Reception & Poster Session

 stimulating both the aerobic and anaerobic metabolic pathways of the fermentative bacteria to enhance their growth. Experimental studies on E Coli. reported that the gene expressions of aerobic terminal enzymes (cytochrome bd oxidases and cytochrome bo oxidases), were at their highest under micro-aerobic conditions. However, research on the interaction in enzymes between the aerobic and anaerobic metabolism remains limited. In this work, we conceptualised the bacterial consortium as a decision-maker, who receives external information and responds accordingly. We then used the principles of quantum mechanics to formulate a mathematical model describing the bacterial actions. This model was parameterised by a probabilistic interference term indicating the interaction between the aerobic and anaerobic metabolic pathways. Finally, we incorporated empirical data into the model to determine the interference term. The result showed consistent probabilistic interference (0.2 to 0.4) across all enzymes involved, highlighting their similarity in interaction levels between aerobic and anaerobic pathways during micro-aeration. Although the current model requires further investigations for full-scale reactor simulation, it possesses several advantages. First, it establishes a new quantity applicable to characterise highly stochastic biological systems. Second, it can connect gene, protein and metabolites productions to generate a more comprehensive understanding of microbiology. Lastly, rooted in quantum information theory, it holds potential for implementation on fault-tolerant quantum computers in the future. Overall, this work introduces a transformative dimension to the future of biochemical engineering, promising significant advancements in the field.

 stochastic process using P. putida NBUS12 polyhydroxylalkanoate (PHA) biosynthesis as a case study, and established a framework towards utilizing the power of quantum computing to simulate biological phenomena.

 Aspen Plus, a process simulator, with Activity Browser, an open-source Life Cycle Assessment (LCA) software. Conducting multiple sensitivity analyses, our automated interface framework generates preliminary ReCiPE indicators for LCA. Additionally, we compare classical Support Vector Regression (SVR) models with quantum SVR models. The approach involves training an SVM model using Python's scikit-learn package. Classical data points are converted into quantum data points using a parametrized quantum circuit. The SVM Kernel is then built and trained as a classical SVM using Qiskit packages for quantum machine learning. Preliminary results demonstrate quantum SVR's capabilities in enhancing efficient, accurate, and sustainable automated process simulation optimization for next-generation design and assessment approaches.

An important tool to optimize operator conditions and improve PHA yields is metabolic modeling using omics data from next-generation sequencing. This method evaluates how metabolic pathways can be exploited via reactor design to select for the phenotype of interest while circumventing the need to rely on thermodynamic and kinetic parameters that are often system dependent and incomplete (Zhou et al., 2021). Thus, data-driven optimization using the underlying biological information may prove key to improve the production scale of biotechnological systems. Machine learning (ML) applications have led to significant advances in optimization problems due to their performance in handling complex big data (Cruz et al., 2021). Thus, we developed classical machine learning (random forest and artificial neural network classification models) and variational quantum circuits to elucidate the gene expression pattern underlying PHA bioplastic production in a pure culture, laboratory-scale system using Pseudonomas putida. We utilize transcriptomics data to compare model performance between classical and quantum computing and the biological insights derived from each method. Classical computing methods may struggle to capture the full spectrum of interactions and dynamics within the gene regulatory network due to their limitations in handling the immense computational demands. However, quantum computing, with its unique capacity to feature entanglement and superposition of complex systems, may provide a more comprehensive and accurate understanding of gene expression in the context of PHA production (Weidner et al., 2023; Li et al., 2018). By comparing classical and quantum computing techniques, we further develop a methodology with which data-driven models can be evaluated to optimize PHA production for industrial-scale impact.

quantum feature maps for developing enhanced quantum classifiers using the quantum kernel method, extending the application of quantum computers to machine learning (Havlíček, V., Córcoles, A.D., Temme, K. et al., 2019). We have developed enhanced quantum kernels based on six different quantum feature maps and demonstrated proof of concept with three different 2-dimensional datasets for binary classification tasks (Suzuki, Y., Yano, H., Gao, Q. et al., 2020). Our work aims to showcase the significance of quantum feature maps and the role of hyperparameters in developing quantum support vector classifiers (QSVC) for various benchmark datasets. We have conducted a comprehensive analysis of hyperparameters, including quantum gates and rotation factors, to demonstrate their influence on data distributions, thereby offering enhanced analytical quantum advantages. Furthermore, we demonstrate the applicability and advantages of QSVC when exposed to real-world datasets. We have applied the method to brain data, which is highly complex and multivariable, containing spatio-temporal information. Spiking neural networks are employed to extract features from the EEG data, and the resulting features are utilized to develop the quantum classifier (Kasabov, 2014). The methodology yields promising results compared to various other classical machine learning models; thereby providing the quantum advantage. Finally, we claim to uniquely represent the hybrid approach for bridging neuromorphic and quantum computing, capable of providing an energy-efficient computational paradigm in the future.

(TS-GANs) in a hybrid approach. We propose that Quantum Generative Artificial Intelligence (QGAI) can rapidly and accurately generate synthetic data. This capability will be instrumental in scaling up challenging tasks, including the global optimization of bioprocess systems. Inspired by recent advancements in Quantum Drug Design, we explore a novel approach. We feed latent-space data from a GAN into a Parameterized Quantum Circuit (PQC). The PQC’s output informs the generator, enhancing the learning of the prior distribution. This iterative process improves the discriminator’s ability to distinguish between passing and failing data generated by the model. Currently, we employ the DoppleGANger model to generate synthetic data based on available process data. However, we anticipate that incorporating a PQC will allow us to create accurate synthetic data even from sparse observations. This advancement promises faster training times, reduced computational costs, and higher-quality outputs. Ultimately, it benefits industries seeking Generative Enhanced Optimization (GEO) for their industrial bioprocesses.