K-Means Clustering on Noisy Intermediate Scale Quantum Computers
arXiv:1909.12183
Abstract
Real-time clustering of big performance data generated by the telecommunication networks requires domain-specific high performance compute infrastructure to detect anomalies. In this paper, we evaluate noisy intermediate-scale quantum (NISQ) computers characterized by low decoherence times, for K-means clustering and propose three strategies to generate shorter-depth quantum circuits needed to overcome the limitation of NISQ computers. The strategies are based on exploiting; i) quantum interference, ii) negative rotations and iii) destructive interference. By comparing our implementations on IBMQX2 machine for representative data sets, we show that NISQ computers can solve the K-means clustering problem with the same level of accuracy as that of classical computers.
References in corpus (8)
- Scikit-learn: Machine Learning in Python
- Quantum Computing in the NISQ era and beyond
- Elementary gates for quantum computation
- Quantum support vector machine for big data classification
- Quantum algorithms for supervised and unsupervised machine learning
- A Quantum Algorithm for Finding the Minimum
- Quantum machine learning for data scientists
- A Case for Variability-Aware Policies for NISQ-Era Quantum Computers
Cited by in corpus (7)
- Quantum computing for finance
- Quantum Machine Learning for Finance
- Quantum anomaly detection in the latent space of proton collision events at the LHC
- Practical Quantum K-Means Clustering: Performance Analysis and Applications in Energy Grid Classification
- How quantum computing can enhance biomarker discovery
- Balanced k-Means Clustering on an Adiabatic Quantum Computer
- Max-cut Clustering Utilizing Warm-Start QAOA and IBM Runtime