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