• Corpus ID: 229158635

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… 

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References

SHOWING 1-10 OF 20 REFERENCES

Supervised learning with quantum-enhanced feature spaces

Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.

Towards quantum machine learning with tensor networks

A unified framework is proposed in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization.

Quantum Machine Learning in Feature Hilbert Spaces.

This Letter interprets the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space and shows how it opens up a new avenue for the design of quantum machine learning algorithms.

Quantum Machine Learning

This review focuses on the supervised classification quantum algorithm of nearest centroid, presented in [11], which helps to overcome the main bottleneck of the algorithm: calculation of the distances between the vectors in highly dimensional space.

Accelerated Variational Quantum Eigensolver.

A generalized VQE algorithm is proposed that interpolates between these two regimes via a free parameter α∈[0,1], which can exploit quantum coherence over a circuit depth of O(1/ε^{α}) to reduce the number of samples to O( 1/ε-α) and give a new routine for expectation estimation under limited quantum resources that is of independent interest.

Advances in quantum machine learning

There are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation, and the field's outlook is generally positive, showing significant promise.

A variational eigenvalue solver on a photonic quantum processor

The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future.

Quantum algorithms for some hidden shift problems

The hidden coset problem is defined, which generalizes the hidden shift problem and the hidden subgroup problem and provides a unified way of viewing the ability of the Fourier transform to capture subgroup and shift structure.

Multiple Kernel Learning Algorithms

Overall, using multiple kernels instead of a single one is useful and it is believed that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels.

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

  • P. Shor
  • Computer Science
    SIAM Rev.
  • 1999
Efficient randomized algorithms are given for factoring integers and finding discrete logarithms, two problems that are generally thought to be hard on classical computers and that have been used as the basis of several proposed cryptosystems.