# Practical application improvement to Quantum SVM: theory to practice

@article{Park2020PracticalAI, title={Practical application improvement to Quantum SVM: theory to practice}, author={Jae-eun Park and Brian Quanz and Stephen P Wood and Heather Higgins and Ray Harishankar}, journal={ArXiv}, year={2020}, volume={abs/2012.07725} }

Quantum machine learning (QML) has emerged as an important area for Quantum applications, although useful QML applications would require many qubits. Therefore our paper is aimed at exploring the successful application of the Quantum Support Vector Machine (QSVM) algorithm while balancing several practical and technical considerations under the Noisy Intermediate-Scale Quantum (NISQ) assumption. For the quantum SVM under NISQ, we use quantum feature maps to translate data into quantum states…

## 19 Citations

### Optimal quantum kernels for small data classification

- Computer ScienceArXiv
- 2022

An algorithm for constructing quantum kernels for support vector machines that adapts quantum gate sequences to data and the performance of the resulting quantum models for classification problems with a small number of training points significantly exceeds that of optimized classical models with conventional kernels.

### Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines

- Computer ScienceArXiv
- 2022

The authors prove that a PROMISEBQP-complete problem can be expressed by variational quantum classifiers and quantum support vector machines, meaning that a quantum advantage can be achieved for all ML classification problems that cannot be classically solved in polynomial time.

### Structural risk minimization for quantum linear classifiers

- Computer ScienceQuantum
- 2023

This paper proves that two model parameters closely control the models' complexity and therefore its generalization performance, and gives rise to new options for structural risk minimization for QML models.

### Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

- Computer Science, PhysicsComput. Softw. Big Sci.
- 2021

This work investigates the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance, and shows an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations.

### Master Computer Science Near-term quantum algorithms for regression, overﬁtting analysis and regularization

- Computer Science
- 2022

This work shows parametrized quantum circuits can be e-ectively applied to real-world regression problems and provides useful insights and methods to improve the generalization performance of these circuits.

### Boosting Method for Automated Feature Space Discovery in Supervised Quantum Machine Learning Models

- Computer Science
- 2022

A boosting approach for building ensembles of QSVM models and assess performance improvement across multiple datasets is proposed, derived from the best ensemble building practices that worked well in traditional machine learning and thus should push the limits of quantum model performance even further.

### Generating quantum feature maps for SVM classifier

- Computer Science
- 2022

Two methods of generating quantum feature maps for quantum-enhanced support vector machine, a classiﬁer based on kernel method, by which they can access high dimensional Hilbert space eﬃciently are presented and compared.

### Eﬀects of ﬁtness function in genetically auto-generated quantum feature maps

- Computer Science
- 2022

A multi-objectiveness function using penalty method, which incorporates maximizing the accuracy of classiﬁcation and minimizing the gate cost of quantum feature map’s circuit as the original method is presented.

### Genetically auto-generated quantum feature maps

- Computer Science
- 2022

We present a method using genetic algorithm to automatically generate quantum feature maps for quantum-enhanced support vector machine, a classiﬁer based on kernel method, by which we can access high…

### OptiPauli: An algorithm to find a near-optimal Pauli Feature Map for Quantum Support Vector Classifiers

- Computer Science2022 IEEE International Conference on Quantum Computing and Engineering (QCE)
- 2022

This work proposes an algorithm that finds a near-optimal Pauli Feature Map by solving several sub-problems with varying numbers of decision variables and their constraints, using genetic algorithm to solve each sub-problem, and then selecting the best out of all the near-OptimalPauli Feature Maps.

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