Geometric Causal Intelligence and the Structural Limit of Quantum Computation

Description (Abstract): Quantum computing is widely framed as the ultimate accelerator of computation. This paper clarifies a categorical limitation of that framing: even a maximally powerful quantum computer remains a downstream execution engine. It accelerates computation over an already admitted state space, but it does not perform causal admissibility filtering prior to execution. Geometric Causal Intelligence (GCI) operates at a different logical layer. Rather than accelerating evaluation, GCI eliminates causally inadmissible routes before execution-scale computation occurs. As a result, the performance advantage of GCI, measured in Speed Per Precise Result (SPPR), defined as time per correct deterministic decision, is structural rather than incremental. Increasing computational power, classical or quantum, does not close this gap. This paper establishes the orthogonality between GCI and quantum computing, formalizes the distinction between upstream and downstream computation, and explains why SPPR constitutes a hard boundary that compute scaling cannot cross.