This article provides a summary of the operation and activities of the IEEE Systems, Man and Cybernetics Society (SMCS) Technical Committee on Quantum Cybernetics (TC-qCyber) since its inception on 30 March 2017.
The 2nd Workshop on Quantum in Consumer Technology, held during the IEEE Quantum Week 2023, continued to explore the integration and application of quantum technologies in consumer electronics. Organized by the Quantum in Consumer Technology Technical Committee (QCT TC) of the IEEE Consumer Technology Society (CTSoc), this year's workshop featured two insightful panels discussing the cutting-edge advancements and applications of quantum technologies that aim to revolutionize consumer products and services.
Abstract: This document defines a new technology class: Information-Based Matter Regulation. While classical high-energy physics relies on kinetic collision to observe subatomic structures, this framework presents a low-energy, cybernetic approach to program matter at the source. We present the theoretical model of the atom as a "cybernetic organism", controlled by an internal "Quark-Gluon-Exchange" (QGE) processor and a "photon metabolism". Complementing this, the complete technical specification for an interface (TL-001) is disclosed. This device utilizes entangled operator logic, superconducting qubit chains, and synchronized resonance fields to modulate atomic states directly or indirectly. The framework demonstrates the theoretical consistency and technical feasibility of this approach, thereby establishing the state of the art for the systematic programming of matter. Status: Based on patent application DE 10 2025 001 110.5 (Priority: March 25, 2025) by Thorsten Loew. Classified as a "new, unpublished scientific theory and new field of research". Patentclassification G06N10/00 Quantumphenomena Keywords: Programmable Matter, Quark-Gluon-Exchange (QGE), Atomcode, Quantum Control, Cybernetic Atom Model, Low-Energy Transmutation, Information Physics.
Quantum reinforcement learning has been systematically presented in a recent paper [Dong et al, Quantum reinforcement learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 38, No. 5, pp.1207–1220, 2008], and such results have shown that quantum reinforcement learning is an effective approach for the solutions to some complex problems. The purpose of this paper is to analyze the complexity of quantum reinforcement learning. In particular, storage complexity and exploration complexity are defined and a collection of results are presented to demonstrate such complexities by several simple examples.