Quantum Accelerates Fraud Detection Machine Learning

Machine learning on classical computers has significantly improved financial fraud detection capability. Speaking on the “Qubits and neurons to turbocharge the world of finance” panel at an IBM quantum and AI event last week, IBM quantum applications researcher Christa Zoufal explained that quantum computers can select the features that best train the machine learning model.

“In the financial industry, we have quite a lot of equipment and problems that have a very complex structure underneath, and with this complex structure it can get hard for classical algorithms to solve them,” she said.

“If you want to do fraud detection, for example, you can have a lot of data with features that

Feature selection is a difficult problem and the number of solutions in the problem scales exponentially with the number of features that need to be considered. Most classical machine learning algorithms can, at most, look at correlations between two features, and even if there are 1,000 features, classical algorithms will look at correlations between one pair of these features at a time.

“What you can do with quantum machine learning in this particular context is run the algorithm using the quantum circuit and look at all of the correlations at the same time. In some instances, we have already found that with this quantum algorithm we can get a better picture with very simple models,” said Zoufal.

Zoufal said that although this is an early study, it shows that feature selection can be performed better on a quantum computer. Those selected features can be passed into classical machine learning algorithms to what consequences changing the value of these features has on detecting potential fraud.

She added that IBM’s experiment found a measure of how much information can be represented by a quantum neural network and how much by a classical neural network.

“We have some indications that the quantum neural networks can present more information with the same set of parameters than a comparable classical neural network,” Zoufal said.

“The take-home message is there are various aspects of quantum computing that can give us benefit in the quantum machine learning field. Now we need to identify the things that are difficult for classical computers and see where the quantum computers can get us.”