💡[Feature]: Quantum Circuit Probability Prediction using ML #1489
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Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. If you have any questions reach out to LinkedIn. Your contributions are highly appreciated! 😊 Note: I Maintain the repo issue twice a day, or ideally 1 day, If your issue goes stale for more than one day you can tag and comment on this same issue. You can also check our CONTRIBUTING.md for guidelines on contributing to this project. |
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If possible please assign me this project as its quite efficient , interesting to carry out and effective in the field of ML Researc. |
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we are not accepting new documentation like this, see the pinned issues, |
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Hello @Panchadip-128! Your issue #1489 has been closed. Thank you for your contribution! |
Is there an existing issue for this?
Feature Description
The Quantum Circuit Probability Predictor is a machine learning-based application designed to predict the probability of measuring a specific quantum state after applying a series of quantum gates to a qubit. Leveraging the principles of quantum mechanics and classical machine learning, this project aims to create a robust model that accurately estimates the probabilities associated with different quantum states resulting from varied input parameters.
Use Case
The following are the variety of use cases for the model:
Quantum Key Distribution (QKD): Enhances secure key exchange in cryptographic protocols.
Quantum Classification: Improves accuracy in machine learning classifiers.
Molecular Simulation: Predicts chemical reactions and material properties.
Quantum Approximate Optimization Algorithm (QAOA): Solves combinatorial optimization problems.
Game Theory Models: Analyzes strategies in quantum games.
Quantum Auctions: Optimizes bidding strategies in auction scenarios.
Quantum Error Correction: Enhances fault tolerance in quantum systems.
Biological Sensing: Improves accuracy of medical sensors through quantum measurements.
Genomic Data Analysis: Identifies genetic variations impacting diseases.
Generative Quantum Models: Generates new data samples similar to classical GANs.
Reinforcement Learning: Enhances decision-making processes in AI models.
Benefits
Following are the applications where Quantum Circuit forms the base:
Speed: Quantum circuits can process information in parallel, potentially offering exponential speedup for specific computations over classical algorithms.
Enhanced Accuracy: Quantum probability distributions can capture complex correlations, improving the accuracy of predictions in tasks like classification and simulation.
Resource Efficiency: Quantum algorithms may require fewer resources (like memory and time) for certain computations, optimizing performance.
Handling Complex Systems: Quantum circuits excel in modeling and predicting behaviors in complex systems, such as molecular interactions or financial markets.
Optimization: Quantum circuits can efficiently tackle combinatorial optimization problems, yielding better solutions than classical methods.
Improved Cryptography: Leveraging quantum mechanics enhances security protocols through quantum key distribution and other cryptographic methods.
Robustness Against Noise: Quantum algorithms can be designed to be less sensitive to certain types of noise, improving performance in real-world applications.
Innovative Applications: Enables new applications in various fields, such as drug discovery, climate modeling, and artificial intelligence, that were previously infeasible with classical computing.
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