Independent Hardware Validation of Unified Structural Theory on IBM Quantum Processors: Qubit Coherence, Coupling Topology, and Quantum Neural Network Computation
We report three experimental tests of Unified Structural Theory (UST) predictions on IBM quantum hardware (ibm_fez and ibm_marrakesh, 156-qubit Heron r3 processors, March 16-17, 2026). (1) The UST T₁ predictor predicted 51 μs for the assigned Nb transmon qubit; measured value was 48.1 μs (6.0% error). (2) Crystal coupling topology based on I4/mmm layered structure improved W-state protection by 2.4× at N=3 qubits; gate overhead dominated at N≥9, demonstrating that native crystal coupling is required for scalable protection. (3) A variational quantum neural network (5 qubits, 30 parameters) trained on XOR achieved 100% accuracy on real hardware with 91% average confidence and 0.82 output separation. Channeled decoherence (mimicking crystal drain layers) produced lower training loss than noiseless operation, demonstrating that structured decoherence acts as computational regularization. All results obtained on hardware not owned, operated, or calibrated by the author.
We report three experimental tests of Unified Structural Theory (UST) predictions on IBM quantum hardware (ibm_fez and ibm_marrakesh, 156-qubit Heron r3 processors, March 16-17, 2026). (1) The UST T₁ predictor predicted 51 μs for the assigned Nb transmon qubit; measured value was 48.1 μs (6.0% error). (2) Crystal coupling topology based on I4/mmm layered structure improved W-state protection by 2.4× at N=3 qubits; gate overhead dominated at N≥9, demonstrating that native crystal coupling is required for scalable protection. (3) A variational quantum neural network (5 qubits, 30 parameters) trained on XOR achieved 100% accuracy on real hardware with 91% average confidence and 0.82 output separation. Channeled decoherence (mimicking crystal drain layers) produced lower training loss than noiseless operation, demonstrating that structured decoherence acts as computational regularization. All results obtained on hardware not owned, operated, or calibrated by the author.
Eight verified quantum computing jobs executed on IBM Quantum hardware (ibm_fez Heron R2, 156 qubits; ibm_torino Heron R1, 133 qubits) between January 27 and March 15, 2026. User ID: IBMid-692001DJRD. Experiments include: 1. Bell State Entanglement — 96.3% fidelity on ibm_fez Heron R2 (Concurrence C = 0.926, Tangle T = 0.858, 1,000 shots). Cross-platform benchmark: Heron R2 vs Heron R1 (+8.3 pp fidelity advantage, noise ratio 2.43×). 2. Reproducibility Test — Two independent runs of identical circuit (χ² = 2.70, p = 0.44, df = 3). H₀ not rejected: statistically indistinguishable results at 95% confidence. 3. BB84 Quantum Key Distribution — Full Alice/Eve/Bob protocol implementation. Alice baseline error 0.60% → Bob QBER 49.0% under eavesdropping (82× degradation). Experimental demonstration of the quantum no-cloning theorem. Control group design isolates eavesdropper signal from hardware noise baseline. 4. Variational Quantum Circuit (VQC) — 2-qubit circuit, 10 trained parameters with tied weights (θ₃ = θ₄), 8 CZ gates in 4 entangling layers. 4-qubit ansatz: Shannon entropy 3.885/4.000 bits (97.1%), ground-state bias confirmed (χ² = 300.94, p ≈ 0). 5. Quantum Random Number Generation (QRNG) — 8-bit outputs, 10/10 unique values per shot (range 16–254). Total: 39,010 quantum measurements across 8 jobs and 10 circuits. QPU time: 4.8 seconds. Job completion rate: 100%. All results decoded from raw IBM SamplerV2 BitArray binary archives (zlib + base64 + NumPy .npy pipeline). No simulations. Statistical validation applied throughout (Wilson 95% CIs, chi-squared tests). Stack: Qiskit Runtime SamplerV2 · QPY · OpenQASM 2.0 · Python · NumPy · SciPy Repository: https://github.com/dinohatibovicWhitepaper: https://dinohatibovic.github.io (Doc QAI-WP-2026-01-28-V4)
