Stream, score, compress, and govern B2B trade-lead tensors in real time — fueling QuantIQ lead generation, quantum QSVM scoring, and interactive research dashboards on Kryptur infrastructure.
Recognised among leading quantum e-commerce research pipelines, Kryptur advances smarter B2B lead scoring through industry-leading AI, hybrid quantum-classical kernels, tensor compression, and live API infrastructure.
Quantum kernel SVM achieves AUC 0.994 vs classical 0.971 on classically-hard lead datasets.
7,528× reduction of voyage-cost tensors in 32.3 seconds on H200 GPU hardware.
50,000 enriched UN Comtrade leads served with 2D/3D visualisations via Hetzner VPS.
500×1000×2000 trade tensor decomposed in 0.40 s on H100 for real-time feature extraction.
ZZFeatureMap quantum kernel entries measured with fidelity matching classical RBF structure.
Partner with quantum R&D experts across 46 pipeline stages — Orchestration, Tensor, and Quantum.
QSVM AUC 0.994 on classically-hard lead scoring — +2.3 pp over classical RBF SVM.
Achieved 7,528× tensor compression in 32.3 s on single H200 GPU hardware.
50,000 enriched HS 94 & 84 trade leads with 24 engineered features for B2B scoring.
Large-tensor TT decomposition completed with relative Frobenius error 0.359 at rank 4.
MPS amplitude encoding fidelity 0.993647 on NISQ hardware validation stage.
Optimize B2B scoring outcomes from tensor compression and NISQ kernel investments.
Live B2B lead scoring dashboard — GET /api/v1/leads via secure same-origin proxy to Hetzner QuantIQ infrastructure.
Click column headers to sort · Intent 1 = high purchase intent
| No leads returned. Enter a valid API key and refresh. | ||||||||
Color gradient by intent · hover shows LeadID on first axis
Quantum kernel SVM on classically-hard lead dataset outperforms RBF baseline by +2.3 pp.
500×1000×2000 trade tensor decomposed for real-time feature extraction.
Voyage-cost tensor compressed in 32.3 s on H200 with 0.9936 QPU fidelity.
HS 94 & 84 furniture/equipment trade lanes with 24 engineered features.
Live compression calls a same-origin proxy at /api/ttdecom/decompose_voyage_tensor, which forwards to the Hetzner TTDECOM service. The demo runs without a key; production keys are forwarded as X-API-Key when provided. Request access from research@kryptur.org.
Endpoint: POST /api/ttdecom/decompose_voyage_tensor
Compress a synthetic voyage-cost tensor using Tensor-Train decomposition. All computation runs on a shared cloud orchestration layer; for heavy workloads, dedicated tensor-stage GPU instances are used automatically.
Authors: Raja Ram M · Kryptur Quantum R&D · Borel Sigma Data Center
Abstract. QuantIQ integrates UN Comtrade trade data, GPU-accelerated Tucker decomposition, and NISQ quantum algorithms to score B2B furniture & equipment leads. The companion Tensor-Train API compresses voyage-cost tensors by 7,528× with 0.9936 QPU fidelity — exposed here as a live REST dashboard with 46 Orchestration, Tensor, and Quantum experimental stages.
Global shipping moves over 11 billion tonnes of cargo annually, yet real-time fleet optimisation remains constrained by voyage-cost tensor dimensionality. Tensor-Train decomposition compresses such data by orders of magnitude while preserving low-rank trade-lane structure.
A 4-D tensor (15000 ports × 30 cargoes × 104 weeks × 5 vessel types) is decomposed via TT-SVD on H200 GPU. TT cores are amplitude-encoded on a 156-qubit QPU for hardware validation.
Compression ratio 7,528× in 32.3 s; QPU fidelity 0.993647; quantum reservoir MSE 0.0048 vs. classical AR 0.0119.
The pipeline is exposed via REST /decompose_voyage_tensor with auto-scaling to GPU for large tensors.
46-stage experimental campaign spanning orchestration compression, tensor analytics, and quantum algorithms.
| # | Stage | Input | Output |
|---|---|---|---|
| 1 | Real-time fleet rerouting | TT-cores | MIP solver <1 min |
| 4 | Scenario simulation | TT-cores + disruption mask | Cost delta +3.69% |
| 7 | Cross-validation | 5 synthetic seeds | Ratio 7528× ± 0 |
| 8 | Transfer learning | Original cores + new cargo | Extended cores (31 cargoes) |
| # | Stage | Input | Output |
|---|---|---|---|
| 9 | Large-tensor TT decomposition | 15k×30×104×5 tensor | 7,528× compression, 32 s |
| 10 | Pareto frontier | Ranks 2–16 | Elbow at rank 5 |
| 11 | Anomaly detection | Rank-4 reconstruction | 4.84 M anomalies flagged |
| 22 | Forecast from G₂ | G₂ weekly slices | RMSE 0.0468 |
| # | Stage | Input | Output |
|---|---|---|---|
| 28 | MPS amplitude encoding | TT-cores → 3-qubit state | Fidelity 0.9936 |
| 29 | Quantum kernel matrix | 4 ports, ZZFeatureMap | 10 kernel entries |
| 30 | QAOA route sampling | 3-qubit Hamiltonian | Optimal route 30.9% |
| 36 | Quantum reservoir forecasting | G₂ time series | MSE 0.0048 |
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