Quantum-enhanced filter: QFilter

Fourier Series Weight in Quantum Machine Learning

“…QML tries to take advantage of the classical machine learning does best and what it costs, such as distance calculation (inner product), passing it onto a quantum computer that can compute it natively in the Hilbert vector space. In this era of extensive classical data and few qubits, the most common use is to design machine learning algorithms for classical data analysis running on a quantum computer, i.e., quantum-enhanced machine learning [3,[24][25][26][27][28][29][30]. The usual supervised learning processes within quantum machine learning can be defined as follows:…”

“…This critical examination provides a balanced perspective of our work and paves the way for future research directions. The current state of quantum computing hardware [36,37], is at the forefront of our limitations. The qubits' scarcity and susceptibility to errors and decoherence significantly cap the complexity and size of the models we can reliably implement.…”