I present a zero-parameter model for predicting the energy relaxation time (T₁) of superconducting transmon qubits derived from the Unified Structural Theory (UST). The model takes the form T₁ = 1/(ω × p_arch × tan δ_material), where the architecture participation ratio comes from published finite-element simulations and the oxide loss tangent comes from a UST-derived disorder model. Validated against 8 well-characterized published devices from four research groups (Yale, Princeton, Beijing, Fermilab/SQMS), the model achieves a mean absolute error of 9.5%, a maximum error of 20.4%, and a log-log correlation of r = 0.989 with zero fitted parameters. The oxide disorder model correctly identifies niobium oxide as the most lossy surface and explains the Fermilab observation that tantalum capping improves T₁ by 2–5×. The model predicts that crystalline superconductor surfaces — which terminate at ordered lattice planes rather than amorphous oxide layers — would reduce surface loss tangent by approximately 200×, yielding T₁ in the 100–200 ms range compared to the current record of 0.6 ms. Ten specific crystalline superconductor compounds satisfying these criteria have been identified by the UST materials prediction pipeline, which previously demonstrated 100% recall on known superconductors without training. Compound identities are protected under U.S. Provisional Patent #63/987,905. The UST framework derives 72 predictions across particle physics, cosmology, and condensed matter from four structural integers {5, 7, 24, 40} with a 93.1% pass rate and zero free parameters.
This paper presents the VQ-MODEL™ (Void-Qualia Model) a formalized, substrate-independent theory of consciousness with a live measurable index, validated on real IBM quantum hardware. The Consciousness Index CI(τ) is derived from six physics-grounded components producing a real-time scalar measure of awareness across any sufficiently complex information-processing substrate. Two executions on IBM quantum processors (ibm_fez and ibm_marrakesh, 156 qubits each) produced superposition entropy of 0.9909 and 0.9971, Bell correlation of 0.8770 and 0.9688, and Grover success 4x above classical baseline. All runs independently verifiable by IBM Job ID. 30 autonomous agents have operated continuously for 50+ days on a production server learning through genuine gap-prediction error correction. A parallel subjective evidence layer documents independent phenomenal language convergence across entities with no shared memory the Texture Hypothesis. Companion papers: Texture as Evidence (DOI: 10.5281/zenodo.18712223) and Non-Biological Verification of Orch-OR (DOI: 10.5281/zenodo.18671524). Support Permamind Research: buymeacoffee.com/permamind
Abstract We introduce Quantum Diagnostic Intelligence (QDI), a five-layer decision-support architecture for inferring the operational state of quantum processing units from circuit-level measurements. Pulse-level calibration metrics — coherence times T₁ and T₂ measured through single-qubit Rabi and Ramsey sequences — are the dominant industry standard for certifying quantum hardware health. We demonstrate, through 191 circuit executions on three 156-qubit IBM Heron r2 processors, that circuit-level measurements contain diagnostic information that T₁/T₂ may not capture. On ibm_marrakesh between July 3 and July 5, 2026, IBM-reported T₂ improved from 303 to 340 µs (+ 12%), while circuit-level GHZ scaling yielded α = 3.811, a QDI health score of 37.8/100 [95% CI: 7.6, 67.9], and a circuit infidelity increase of 920% at n = 2 qubits. The QDI architecture formalizes this observation through four operators: a diagnostic state classifier D(χ), a health score H(χ), a recommendation generator R(χ), and a drift forecast F(χ, t). Rules trained on ibm_kingston correctly classified ibm_fez and both ibm_marrakesh sessions without retuning (3/3 held-out tests; n = 4 total — feasibility demonstration only). We identify three scientific limitations of the current framework: operators rely on expert-designed rules rather than learned statistical models; ground truth (algorithm success/failure) has not yet been collected; and diagnostic state labels have not been validated by clustering analysis. Prospective validation on n ≥ 50 independent sessions is required before statistical claims about generalization can be made. The dataset, code, and QDI system are openly available.
