SIH26139/MedTech / BioTech / HealthTech

Quantum machine learning platform for disease detection

A software platform that lets researchers train hybrid quantum-classical machine learning models for early disease detection using biomedical datasets.

Egreen Quanta · Software

Teams already in: Not read yet
Buildable in 36 hours: LowOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in Dataset

Whether this suits your team

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Data Ingestion Pipeline
Handles the import and pre-processing of high-dimensional biomedical datasets.
02
Hybrid QML Models
Implements quantum-enhanced classification and regression models using simulators or near-term devices.
03
Training and Inference
Executes workflows for model training and makes disease predictions.
04
Explainability Module
Provides interpretability features for the model outputs.
05
Benchmarking Dashboard
Evaluates performance against purely classical baselines in accuracy and efficiency.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will check whether your hybrid quantum-classical models run successfully on simulators, improve accuracy over classical baselines, and include all required features like data handling and explainability.

They never say

which specific diseases the platform must target
which exact biomedical datasets will be provided or used

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Which specific diseases the platform must target?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Which exact biomedical datasets will be provided or used?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

8 more— the ones a panel gets to if there is time

The brief asks for data ingestion pipeline. How would you build that?
Decoded from SIH26139 itself — Handles the import and pre-processing of high-dimensional biomedical datasets. The brief asks for it by name.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No specific user named
The text mentions applying the platform to biomedical datasets but never names the exact end user.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: Provided — open it
Does the brief need data?: Yes, and they gave it to you.

Also from Egreen Quanta

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26139, set by Egreen Quanta.
Its official title, word for word: "Hybrid Quantum Machine Learning Platform for Early Disease Detection"
Theme: MedTech / BioTech / HealthTech. Category: software.

In plain terms: A software platform that lets researchers train hybrid quantum-classical machine learning models for early disease detection using biomedical datasets.

## The organisers' full description, verbatim

Background Early and accurate detection of diseases significantly improves treatment outcomes and reduces healthcare costs. Classical machine learning models have achieved notable success in medical diagnosis; however, they often face limitations when dealing with high-dimensional, noisy, and complex biomedical data (e.g., genomics, medical imaging, and electronic health records). Quantum machine learning (QML) offers the potential to capture intricate patterns through quantum superposition and entanglement. Due to current hardware constraints, a hybrid quantum-classical approach provides a practical pathway to leverage quantum advantages while remaining executable on existing quantum simulators and near-term quantum devices. Description This problem focuses on designing and developing a hybrid quantum machine learning platform for early disease detection. The platform will integrate classical pre-processing and feature engineering with quantum-enhanced learning models (such as quantum support vector machines, quantum neural networks, or variational quantum classifiers). It will be applied to biomedical datasets for the early identification of diseases (e.g., cancer, cardiovascular disorders, or neurological conditions). The system should support data ingestion, hybrid model training, prediction, explainability, and performance evaluation against purely classical baselines. Objectives • Design a hybrid quantum-classical machine learning architecture suitable for early disease detection. • Develop quantum-enhanced classification/regression models that can process high-dimensional biomedical data. • Improve detection accuracy, sensitivity, and specificity compared with classical machine learning baselines. • Ensure the platform is scalable, interpretable, and compatible with near-term quantum hardware and simulators. • Incorporate data pre-processing, feature selection, and model explainability modules. • Benchmark the hybrid approach against classical models in terms of accuracy,computational efficiency, and generalization performance. Expected Solution A fully functional hybrid quantum machine learning software platform capable of performing early disease detection on real or benchmark biomedical datasets. The solution must include data handling pipelines, hybrid quantum-classical model implementation, training and inference workflows, performance evaluation, explainability features, and comprehensive documentation. Add 'Delivery Table (Expected Deliverables)' here

## What the brief never says

- which specific diseases the platform must target
- which exact biomedical datasets will be provided or used
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- The organisers attached a dataset, so build against theirs rather than inventing one.
- Known obstacles in the brief: No named user.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from Egreen Quanta's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (304 words)

Background Early and accurate detection of diseases significantly improves treatment outcomes and reduces healthcare costs. Classical machine learning models have achieved notable success in medical diagnosis; however, they often face limitations when dealing with high-dimensional, noisy, and complex biomedical data (e.g., genomics, medical imaging, and electronic health records). Quantum machine learning (QML) offers the potential to capture intricate patterns through quantum superposition and entanglement. Due to current hardware constraints, a hybrid quantum-classical approach provides a practical pathway to leverage quantum advantages while remaining executable on existing quantum simulators and near-term quantum devices. Description This problem focuses on designing and developing a hybrid quantum machine learning platform for early disease detection. The platform will integrate classical pre-processing and feature engineering with quantum-enhanced learning models (such as quantum support vector machines, quantum neural networks, or variational quantum classifiers). It will be applied to biomedical datasets for the early identification of diseases (e.g., cancer, cardiovascular disorders, or neurological conditions). The system should support data ingestion, hybrid model training, prediction, explainability, and performance evaluation against purely classical baselines. Objectives • Design a hybrid quantum-classical machine learning architecture suitable for early disease detection. • Develop quantum-enhanced classification/regression models that can process high-dimensional biomedical data. • Improve detection accuracy, sensitivity, and specificity compared with classical machine learning baselines. • Ensure the platform is scalable, interpretable, and compatible with near-term quantum hardware and simulators. • Incorporate data pre-processing, feature selection, and model explainability modules. • Benchmark the hybrid approach against classical models in terms of accuracy,computational efficiency, and generalization performance. Expected Solution A fully functional hybrid quantum machine learning software platform capable of performing early disease detection on real or benchmark biomedical datasets. The solution must include data handling pipelines, hybrid quantum-classical model implementation, training and inference workflows, performance evaluation, explainability features, and comprehensive documentation. Add 'Delivery Table (Expected Deliverables)' here