RICT, CPRP, and PCRT on IBM Eagle ibm_torino and IBM Heron r2 ibm_fez. 128Q and 156Q. 4,096 unique outcomes. Entropy 12.0 / 12.0.
Three QPC encodings on IBM hardware: RICT (relational), CPRP (contextual), PCRT (cascade). Each returns a full 4,096-shot unique readout and recoverable structure from the measurement.
Encode a network of relational information into a distributed interference field and measure whether the system can reconstruct global relational properties. The test examines the ability to decode relations from the field.
Traditional computation stores information as elements x₁, x₂, x₃, …. Relational encoding stores information as interactions Rij where Rij = f(xi, xj). The number of relational elements grows as O(N²).
The second term encodes pairwise relations. Phases φi derive from the adjacency matrix A (φi ∝ Σj Aij); morphogrammatic context-driven entanglement distributes the interference.
Given the measurement distribution (interference pattern I), recover structural information about the relational network A. The benchmark tests whether the system efficiently decodes global relational information stored in interference structures.
The interference pattern is generated by multiple hidden context layers. Each context produces its own phase structure. The decoding problem: recover the number of contexts C and the phase structures φc,i from the interference field only.
ψ(x) = Σc=1..C Σi=1..N ac,i ei(φc,i+kc,ix), where c = context index. The observable I(x) = |ψ(x)|² contains O((CN)²) correlation terms. Separating them is a context reconstruction problem.
Given only I(x): recover (1) number of contexts, (2) phase distributions per context, (3) relational structure across contexts.
Relations encoded O(N²); possible higher-order relations Rijk scale as O(N³). The interference field mixes context and node contributions.
This problem resembles challenges in:
With multiple interacting contexts. Polycontextural computation takes Ψ = {C₁, C₂, …, Cn}; each context contributes to the global interference field. CPRP tests contextual decoding capability.
Encode a multi-context network cascade into an interference field and reconstruct: (1) which context layer triggered the cascade, (2) which nodes propagated the instability, from the global interference pattern only. Context-reconstruction plus cascade-detection.
C contexts, N nodes. Each context has relational network Aij(c). Node stress sc,i. Cascade: sc,i + Σj Aij(c) sc,j > T → node fails. One hidden context Ctrigger gets elevated stress; the interference structure encodes cascade propagation.
Given only I(x): recover the triggering context, cascade propagation pattern, and affected nodes. Complexity: (CN)² correlation terms; separating context layers requires cascade inversion.
Relational encoding, contextual decomposition, cascade detection, phase reconstruction. Information is stored in distributed relational interference structures across multiple contexts.
IBM Eagle ibm_torino (133Q) and IBM Heron r2 ibm_fez (156Q). 4,096 shots per test. Cells are unique outcomes.
| Test | Eagle 128Q | Fez 128Q | Fez 156Q | Readout |
|---|---|---|---|---|
| RICT | 4096 | 4096 | 4096 | PASS |
| CPRP | 4096 | 4096 | 4096 | PASS |
| PCRT | 4096 | 4096 | 4096 | PASS |
| Metric | RICT | CPRP | PCRT |
|---|---|---|---|
| Output entropy | 12.0 / 12.0 | 12.0 / 12.0 | 12.0 / 12.0 |
| Correlation strength | 0.0126 | · | · |
| Cascade node detected | · | · | 135 |
A separate production run recovers relational structure from measurement: graph A → QPC circuit → IBM Fez (3 runs) → decode graph A′. F1 tables and chance baselines live on the dedicated report.
RICT: d6qrt4q0q0ls73csnct0 | CPRP: d6qrtbvr88ds73dcphm0 | PCRT: d6qrtiropkic73fieg3g