  Quantum machine learning (QML) is promising for potential speedups and
improvements in conventional machine learning (ML) tasks (e.g.,
classification/regression). The search for ideal QML models is an active
research field. This includes identification of efficient classical-to-quantum
data encoding scheme, construction of parametric quantum circuits (PQC) with
optimal expressivity and entanglement capability, and efficient output decoding
scheme to minimize the required number of measurements, to name a few. However,
most of the empirical/numerical studies lack a clear path towards scalability.
Any potential benefit observed in a simulated environment may diminish in
practical applications due to the limitations of noisy quantum hardware (e.g.,
under decoherence, gate-errors, and crosstalk). We present a scalable
quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired
by classical deep neural network architectures. In DeepQMLP, stacked shallow
Quantum Neural Network (QNN) models mimic the hidden layers of a classical
feed-forward multi-layer perceptron network. Each QNN layer produces a new and
potentially rich representation of the input data for the next layer. This new
representation can be tuned by the parameters of the circuit. Shallow QNN
models experience less decoherence, gate errors, etc. which make them (and the
network) more resilient to quantum noise. We present numerical studies on a
variety of classification problems to show the trainability of DeepQMLP. We
also show that DeepQMLP performs reasonably well on unseen data and exhibits
greater resilience to noise over QNN models that use a deep quantum circuit.
DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during
inference under noise than QMLP.
