Project for Team 7 at the NYUAD Hackathon for Social Good 2024.
AlfiniQ is a groundbreaking project that combines machine learning, quantum computing, and genomic sequencing to provide an efficiently scalable early warning system for disease prevention in post-catastrophe zones. By using AI and quantum computing to radically accelerate the analysis of genetic predispositions of individuals affected by crises, AlfiniQ empowers them to take proactive measures and secure their futures, saving millions of lives and helping governments rebuild civilizations from the ashes.
AlfiniQ leverages cutting-edge technologies to address the challenges faced by populations affected by catastrophes, such as chemical weapons, food shortages, and poor sanitary conditions. The project workflow consists of the following components:
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Disease Prediction: A machine learning model (LLM + AI) takes in crisis conditions and outputs a list of diseases likely to outbreak, based on population displacement and DNA data.
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Quantum-Accelerated Genome Sequencing (QC I): Utilizing quantum computing, AlfiniQ sequences entire genomes in a fraction of the time compared to classical methods, reducing the analysis timeline from 12 weeks to just 1.
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Quantum-Enhanced Pattern Matching (QC II): Specialized Grover search algorithms, optimized for bioinformatics, provide a quadratic speedup for genome pattern matching and identifying genetic predispositions.
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Data Privacy: AlfiniQ integrates post-quantum cryptography (PQC), specifically Elliptic Curve Cryptography (ECC), to ensure the robust encryption of DNA data against classical and quantum computer attacks.
AlfiniQ has the potential to revolutionize healthcare and support systems in post-conflict regions, with far-reaching impacts:
- Protecting the futures of more than 100 million Arabs affected by catastrophes, wars, and genocides.
- Revitalizing and supporting a $114B healthcare workforce in the aftermath of crises.
- Reducing genome analysis time by 92.7%, from 12 weeks to just 1, enabling scalability to serve entire populations.
- Fostering greater trust in scientific systems and medicine among 200 million rural Arabs.
- 9 Months: Data collection, generating datasets of crisis conditions and correlated diseases to train and fine-tune the foundational model and random forest.
- 1 Year: Sequencing analysis complete, utilizing quantum computing hardware to run algorithms and complete comprehensive genomic analysis for affected populations.
- 1.5 Years: Prototype and data assistance, returning data about the population's predispositions, collecting further data, and repeating the process to fine-tune the prediction model.
- 2 Years: Alpha version release, empowering people to secure their futures based on the analyzed data.
AlfiniQ collaborates with NGOs for DNA extraction and sequencing, ensuring ethical and responsible implementation of the project.
- Varsamis, G. D., Karafyllidis, I. G., Gilkes, K. M., Arranz, U., Martin-Cuevas, R., Calleja, G., ... & Wong, J. (2023). Quantum gate algorithm for reference-guided DNA sequence alignment. Computational Biology and Chemistry, 107, 107959.
- Sarkar, A. (2018). Quantum Algorithms: for pattern-matching in genomic sequences.
- Funanage V. L. (2021). Impact of Genetic Testing on Human Health:: The Current Landscape and Future for Personalized Medicine. Delaware journal of public health, 7(5), 10–11. https://doi.org/10.32481/djph.2021.12.005