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Implementation of variational Quantum Machine Learning (QML) algorithms onNoisy Intermediate-Scale Quantum (NISQ) devices is known to have issues relatedto the high number of qubits needed and the noise associated with multi-qubitgates. In this paper, we propose a variational QML algorithm using a dressedquantum network to address these issues. Using the "super compressed encoding"scheme that we follow here, the classical encoding layer in our dressed networkdrastically scales down the input-dimension, before feeding the input to thevariational quantum circuit. Hence, the number of qubits needed in our quantumcircuit goes down drastically. Also, unlike in most other existing QMLalgorithms, our quantum circuit consists only of single-qubit gates, making itrobust against noise. These factors make our algorithm suitable forimplementation on NISQ hardware. To support our argument, we implement ouralgorithm on real NISQ hardware and thereby show accurate classification usingpopular machine learning data-sets like Fisher's Iris, Wisconsin's BreastCancer (WBC), and Abalone. Then, to provide an intuitive explanation for ouralgorithm's working, we demonstrate the clustering of quantum states, whichcorrespond to the input-samples of different output-classes, on the Blochsphere (using WBC and MNIST data-sets). This clustering happens as a result ofthe training process followed in our algorithm. Through this Bloch-sphere-basedrepresentation, we also show the distinct roles played (in training) by theadjustable parameters of the classical encoding layer and the adjustableparameters of the variational quantum circuit. These parameters are adjustediteratively during training through loss-minimization.

Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation