The current paradigm of quantum computing is increasingly constrained by a reliance on emergent quasiparticle ontologies and “epistemic patches” that obscure the underlying ab initio dynamics of quantum materials. This paper proposes a radical realignment through the “Signal-Worker” (S-W) framework, which distinguishes between fundamental fermionic workers (electrons) and bosonic signals (photons/forces). We critique the artificial truncation of fluxonium qutrit arrays and contrast them with the passive structural coherence of twistronic and Kagome lattices. Our methodology introduces the Lossless Complexity Index (LCI) as a definitive metric for “architectural intelligence,” with a generalized derivation based on the spectral entropy of the lattice. We identify a “Goldilocks zone” at LCI=1.83 where structural coherence maximizes quantum stability. Findings indicate that passive structural systems provide a $10^3$ efficiency advantage over active flux-driven architectures, offering a robust roadmap toward high-temperature (77K) operation and a theoretical pathway to ambient-temperature computing. By demonstrating predictive divergence from standard BCS theory, we establish the S-W framework as a necessary foundation for the architectonic era of physics-instantiated computing.
The current paradigm of quantum computing is increasingly constrained by a reliance on emergent quasiparticle ontologies and “epistemic patches” that obscure the underlying ab initio dynamics of quantum materials. This paper proposes a radical realignment through the “Signal-Worker” (S-W) framework, which distinguishes between fundamental fermionic workers (electrons) and bosonic signals (photons/forces). We critique the artificial truncation of fluxonium qutrit arrays and contrast them with the passive structural coherence of twistronic and Kagome lattices. Our methodology introduces the Lossless Complexity Index (LCI) as a definitive metric for “architectural intelligence,” with a generalized derivation based on the spectral entropy of the lattice. We identify a “Goldilocks zone” at LCI=1.83 where structural coherence maximizes quantum stability. Findings indicate that passive structural systems provide a 103 efficiency advantage over active flux-driven architectures, offering a robust roadmap toward high-temperature (77K) operation and a theoretical pathway to ambient-temperature computing. By demonstrating predictive divergence from standard BCS theory, we establish the S-W framework as a necessary foundation for the architectonic era of physics-instantiated computing.
The pursuit of macroscopic quantum coherence in strongly correlated electron systems is fundamentally limited by Planckian dissipation, a universal scattering rate that renders active-dynamic control thermodynamically untenable. This paper introduces “Quantum Architectonics,” a design paradigm that leverages passive structural intelligence to overcome this limit. We propose the “Signal-Worker” ontology to unify the physics of 2D Moiré materials and 3D bulk complex oxides (e.g., Cuprates). Central to our framework is the Lossless Complexity Index (LCI), a metric for architectural quality. We provide a first-principles derivation showing the LCI optimum is a universal constant, LCIopt=ln(2π)≈1.83, derived from the fundamental bound on quantum chaos. Our computational models, which approximate Non-Markovian dynamics, demonstrate that optimized “Phononic Scaffolds” can achieve significant coherence gains over memoryless systems. We further show that d-wave pairing symmetry in Cuprates imposes a geometric penalty on shielding efficiency compared to isotropic s-wave systems. This work establishes a rigorous, unified roadmap for engineering “owned coherence,” projecting a theoretical pathway toward stable quantum operation at 77K.
The transition of Quantum Architectonics from a materials-centric discipline to a fundamental ontology of information-matter interaction necessitates a rigorous field-theoretic description. This study bridges that gap by promoting the Signal-Worker ontology to a continuous Gauge-Invariant Quantum Field Theory, where “Workers” are treated as fermionic matter fields and “Signals” as non-Abelian gauge bosons. We construct a SU(2) invariant Lagrangian that recovers the discrete HSW model as a stable infrared fixed point, demonstrating that the lattice architecture of current models is a fundamental emergent reality rather than a mere approximation. The central finding of this research is the first-principles derivation of the Logical Cloning Prohibition (LCI). We demonstrate that the LCI is not merely an architectural heuristic but a symmetry-enforced conservation law—specifically, a Ward identity of the Signal gauge field. Numerical analysis reveals that the LCI scales exponentially with system size (N), providing robust topological protection against local decoherence. By identifying the specific anomalous terms responsible for information robustness, we provide a theoretical blueprint for the next generation of topologically protected quantum devices, validating the Signal-Worker ontology as a complete description of quantum information dynamics. Full text and updates: https://papers.qnfo.org/papers/gauge-invariant-field-theory-of-signal-worker-interactions/
The scalability of active, gate-based quantum computers is fundamentally challenged by the “Thermodynamic Wall,” where the heat from control electronics becomes prohibitive. This study computationally investigates the “Quantum Abacus,” a passive quantum computing architecture that circumvents this limit by using static strain engineering to control fermionic transport. We introduce a theoretical framework that unifies the Signal-Worker ontology with the physics of strain-induced gauge fields and “Tensor Locking.” Using a 1D tight-binding model parameterized for the Weyl semimetal TaAs, we demonstrate that achievable strain gradients (0-5%) can form isolated quantum registers and modulate fermionic hopping integrals. Crucially, simulations utilizing optimized control pulses demonstrate coherent adiabatic transfer with fidelities exceeding 99.9%, resolving previous concerns regarding operation quality. Furthermore, thermodynamic analysis confirms that these strain-mediated operations operate near the Landauer limit, offering an energy efficiency advantage of orders of magnitude over active microwave gating. This work establishes the physical principles and quantifies the thermodynamic advantages of a passive, material-based computational paradigm, offering a rigorous blueprint for a new class of “Green Quantum” technologies.
The prevailing assumption in quantum computing, that quantum effects require near-absolute-zero temperatures to be computationally useful, rests on an engineering convenience, not a physical law. Biological systems routinely exploit quantum coherence at 310 °K (human body temperature), as demonstrated by photosynthetic energy transfer, avian magnetoreception, and enzyme catalysis. This paper argues that these biological precedents point to an unexplored class of room-temperature quantum architectures that could fundamentally transform artificial intelligence. Room-temperature quantum computing is not merely possible; it may be the only path to artificial general intelligence that captures the genuine computational richness of biological cognition. The current consensus, while defensible given gate-model limitations, mistakes one frozen form of quantum computing for all possible forms. Life shows us a different way: not isolating from noise, but structuring it into a computational ally. This roadmap presents: (1) the physical basis for room-temperature quantum coherence; (2) four candidate hardware architectures; (3) a five-phase research agenda spanning 2025-2040; (4) the theoretical foundations that explain why a warm quantum observer might be invisible to its own formalism; and (5) investment and policy recommendations. Throughout, we maintain a rigorous, skeptical stance: no appeals
The quest to build artificial quantum computers has driven decades of research into maintaining coherence at millikelvin temperatures, requiring extreme isolation and consuming enormous resources. Yet nature has already solved the quantum computing problem: electrons operate continuously at the R=F equilibrium point, where rotational and feedback processes balance perfectly. This equilibrium manifests as the fine structure constant α = 1/137.036, representing the optimal architecture for quantum information processing.Rather than attempting to artificially recreate quantum coherence, we propose a paradigm shift: developing electron-interface technology that harnesses the natural computational capacity of electrons at their intrinsic operating point. Within the framework of Fractal Uncertainty Theory (FUT), we demonstrate why electrons represent superior quantum computers compared to artificial systems, explain α as a fundamental computational architecture, and outline a research roadmap toward interfacing with nature’s quantum substrate rather than building fragile imitations of it.The implications are profound: room-temperature quantum computing, trillion-qubit scalability, and energy-efficient quantum information processing—all by working with nature’s design rather than against it.
Abstract Technetium-99m (⁹⁹ᵐTc) radiopharmaceuticals account for more than 80% of diagnostic nuclear medicine procedures, yet their design has remained largely empirical, with minimal integration of quantum-mechanical stability. We present a quantum-entropy optimization framework demonstrating a statistically robust inverse correlation (ρ = -0.76 ± 0.05,p < 0.001) between Rényi-2 entropy (S₂) and quantum state purity (Tr[ρ²]) across ⁹⁹ᵐTc decay pathways. To formalize this relationship and for further research, we propose QCuries (Quantum Curies) as a unit for quantifying quantum-augmented activity, defined as 1 QCurie = 1 Curie × (1 - e⁻ᴿᵉ[ρ]), which reduces to the classical Curie under full decoherence. Our hybrid quantum-classical neural networks (QNN–ANN), trained on ab initio Nikiforov–Uvarov solutions and data from Nuclear information repositories, achieves a 32% accuracy gain over classical ANN baselines in stability and information-theoretic parameter predictions. Predicted phenomena include a 660-attosecond coherence threshold for β⁻ decay, high-purity α emissions (98% at 0.25 nat entropy), and >18% deviations from linear dosimetry in high-entropy regimes(S₂ > 1.5 nat). These results reveal a computationally defined ‘quantum Goldilocks zone’(0.5 < S₂ < 1.5 nat; 0.7-1.2 QCuries), which may guide the optimization of diagnostic tracers pending clinical validation. While clinical validation remains ongoing, this framework provides a physics-grounded path toward more predictive radiopharmaceutical design and may guide future regulatory standards.