Scalable Topological Quantum Error Mitigation in Multi-Qubit Networks via Real-Time Causal-AI Sensitivity Operators on QPU Hardware

Quantum processors in the Noisy Intermediate-Scale Quantum (NISQ) era are severely constrained by spatial hardware asymmetries, temporal drift, and multi-qubit crosstalk. Traditional error suppression protocols apply static decoupling or phenomenological calibrations that struggle under continuous decoherence stress. Building upon the Entropic-Resistance framework—where spacetime is represented as a discrete, algorithmically expanding topological network and time is governed by recursive relaxation—we extend deterministic Causal-AI Taylor sensitivity operators (j1, j2) to multi-qubit topologies. We introduce a Just-In-Time (JIT) compensation engine that queries real-time QPU telemetry (qubit-specific dephasing T2 times and native two-qubit gate error rates) to dynamically compute non-commutative phase compensations. By decomposing physical microwave rotations inside logic gates, microscopic counter-rotations are embedded directly into hardware instructions. We validate this protocol on IBM Quantum's Heron processor (ibm_fez) by subjecting 3-qubit and 5-qubit Greenberger-Horne-Zeilinger (GHZ) states to deliberate decoherence stress via calibrated temporal delays. On 3-qubit states, the Lasky-Tensor engine achieves an absolute fidelity improvement of +3.81% (32.5% relative error reduction). On fragile 5-qubit linear networks, it delivers a +3.42% absolute gain (17.2% relative error reduction). These results establish that recursive network stress models scale effectively, providing a deterministic, white-box quantum error mitigation paradigm without physical qubit overhead. Methodology & Hardware: Hardware: IBM Quantum (ibm_fez, Heron architecture) Framework: Qiskit Runtime SamplerV2 Underlying Theory: Entropic-Resistance Paradigm / Causal-AI What-If Engine