Eustache

Project Owner

Quantum Intelligent Agent for Medical Diagnosis

Expert Rating

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Type SingularityNET RFP
Funding Request $35,000 USD
RFP Guidelines Explore theoretical quantum computing models

Overview

The project aims to develop intelligent agents for medical diagnosis based on a Quantum Deep Q-Learning (QDQL) model that integrates parametric quantum circuits (Quantum Neural Networks - QNNs). These parametric circuits will use unitary gates dependent on continuous parameters that are ideal for modeling complex and non-linear interactions such as those between multiple biological outcomes in a medical diagnosis. A parametric quantum circuit or quantum agent would allow clinical data to be encoded directly into quantum rotations, then use QDQL to: predict a diagnostic category, detect a critical or non-critical condition and identify complex symptom combinations.

Project Tags:
Community and Collaboration

RFP Guidelines

Explore theoretical quantum computing models

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $100,000 USD
Proposals 12
Awarded Projects 1

SingularityNET

Apr. 14, 2025

SingularityNET

rfp owner

Explore theoretical quantum computing models

Complete & Awarded

This RFP seeks a technical and experimental assessment of quantum computing architectures in AGI applications. Proposals should explore the practicality and limitations of various quantum approaches — including trapped-ion, superconducting, photonic, and topological quantum computing — in handling probabilistic reasoning, parallel processing, and large-scale knowledge representation. The research could include quantum-classical hybrid simulations and feasibility studies for applying quantum advancements to AGI workloads. Bids are expected to range from $20,000 - $100,000.

View Full RFP

Proposal Description

Our Team

The team will be multidisciplinary to bring together expertise in quantum computing, artificial intelligence, and medical diagnostics to develop a cutting-edge Quantum Intelligent Agent (QIA) for healthcare applications. The aims is to shared vision of transforming medical diagnostics through quantum-enhanced AI models that deliver faster, more accurate, and personalized diagnoses.

Company Name (if applicable)

VCHF

Project details

Overview:

This project aims to design and implement intelligent diagnostic agents that leverage a Quantum Deep Q-Learning (QDQL) model—an innovative fusion of quantum computing and deep reinforcement learning—to enhance decision-making processes in medical diagnosis. By exploiting the computational advantages of parametric quantum circuits (Quantum Neural Networks - QNNs) and the learning capabilities of Deep Q-Learning, the agent is expected to make faster, more accurate, and context-aware diagnostic decisions.

Core Study Questions:

How does the integration of quantum computing improve the learning efficiency and convergence rate of deep Q-learning algorithms in medical diagnosis?
How does the quantum-enhanced feature representation can improve classification accuracy by capturing complex correlations in patient data more effectively than classical embeddings.

Objectives:

1) Develop a QDQL-based agent: Create a medical diagnosis system where the agent learns optimal diagnostic policies through interaction with simulated or real clinical environments using Quantum Deep Q-Learning.

2) Accelerate learning and decision-making: Utilize quantum computing (e.g., quantum-enhanced function approximation or state space representation) to speed up convergence and handle high-dimensional medical data more efficiently than classical counterparts.

3) Improve diagnostic accuracy: Enable the agent to diagnose complex and multi-symptom diseases, even in cases with incomplete or noisy data.

4) Enable adaptive learning: Allow the agent to continuously learn from new data and evolve its diagnostic strategy over time.

Strategies to Overcome Project Limitations:

1) Addressing Quantum hardware constraints

- Use Quantum simulators with error models

2) Improving scalability (High-dimensional data and multimodal inputs):

- Dimensionality Reduction

- Federated learning approaches

3) Enhancing data diversity and realism

Limitations: Small, synthetic datasets

- Synthetic data generation

4) Interpretability and trust

- Integrate Quantum Explainability Tools: Use or develop QML-compatible tools like quantum SHAP, Pauli decomposition analysis, or observable tracking.

- Visual Dashboards: Create clinician-facing dashboards showing feature influence, confidence levels, and alternate diagnoses.

5) Evaluation scope

- Simulation-based validation

- Limited clinical trials

Methodology:

The quantum computing is at the center of the methodological approach:

1) Define the medical diagnosis problem as a Teinforcement Learning (RL) task

2) Quantum Representation of Patient States

3) QDQL agent architecture design

4) Training the QDQL agent

5) Benchmarking & evaluation

6) Explainability and clinician Interaction

Expected Outcomes:

The expected results from the project are categorized into technical, clinical, and research outcomes.

1) Technical results

- Model performance

- Quantum-enhanced capabilities

2) Clinical and functional results

- Improved diagnostic support

- Clinician evaluation

3) Research and scientific contributions

- Novel contributions:

* Proof of concept: First or among the first validated implementations of QDQL for medical diagnosis.

* Quantum benefit benchmarking: Quantitative demonstration of where and how quantum computing adds value over classical approaches in reinforcement learning tasks.

Links and references

1) Personal website: https://www.vchf.net/index.php

2) Simulation (example): https://github.com/eustache-MA/QIAMed/tree/simulation

3) Modelling software agents for medical decision support system: Agent Builder Framework

(Ebook): https://payhip.com/b/5LAr0

Activity Summary

Milestones

7

Total

Discussion

0

Total Comments

Reviews

0

Total Posted

Project Team

8

Total People

Total Milestones
7
Total Budget
$35,000_USD
Last Updated
24 May 2025

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The weighted average of the 4 perspectives Overall

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Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

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This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

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Review Headline

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Eustache

Project Owner

Team Lead & Project Manager Responsible for designing, implementing, and optimizing quantum-enhanced reinforcement learning models to assist in accurate and early medical diagnosis.

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