A recent study by researchers from CSIRO and the University of Melbourne has made progress in quantum machine learning, a field aimed at achieving quantum advantage to outperform classical machine learning. Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. This research was published Sept.12 in Intelligent Computing, a Science Partner Journal.

The team's results, validated through both simulations and experiments on IBM quantum devices, show that their innovative encoding methods reduced circuit depth by a factor of 100 on average compared to traditional approaches while achieving similar classification accuracies. The findings offer an exciting new pathway for the practical application of quantum machine learning on current quantum devices. Looking forward, the team aims to scale these models for larger, more complex datasets and explore further optimizations in quantum state encoding and quantum machine learning architecture design.

One of the key obstacles to efficient quantum machine learning has been encoding classical data into quantum states, a computationally challenging task requiring deeply entangled circuits. To overcome this, the team introduced three encoding methods that approximate the quantum state of the data using much shallower circuits. These methods -- matrix product state, genetic, and variational algorithms -- maintained classification accuracy on the MNIST image dataset and two others while enhancing resilience against adversarial data manipulation.

Each method uniquely approximated quantum state encoding classical data to enable efficient state preparation:

The work aligns with broader goals in quantum machine learning to build efficient, reliable quantum models for areas such as image recognition, cybersecurity, and complex data analysis. The reduction in circuit depth is critical for achieving practical quantum machine learning on current devices, which are often limited by gate fidelity and qubit count. The increased robustness of these models to adversarial attacks opens up new possibilities for secure quantum machine learning applications in sectors where resilience to tampering is essential.