Variational Quantum Approximate Spectral Clustering for Binary
Clustering Problems
- URL: http://arxiv.org/abs/2309.04465v1
- Date: Fri, 8 Sep 2023 17:54:42 GMT
- Title: Variational Quantum Approximate Spectral Clustering for Binary
Clustering Problems
- Authors: Hyeong-Gyu Kim, Siheon Park, June-Koo Kevin Rhee
- Abstract summary: We introduce the Variational Quantum Approximate Spectral Clustering (VQASC) algorithm.
VQASC requires optimization of fewer parameters than the system size, N, traditionally required in classical problems.
We present numerical results from both synthetic and real-world datasets.
- Score: 0.7550566004119158
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In quantum machine learning, algorithms with parameterized quantum circuits
(PQC) based on a hardware-efficient ansatz (HEA) offer the potential for
speed-ups over traditional classical algorithms. While much attention has been
devoted to supervised learning tasks, unsupervised learning using PQC remains
relatively unexplored. One promising approach within quantum machine learning
involves optimizing fewer parameters in PQC than in its classical counterparts,
under the assumption that a sub-optimal solution exists within the Hilbert
space. In this paper, we introduce the Variational Quantum Approximate Spectral
Clustering (VQASC) algorithm - a NISQ-compatible method that requires
optimization of fewer parameters than the system size, N, traditionally
required in classical problems. We present numerical results from both
synthetic and real-world datasets. Furthermore, we propose a descriptor,
complemented by numerical analysis, to identify an appropriate ansatz circuit
tailored for VQASC.
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