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Quantum machine learning (QML) is emerging as an application of quantumcomputing with the potential to deliver quantum advantage, but its realisationfor practical applications remains impeded by challenges. Amongst those, a keybarrier is the computationally expensive task of encoding classical data into aquantum state, which could erase any prospective speed-ups over classicalalgorithms. In this work, we implement methods for the efficient preparation ofquantum states representing encoded image data using variational, genetic andmatrix product state based algorithms. Our results show that these methods canapproximately prepare states to a level suitable for QML using circuits twoorders of magnitude shallower than a standard state preparation implementation,obtaining drastic savings in circuit depth and gate count without undulysacrificing classification accuracy. Additionally, the QML models trained andevaluated on approximately encoded data display an increased robustness toadversarially generated input data perturbations. This partial alleviation ofadversarial vulnerability, possible due to the "drowning out" of adversarialperturbations while retaining the meaningful large-scale features of the data,constitutes a considerable benefit for approximate state preparation inaddition to lessening the requirements of the quantum hardware. Our results,based on simulations and experiments on IBM quantum devices, highlight apromising pathway for the future implementation of accurate and robust QMLmodels on complex datasets relevant for practical applications, bringing thepossibility of NISQ-era QML advantage closer to reality.

Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning