Conditional Image-Level Closure in the Q-RAIF Framework
Abstract
Acquired quantum records can drive, sense and act again on the same complete material without replacing its old history by independent baths. We construct a positive autonomous Gaussian interface with physical energy-moment and smooth interacting-source bounds, retaining its clock and all old references. A paid Gibbs bank renews a specified receiver. Acquisition, decision, force, renewal and the next reading share one finite cursor path with arbitrarily small joint error as clock resources grow. Exact algebraic transport is a separate question: a reduced coupling supplies a completely positive map whose multiplicative domain is its exactly represented algebra. Discarded non-atomic ports limit recovery; retained oscillator loops and closed propagation permit exact operations. A full-mode fermion-ring realization renews the same body to uniform per-cycle tolerance through four thermal outlets, then reinstalls the mixed receiver and current purifier by neutral pumps. Physical gates compress noisy multi-cycle records, load overlapping packets and sort the actual spectrum into a paid finite-volume thermal carrier. With every atypical label and old purification retained, the complete output Gibbs deficit per unit time can approach zero in a prescribed finite layout. A separate massive-outside body supplies full-angle local gates and weighted physical-source control. A spatial work-clock field executes a finite smooth schedule using the actual retained field state. Matrix Ward equations and the delayed incoming kernel close at the same derivative order; physical energy moments control the complete joint gate/reset output. Gap work, clock baseline stress and its finite work correction remain charged. This uses static endpoints and a supplied material frame, with horizon-dependent clock resources. The distinct receiver, ring and massive-body constructions do not yet supply one dynamically supported, covariant apparatus with sustained fixed-capacity sharp exhaust.
Fields of Study & Subjects
Bayesian Modeling and Causal Inference
Advanced Neural Network Applications
Image and Signal Denoising Methods
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 8 February 2026
- Document type
- Preprint