Variational Quantum Circuits Demonstrate Performance Gains with Rotational Gate Choice

Variational Quantum Machine Learning holds immense promise for future applications, and researchers are actively exploring ways to optimise its performance. Antonio Tudisco from Politecnico di Torino, Andrea Marchesin from Aalto University, and Maurizio Zamboni from Politecnico di Torino, along with their colleagues, investigate how different methods of loading data into quantum circuits affect the accuracy of these learning models. Their work centres on comparing two primary encoding strategies, amplitude and angle encoding, and assessing the impact of specific rotational gates within these methods. The team demonstrates that, even with identical model structures, the choice of encoding can lead to substantial differences in classification performance, ranging from 10% to 41% on standard datasets like Wine and Diabetes, confirming that the data embedding acts as a crucial hyperparameter for Variational Quantum Circuits. This research highlights the importance of careful consideration when designing quantum machine learning models, offering valuable insights for improving their overall effectiveness.

The study explores how these encoding strategies impact the ability of VQML models to learn from data and make accurate predictions. Researchers compared angle and amplitude encoding techniques for representing classical data as quantum states, implementing and testing these methods within various VQML architectures using parameterized quantum circuits. The models were evaluated on benchmark datasets, including Wine and Diabetes, and performance was measured using metrics like accuracy and balanced accuracy.

Experiments were conducted using PennyLane, a Python library for quantum machine learning, and utilized Intel Xeon Gold processors for computation. The results demonstrate that the choice of encoding strategy significantly impacts VQML model performance, with no single encoding universally superior; the optimal choice depends on the specific dataset and model architecture. Angle encoding generally performs well, especially when combined with appropriate circuit designs, and is relatively straightforward to implement. Amplitude encoding can potentially represent more complex data relationships, but often requires more qubits and is more susceptible to noise.

Data preprocessing techniques, such as Principal Component Analysis (PCA), can improve the performance of both encoding methods. The structure of the quantum circuit, particularly the use of strongly entangling layers, plays a crucial role in the model’s expressibility and ability to learn. The study systematically analyzed various Angle encoding mechanisms, benchmarking them while maintaining a consistent model topology and controlling for the number of qubits and features processed through Principal Component Analysis. Importantly, the research suggests that there isn’t a single “best” encoding strategy applicable to all datasets; the optimal approach appears to be dataset-dependent, necessitating a benchmarking process to identify the most effective embedding technique for a specific problem.

Encoding Choice Drives VQC Classification Performance

This work confirms that the encoding strategy functions as a crucial hyperparameter within VQC models, influencing not only accuracy but also training and evaluation times. While the use of re-uploading techniques can further enhance performance, the core encoding method remains a primary determinant of model effectiveness. Researchers acknowledge that reducing the number of features through PCA may have inherently disadvantaged overall model performance, but this was a necessary step to ensure fair comparisons between different encoding strategies.