  Ethereum is one of the most valuable blockchain networks in terms of the
total monetary value locked in it, and arguably been the most active network
where new blockchain innovations in research and applications are demonstrated.
But, this also leads to Ethereum network being susceptible to a wide variety of
threats and attacks in an attempt to gain unreasonable advantage or to
undermine the value of the users. Even with the state-of-art classical ML
algorithms, detecting such attacks is still hard. This motivated us to build a
hybrid system of quantum-classical algorithms that improves phishing detection
in financial transaction networks. This paper presents a classical ensemble
pipeline of classical and quantum algorithms and a detailed study benchmarking
existing Quantum Machine Learning algorithms such as Quantum Support Vector
Machine and Variational Quantum Classifier. With the current generation of
quantum hardware available, smaller datasets are more suited to the QML models
and most research restricts to hundreds of samples. However, we experimented on
different data sizes and report results with a test data of 12K transaction
nodes, which is to the best of the authors knowledge the largest QML experiment
run so far on any real quantum hardware. The classical ensembles of
quantum-classical models improved the macro F-score and phishing F-score. One
key observation is QSVM constantly gives lower false positives, thereby higher
precision compared with any other classical or quantum network, which is always
preferred for any anomaly detection problem. This is true for QSVMs when used
individually or via bagging of same models or in combination with other
classical/quantum models making it the most advantageous quantum algorithm so
far. The proposed ensemble framework is generic and can be applied for any
classification task
