Authors: Nicola Franco, Marie Kempkes, Jakob Spiegelberg, Jeanette Miriam Lorenz

Published on: July 25, 2024

Impact Score: 7.0

Arxiv code: Arxiv:2407.18021

Summary

• What is new: The paper introduces an innovative approach of integrating Grover’s algorithm with quantum randomized smoothing to achieve a quadratic sampling advantage over classical methods.
• Why this is important: Ensuring the robustness and efficiency of quantum algorithms as quantum machine learning develops rapidly.
• What the research proposes: Matching data encoding and perturbation modeling approaches in quantum randomized smoothing to attain robust certificates and using constrained k-distant Hamming weight perturbations as a noise distribution.
• Results: The proposed framework effectively demonstrates the quadratic sample reduction advantage, especially with larger sample sizes, on a time series classification task using a Bag-of-Words pre-processing solution.

Technical Details

Technological frameworks used: Quantum randomized smoothing framework integrating Grover’s algorithm

Models used: Grover’s algorithm, k-distant Hamming weight perturbations

Data used: Time series data for classification using Bag-of-Words pre-processing

Potential Impact

Quantum Computing, Machine Learning, Data Science sectors; companies involved in advanced analytics and quantum computing could benefit significantly.

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