Quantum Deepflow: A Quantum-Integrated Forecasting Platform for Strategic Decisions in Raw Material Procurement
Bibliographic record
Abstract
We present Quantum Deepflow, a forecasting and decision support platform that integrates classical and quantum sequence modeling to address volatility and data irregularity in raw material procurement. The system combines an LSTM autoencoder with a Quantum Long Short-Term Memory (QLSTM) model, which enables robust and accurate forecasts from noisy time-series inputs. Users can interact with the platform through a visual interface that links forecast outputs to strategic key performance indicators such as purchase timing, cost estimates, and inventory risk. In a real-world deployment at a Korean steel manufacturer, the system achieved a 32.5% reduction in overstocking and saved $1.8 million in inventory costs. This work demonstrates a practical approach to exposing quantum-enhanced forecasting capabilities through an automated, cloud-based interface that bridges the gap between emerging quantum technology and enterprise-scale decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".