Quantum deep learning for brain informatics

Nabil Anan Orka, Md. Abdul Awal, Píetro Lió +3 more

Abstract Neuroinformatics involves replicating and detecting intricate brain activities through computational models, where deep learning plays a foundational role. Our systematic review explores quantum deep learning (QDL), an emerging deep learning sub-field, to assess whether quantum-based approaches outperform classical approaches in brain data learning tasks. This review is a pioneering effort to compare these deep learning domains. In addition, we survey neuroinformatics and its various subdomains to understand the current state of the field and where QDL stands relative to recent advancements. Our statistical analysis of tumor classification studies (n = 16) reveals that QDL models achieved a mean accuracy of 0.9701 (95% CI 0.9533–0.9868), slightly outperforming classical models with a mean accuracy of 0.9650 (95% CI 0.9475–0.9825). We observed similar trends across Alzheimer’s diagnosis, stroke lesion detection, cognitive state monitoring, and brain age prediction, with QDL demonstrating better performance in metrics such as F1-score, dice coefficient, and RMSE. Our findings, paired with prior documented quantum advantages, highlight QDL’s promise in healthcare applications as quantum technology evolves. Our discussion outlines existing research gaps with the intent of encouraging further investigation in this developing field.

Abu Kamruzzaman, Yousef Salamah Alhwaiti, Avery M. Leider +1 more

Dipesh Niraula, Jamalina Jamaluddin, Julia M. Pakela +1 more

Salah A. M. Elshourbagy

Dukuru Chiranjevi, Anisha Mishra, Ranjan Kumar Behera +1 more

Hoang-Quan Nguyen, Xuan-Bac Nguyen, Hugh Churchill +4 more

Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usually introduced in a traditional learning paradigm missing the ability to learn the connectivities between brain regions. Meanwhile, the quantum computing theory offers a new paradigm for designing deep learning models. Motivated by the connectivities in the brain signals and the entanglement properties in quantum computing, we propose a novel Quantum-Brain approach, a quantum-inspired neural network, to tackle the vision-brain understanding problem. To compute the connectivity between areas in brain signals, we introduce a new Quantum-Inspired Voxel-Controlling module to learn the impact of a brain voxel on others represented in the Hilbert space. To effectively learn connectivity, a novel Phase-Shifting module is presented to calibrate the value of the brain signals. Finally, we introduce a new Measurement-like Projection module to present the connectivity information from the Hilbert space into the feature space. The proposed approach can learn to find the connectivities between fMRI voxels and enhance the semantic information obtained from human perceptions. Our experimental results on the Natural Scene Dataset benchmarks illustrate the effectiveness of the proposed method with Top-1 accuracies of 95.1% and 95.6% on image and brain retrieval tasks and an Inception score of 95.3% on fMRI-to-image reconstruction task. Our proposed quantum-inspired network brings a potential paradigm to solving the vision-brain problems via the quantum computing theory.