EXPLORATION OF AMPLITUDE CODING CAPACITIES FOR Q-ML MODEL

Unang Achlison; Dendy Kurniawan; Toni Wijanarko Adi Putra; Siswanto Siswanto

doi:10.55606/jeei.v2i3.916

Unang Achlison Universitas Sains dan Teknologi Komputer
Dendy Kurniawan Universitas Sains dan Teknologi Komputer
Toni Wijanarko Adi Putra Universitas Sains dan Teknologi Komputer
Siswanto Siswanto Universitas Sains dan Teknologi Komputer Semarang

DOI: https://doi.org/10.55606/jeei.v2i3.916

Keywords: Machine Learning, Amplitude Coding, Quantum Machine Learning, Quantum Computation, TensorFlow Quantum

Abstract

Quantum computing implements computation adopting environmental phantasm and the foundation of quantum mechanics to clear up the issues. This design of calculation has been demonstrated to serve the acceleration of some modern processing issues. Current evolution in quantum technology is emerging, and the application of learning design to this current instrument is developing. With enough prospects, the application of quantum development in the area of Machine Learning has come clear.

This research develops a TensorFlow Quantum (TF-Q) software framework model for machine learning functions. The two models advanced the application of material coding techniques from amplitude coding to constructing a case in the quantum learning model. This study aimed to explore the scope of amplitude coding to serve enhanced case establishment in learning techniques and in-depth investigation of data sets that bring insight into the practice data adopting the “Variational Quantum Classifier” (VQ-C). The emergence of this current method raises the investigation of how best this tool can be adopted, the aim is to provide several analysis explanations for the element of quantum machine learning that can be applied given the constraints of the actual device.

The results of this study indicate there are clear advantages to adopting amplitude coding over another technique as demonstrated by adopting the combination of quantum-humanistic neural networks in TF-Q. In addition, the different preprocessing steps can generate more aspect-affluent data while using VQ-C the no-charge lunch assumption dominance for quantum learning technique for humanistic models. The material even though conceal in quantum by unadvanced data preparation steps but involves new ways of understanding and appreciating these new methods. Future studies will lack expansion into multi-type of analysis models that are sufficiently advanced to be relevant in work similar to this.

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