Multimodal machine learning models can exploit complementary information from multiple data modalities. MultiFIX (Multimodal Feature engIneering eXplainable artificial intelligence) is a framework designed to construct partially interpretable multimodal models, providing explanations for both modality-specific features and each modality its contribution to the final prediction. However, it was shown to not scale effectively for tasks with extreme joint-modality dependence.

This thesis proposes an alternative training strategy that integrates knowledge of the features to be engineered, expressed as feature targets that guide the learning process. The strategy improves upon baseline performance, even when the feature targets are non-ideal. Since ground-truth feature targets are typically unavailable in real-world settings, the feature targets are optimised using the Gene-pool Optimal Mixing Evolutionary Algorithm. The optimised feature targets, though only loosely aligned with the ground-truth features, enables the alternative training method to surpass baseline MultiFIX performance on a three-gated XOR task.

The same approach was evaluated on simpler tasks, such as the single XOR and AND problems, where it achieved slightly lower but still comparable performance to the already strong baselines. Results indicate that this computationally intensive approach is most beneficial for problems characterised by high joint-modality dependence and complex feature interactions. Interestingly, closer alignment between the optimised and ground-truth feature targets did not consistently lead to higher MultiFIX performance. Consequently, future improvements are likely to stem from refining how feature targets are integrated into the training process, rather than from further optimisation of the targets themselves.

Quantum sensing technology has emerged as a promising field with superior sensitivity and accuracy in magnetic field sensing compared to conventional technologies. This breakthrough offers significant prospects in the biomedical sector, particularly in the realm of medical imaging.

This project focuses on the implementation of quantum sensing using an array of single Nitrogen Vacancy (NV) centers combined with an array of Single Photon Avalanche Diodes (SPADs). SPADs are available in various technologies and forms. To identify the optimal SPAD for this specific application, different SPADs with diverse parameters are designed on a chip using standard 40 nm TSMC process. These parameters include active area, active area shape and metal shielding.

The research aims to validate and characterize the performance of these distinct SPADs. However, during the validation tests, it was observed that the SPADs did not meet the criteria. The presence of ESD diodes on the chip introduced a low resistance current path, thereby hindering the characterisation process of the SPADs. Solutions to fix this problem and improve the system are provided.

The documented validation methods include general chip testing, establishing the I-V curves of the SPADs and avalanche and quenching testing. In addition, the characterisation methods for junction capacitance, dark count rate (DCR), photon detection probability (PDP) and time jitter are given.

For characterizing DCR and PDP a readout system is designed. It consists of an analog-to-digital converter (ADC), FPGA and Arduino. The data is communicated continuously to a PC. The ADC is not fully tested. On the other hand, the FPGA and Arduino are tested and verified to be sufficient for the DCR measurements. The readout system is too inaccurate for PDP measurements. A recommendation on how to make the readout system sufficient for PDP is given.