their quantum states. The episode outlines the three-phase evolution of quantum-centric supercomputing: from specialized compute engines within existing systems, to tightly coupled resources through advanced middleware, to fully co-designed quantum-HPC systems. Listeners will learn about key challenges facing this technology, including error correction approaches, scaling quantum processors through next-generation interconnects, and algorithm discovery. The discussion also covers IBM's ambitious roadmap toward systems with 2,000 logical qubits by 2033, providing insight into how this revolutionary computing paradigm will mature in the coming years.

Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing
Find more episodes at https://www.ibm.biz/techsplainers-podcast 

Narrated by Ian Smalley

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This episode of Techsplainers introduces quantum-centric supercomputing, a revolutionary approach that combines quantum computing with traditional high-performance computing to create powerful integrated systems. Host Ian Smalley explains how these systems leverage the unique properties of qubits—including superposition, entanglement, interference, and decoherence—to potentially solve complex problems exponentially faster than classical computers alone. The episode covers the fundamental differences between classical and quantum computing, explores potential applications in pharmaceuticals, chemistry, and machine learning, and clarifies that quantum computing will complement rather than replace classical computing. Listeners will gain insight into this cutting-edge technology that IBM predicts will enable major breakthroughs in simulation, optimization, and solving challenging mathematical equations across multiple industries.

Find more information at www.ibm.com/think/topics/quantum-centric-supercomputing

Find more episodes at https://www.ibm.biz/techsplainers-podcast 

Narrated by Ian Smalley

Explore Think 2026: https://www.ibm.biz/think2026event

This episode of Techsplainers introduces OpenRAG, IBM's open-source framework that connects large language models to enterprise data sources. We explore how OpenRAG builds bridges between powerful AI and organizational knowledge through Retrieval-Augmented Generation (RAG), enabling AI systems to ground their responses in actual company information rather than relying solely on training data. The discussion covers OpenRAG's flexible deployment options—from fully self-hosted architectures to hybrid cloud implementations—and highlights its modular design that allows organizations to customize components based on their specific needs. We examine real-world applications including enterprise knowledge assistants, customer support automation, regulatory compliance tools, research document analysis, data exploration interfaces, and collaborative knowledge systems. The episode concludes with practical guidance on getting started with OpenRAG, emphasizing its accessibility for both experimentation and enterprise-scale deployment.

Find more information at https://www.ibm.com/think/topics/openrag

Find more episodes at https://www.ibm.biz/techsplainers-podcast

Narrated by Matt Finio

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This episode of Techsplainers explores data retrieval, the essential process of accessing information from various data sources. We examine how this field has evolved beyond simple database queries to encompass complex AI-driven techniques. The discussion covers traditional approaches like SQL and indexing alongside modern methods including vector search, natural language processing, and retrieval augmented generation (RAG). We highlight how agentic RAG elevates retrieval capabilities through intelligent decision-making components like semantic caching, routing agents, and query planning. Real-world examples demonstrate impressive efficiency gains across healthcare, financial services, and e-commerce, while we also address challenges including data quality, security concerns, and vendor lock-in. As organizations manage ever-expanding data volumes and AI workloads, sophisticated data retrieval becomes increasingly critical to business success.

Find more information at https://www.ibm.com/think/topics/data-retrieval

Find more episodes at https://www.ibm.biz/techsplainers-podcast

Narrated by Matt Finio

Explore Think 2026: https://www.ibm.biz/think2026event

This episode of *Techsplainers* explores multi-agent collaboration, where multiple AI agents work together as a coordinated team to accomplish complex tasks. We explain how these systems have evolved beyond traditional LLMs to create autonomous workflows for research, support, analysis, and operations. The discussion covers key collaboration models including rule-based, role-based, and model-based approaches, and examines leading frameworks like IBM's Bee Agent, LangChain, and OpenAI's Swarm. We also highlight Watsonx Orchestrate as an enterprise solution for orchestrating AI-enabled workflows through interconnected components. Throughout the episode, we use the analogy of drone teams searching disaster sites to illustrate how independent agents can coordinate effectively without centralized control to tackle complex challenges that would overwhelm a single agent.

Find more information at https://www.ibm.com/think/topics/multi-agent-collaboration
Find more episodes at https://www.ibm.biz/techsplainers-podcast

Narrated by Matt Finio