Classiq
Enhancing Genomics via Quantum Kernels
Authors: @ManjulaGandhi, @sgayathridevi, @swati-saraswathi, @Sunitha618
Our team is excited to take part in the Classiq and Quantum Coalition “Implementation Challenge” by implementing key ideas from the paper Classical-to-Quantum Sequence Encoding in Genomics
A comprehensive overview of our plan and technical details can be found in the attached document: ENHANCING GENOMICS VIA QUANTUM KERNELS
Thank you @swati-saraswathi , I did not completely understand what is going to be implemented as part of this issue. You mention several data encoding techniques, as well as four algorithmic approaches. Are you going to implement all four? In particular, you mentioned quantum anhilling, how are you going to implement this approach with Classiq (which deals with gate-based quantum computing)?
Thank you for your comment @TomerGoldfriend! The project primarily focuses on implementing quantum kernel estimation and hybrid quantum-classical neural networks, while other approaches like VQC and quantum annealing are explored theoretically for scalability.
Regarding quantum annealing, you’re right that it is typically implemented on quantum annealers like D-Wave. Since Classiq is designed for gate-based quantum computing, we plan to investigate alternative formulations using QAOA to approximate annealing behavior within the circuit model.
Additionally, we are exploring a hybrid classical-quantum approach, where Variational Quantum Eigensolver (VQE) or simulated annealing assists in optimizing the alignment process while keeping computations feasible on gate-based quantum systems.
OK @Sunitha618 , from your description it seems like you are indeed going to explore all four algorithms (QSVM, QNN, QAOA, Adiabatic approach). You should focus on one or two approaches, and also provide more details on the implementation, and how you will use Classiq high-level modelling for it. We already have several examples of QSVM, QNN, and QAOA. Some of these are basic examples and some more applicative (e.g., credit fraud detection, cybersecurity).
In order to review your suggestion we need to understand how it will defer from the already existing examples. Applying existing models to a different use-case will not suffice. On the other hand, if you have a concrete suggestion for new models (quantum kernels in QSVM, qlayers in QNN, penalty layers in QAOA) then it might be relevant.
@Sunitha618 I am assigning you to this issue. Please note that we accept high-quality implementations to our repository and will be glad to accept a contribution that meets our standards.
Feel free to reach out to the community for any questions! Good luck!
Subject: Request for Extension for Classiq Issue Submission
We are honored to have been selected for publishing our issue Enhancing Genomics Via Quantum Kernels in Classiq. However, due to unforeseen circumstances, we require additional time to complete our work. Our topic demands extensive research, and ensuring high accuracy in implementation has proven to be more challenging than anticipated.To deliver a well-researched and high-quality submission, we kindly request an extension of one more week. This additional time will allow us to refine our work and meet the expected standards.We appreciate your understanding and consideration. Please let us know if this extension is possible.
Best regards, Sunitha.S Issue #813
On Thu, 6 Mar 2025 at 18:25, TomerGoldfriend @.***> wrote:
Assigned #813 https://github.com/Classiq/classiq-library/issues/813 to @Sunitha618 https://github.com/Sunitha618.
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Hi @TaliCohn , we have created the pull request for the issue https://github.com/Classiq/classiq-library/pull/984