Bangladesh Quantum Research Group
We work on two fronts. We turn the data the government already holds into hyper-local, crop-stage advice a farmer can act on today. And we help national systems move to post-quantum cryptography, before the records they protect outlive the encryption around them.
Film
A short introduction to the platform: where the data comes from, what the model does with it, and how the advice reaches a farmer holding a feature phone.
Hosted on YouTube. Playback starts only when you press play.
What we do
Quantum methods are useful to Bangladesh in two very different ways today. One is an opportunity in the fields. The other is a deadline in the data centre. We work on both, and we are careful about the difference.
Opportunity
A decision-support system that reads the farmer registry, BAMIS, BMD forecasts and open satellite data, and returns advice for one plot, one crop, one week — delivered by SMS, voice call and a Bangla app.
Read the pre-pilot plan →Deadline
An inventory of where RSA and elliptic-curve keys live in a national system, a risk ranking by how long each record must stay secret, and a migration path to the finalised NIST standards.
See the migration path →Why the fields first
Farmers are not farming badly. They are farming without the hyper-local signal they need at the exact moments — sowing, irrigation, pest pressure — when a district-level forecast is not enough. Sources: Bangladesh Rice Research Institute; peer-reviewed rice yield-gap modelling
Method
No quantum hardware is procured, and nothing a farmer depends on runs on a quantum machine. Quantum methods are used offline, during training, and only where they beat a strong classical baseline on the same data.
Which mix of satellite bands, soil variables and weather features actually predicts irrigation need in a given upazila? We use QAOA-style feature selection and tensor-network simulation on GPU servers to explore a space that grows exponentially with each variable added.
Quantum kernel methods act as offline teacher models where field data is scarce. Their behaviour is distilled into a small classical student model — the one that actually ships. Every teacher is scored against a strong classical baseline, and we publish the comparison either way.
The student model runs on ordinary servers and pushes plain-language advice through the channels farmers already use: SMS, automated voice calls for feature phones, and an app for smartphone users. Delivery stays with trusted DAE field officers.
SMS
কাল বৃষ্টি হবে, সার দিও না
Rain tomorrow — hold the fertiliserVoice call
১৫ দিন পর ফসল কাটবেন
Harvest in about 15 daysApp
ক্ষেতে মাটি ভেজা থাকতে সার দিন
Apply fertiliser while the soil is still wetOn honesty about the method: quantum advantage for these workloads is not proven. The pre-pilot is designed to find out, with a classical fallback that is already an improvement on current practice. If the quantum-inspired path adds nothing, that result gets written down too.
Inputs
Nothing here starts with a new national survey. The pre-pilot reads existing registries and open Earth observation, subject to data-sharing agreements with each custodian.
How the pipeline is assembled →National alignment
Chief Executive Officer
Quantum algorithms, research direction and government engagement.
View credentials →Chief Technology Officer
Platform architecture, data pipelines and field deployment.
Forty-five minutes, no slides required. Bring a problem — a crop, a district, or a system whose keys are older than its data retention rules.
