Fusion of classical and quantum data can improve machine learning predictions
When do quantum views help? Complementary classical-quantum representations in multi-view fusion
Artificial Intelligence
Summary
Machine learning models can sometimes do better when they combine different types of data. This paper looks at mixing regular data with data that comes from quantum simulations. The researchers show that when these two types of data capture different useful clues, putting them together helps predict things more accurately. They also find that similar improvements happen when only classical data with matching properties is used, meaning the key is using complementary information. Finally, they test when and how this combination helps and when it does not.
What this means in practice
- •For data engineers: Improve prediction accuracy by combining classical and simulated quantum data representations that capture complementary patterns in datasets.
- •For machine learning practitioners: Design fusion strategies that align representation type and fusion methods to enhance classification tasks depending on target data relationships.
Authors
Azadeh Alavi, Fatemeh Kouchmeshki, Hossein Akhoundi, Yongli Re, Abdolrahman Alavi, Muhammad Usman
Abstract
Recent studies have reported promising results from quantum machine learning in drug discovery, healthcare, materials science and cybersecurity. Research includes comparisons with classical machine learning algorithms and the development of sequential hybrid pipelines. We propose that fusion can improve prediction when classical and quantum representations capture complementary predictive relationships. Our classical-quantum multi-view framework preserves the full classical input alongside simulated quantum features. Concatenation and attention combine the views; single-view comparisons, replacement and masking controls assess both views' contributions. Controlled angular-polynomial experiments distinguish targets adequately represented by a single view from those that depend on complementary angular and polynomial relationships. For these mixed targets, fusion improves prediction by combining the relevant feature sets, while matched classical representations recover the same benefit, linking the improvement to their complementary predictive content. Controlled classification tests further show that gains depend on alignment between the chosen representation and the target, and on a suitable fusion rule. Benchmark validation shows improved mean predictive performance over classical baselines on Wine, Wisconsin Diagnostic Breast Cancer, three-class Fashion-MNIST and Steel Plates Faults within the evaluated protocols. Classical replacement and masking controls remain competitive, classical baselines lead on Digits and Covertype, and outcomes with missing or corrupted views depend on the available inputs. These findings support evaluating quantum representations for their complementary predictive contribution and matching the representation and fusion strategy to the target relationships.