The solution: SQETCH 2026
01
Deployanywhere: Cloud PoC
02
on-prem enclave
03
scale.
QML/QFL Explorer 2025
- Interfaces: Python SDK, REST, Excel; a p p dockers for backtests & QML
- Simplify the training of QAI model
- Get insights form sparse data, scattered data
- Our modelsaredatasober
- The classics: hyperparameter finetuning, distribution shift monitoring
Define Compute, Storage, Train, Inference Resources
Train, Monitor, Infer, In One click Or One API Call
QML Explorer '25
Train federated models across several sites. Data never leaves. Only derived data is aggregated.
QML Explorer '25
-Waveforms
-Tabular, Text
-Images
More accuracy in low-data regime Thanks to Quantum Neural Networks
-Tabular, Text
-Images
More accuracy in low-data regime Thanks to Quantum Neural Networks
-Non-Unitary models (Mid-circuit measurement)
-Progressive layers
-Allow for bigger, more powerful models -QPU fine tuning
-Blind &Confidential Computing
-Keep you data secure
-Progressive layers
-Allow for bigger, more powerful models -QPU fine tuning
-Blind &Confidential Computing
-Keep you data secure
If your data is unique, Expect a figure of merit to get a top up today
With Middle Circuit Measurement
Without Middle Circuit Measurement
With Middle Circuit Measurement
Without Middle Circuit Measurement
Non-Unitary models (Mid-circuit measurement)
Allow for bigger, more powerful models
Multimodal capacity
- - Because reality i s multimodal Machine readable news Add extra fresh data
- - Push AUC & PR-ROC Compared t o purely classical Reduce classification errors
