Quantum information science and technology

Michael Kratsios explains how the White House coordinates technology policy across federal agencies and argues that regulation should keep pace with fast-moving technologies. He describes the administration’s support for open source AI, a national framework to reduce compliance burdens for startups, and efforts to remove barriers for technologies such as commercial drones and supersonic flight. He discusses risks from cyber-capable AI and biological misuse, while emphasizing the need for testing and evaluation. He also outlines how AI could transform scientific research through autonomous laboratories and identifies quantum information science as a priority for the coming years.

Satya Nadella describes AI as a tool and a new platform built on cloud infrastructure, with value measured by the economic and social benefits it creates. He argues that useful AI products need more than a model: they need application layers, tools, memory, entitlements, and feedback loops that connect models to real work. He sees organizational change and workflow redesign as major barriers to adoption, while AI could reduce routine work in fields such as healthcare, education, and software engineering. He also discusses Microsoft’s Copilot, the future role of software engineers, and the company’s quantum computing research.

Leonard Susskind describes how apparent conflicts in physics led him to work on quark confinement, string theory, and the holographic principle. He explains how the holographic principle connects black hole information to the universe’s boundary, and discusses the proposed relationship between quantum entanglement and Einstein-Rosen bridges. He surveys open problems including quantum gravity, dark energy, and quantum complexity, while distinguishing scientific curiosity from speculation without explanatory or predictive value. He also reflects on Richard Feynman’s approach to philosophy and on why teaching physics to the public helps him clarify his own ideas.

John Preskill traces quantum computing from Feynman’s proposal to simulate nature with quantum systems through quantum error correction and today’s hardware approaches. He explains entanglement, interference, and Grover’s search algorithm, emphasizing that quantum computers do not simply perform many readable calculations at once. He discusses likely applications in materials and chemistry, the risks to current public-key cryptography, and quantum communication and sensing. He also describes open challenges in scaling hardware, the uncertain prospects of competing technologies, and how quantum information may inform basic physics and education.