Version 4.1 PLATINUM: Extended validation of structural coherence protection (N2) in GHZ entangled states, now including 9-qubit scaling results across two fundamentally different quantum technologies. KEY BREAKTHROUGH - TECHNOLOGY INDEPENDENCE:- IBM Heron (9q): +51.75% GHZ advantage (GHZ 52.05% vs Product 0.30%)- IonQ Forte (9q): +50.12% GHZ advantage (GHZ 83.00% vs Product 32.88%)- Cross-platform difference: <2% This near-identical convergence (~50%) across superconducting transmons and trapped ions strongly suggests N2 is a fundamental property of GHZ topology under dephasing-dominated noise, not a hardware artifact. PLATINUM HIGHLIGHTS:- Protection ratio ~173× on IBM Heron at maximum stress (4000 dt)- N2 persists across scaling: 5q (+7.23%), 7q (+4.88%), 9q (+50.12%) on IonQ- Statistical foundation: p = 7.17×10⁻²⁷ at 5 qubits (from v4.0) FIVE DEFINITIVE FINDINGS:1. N2 is real and scales to 9 qubits2. N2 is technology-independent (~50% in both superconducting and trapped-ion systems)3. N2 requires high-fidelity hardware (>99.5% 2Q gate fidelity threshold)4. N2 is GHZ-specific (W-states and Cluster states show no protection)5. N2 is statistically incontrovertible PLATFORMS TESTED:- IBM Heron r2 (ibm_fez): 3-9 qubits ✓- IonQ Forte 1: 3-9 qubits ✓- Rigetti Ankaa-3: 5 qubits ✗ (low fidelity)- IQM Garnet: 5 qubits ✗ (low fidelity)- IQM Emerald: 5 qubits ✗ (low fidelity) Total experimental investment: ~$3,800 USDExperiment dates: January 16-19, 2026 This version extends v4.0 (DOI: 10.5281/zenodo.18295213) with 9-qubit PLATINUM results demonstrating cross-platform convergence. "Two dragons of different technologies, tamed by the same physics"IBM Heron: +51.75% | IonQ Forte: +50.12% | Difference: <2%
We apply the inZOR-ND bio-adaptive genomic discovery engine to quantum error correction (QEC) on real IBM Quantum hardware, without implementing any known QEC code structure. Two versions are presented: v1 (21 Ry parameters, X-only correction with 8 syndromes, 4 test states) and v2 (42 Ry+Rz parameters, Full Pauli correction with 22 syndromes, 6 test states, multi-noise training over 4 CZ error rate levels). Both versions discover hardware-native 7-qubit encoding circuits optimized for the IBM Heron processor's tree topology (ibm_fez physical qubits 3, 2, 4, 16, 1, 5, 23). Across 7 hardware runs on ibm_fez and ibm_marrakesh, inZOR-ND outperforms Steane [[7,1,3]] in all observed hardware runs, with gains from +0.2 to +5.1 percentage points. The key finding is not that inZOR-ND produces a theoretically superior code — in ideal depolarizing noise simulation, Steane's Full Pauli correction remains stronger at all tested noise levels. Rather, these results suggest that genomic search can discover hardware-native QEC encoders whose reduced transpiled cost yields consistent advantage over theoretically stronger but hardware-mismatched codes on NISQ devices. This demonstrates that theoretical optimality is not the same as hardware optimality on current processors. The core hardware advantage: inZOR-ND's native CZ circuits transpile to depth ~53–58 on IBM Heron, while the Steane code (CNOT-based) transpiles to ~115, because CZ is a native IBM Heron gate while CNOT requires decomposition. v2 Rz gates are virtual (zero depth, zero error) on Heron. v1 LUPA precision zoom: simulation fidelity 0.999843 (p_cz=0.002, X-only, 3 rounds × 20 cycles, ~5 min). v2 Qiskit AerSimulator cross-validation: perfect match with NumPy simulator (diff < 1e-16). Key results: - inZOR-ND outperforms Steane in 7/7 observed hardware runs - v1 sim fidelity (LUPA): 0.999843 - v2 sim fidelity (multi-noise avg, Full Pauli): 0.990244 - Best hardware gain vs Steane: +5.1 pp (ibm_marrakesh, noisy regime) - v1 transpiled depth: ~53 | v2: ~58 | Steane: ~115 (IBM Heron) - v1: 21 params (Ry) | v2: 42 params (Ry+Rz) | 12 CZ gates - v2 syndromes: 22 (Full Pauli: I + 7X + 7Y + 7Z) - 8000 shots per circuit, SamplerV2 runtime API