# Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing

arXiv: https://arxiv.org/abs/2310.01443 · HTML: https://mlsanity.com/paper/2310.01443/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [A Practical Quantum Instruction Set Architecture](https://mlsanity.com/paper/1608.03355/) (2016)
- [Full-Blind Delegating Private Quantum Computation](https://mlsanity.com/paper/2002.00464/) (2020)
- [A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets](https://mlsanity.com/paper/2309.14366/) (2023)
- [A Quantum-based Database Query Scheme for Privacy Preservation in Cloud Environment](https://mlsanity.com/paper/2002.00192/) (2020)
- [Privacy-Preserving Quantum Two-Party Geometric Intersection](https://mlsanity.com/paper/2309.12605/) (2023)
- [Quantum Searchable Encryption for Cloud Data Based on Full-Blind Quantum Computation](https://mlsanity.com/paper/2309.13685/) (2023)

## Abstract (arXiv)

The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multi-classification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and Ry, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover-Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold τ. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to O(sqrt(M)), and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to O(sqrt(M)). Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.