Quantum-Assisted Clustering Algorithms for NISQ-Era Devices
This work addresses the problem of finding near-term applications for NISQ-era quantum devices in machine learning, offering incremental advancements in clustering algorithms.
The paper tackles the challenge of leveraging noisy intermediate-scale quantum (NISQ) devices for practical problems by developing hybrid quantum-classical clustering algorithms that require at most logarithmic qubits in data size, resulting in performance and/or runtime improvements over classical methods.
In the NISQ-era of quantum computing, we should not expect to see quantum devices that provide an exponential improvement in runtime for practical problems, due to the lack of error correction and small number of qubits available. Nevertheless, these devices should be able to provide other performance improvements, particularly when combined with existing classical machines. In this article, we develop several hybrid quantum-classical clustering algorithms that can be employed as subroutines on small, NISQ-era devices. These new hybrid algorithms require a number of qubits that is at most logarithmic in the size of the data, provide performance improvement and/or runtime improvement over their classical counterparts, and do not require a black-box oracle. Consequently, we are able to provide a promising near-term application of NISQ-era devices.