Epitopes are short antigenic peptide sequences which are recognized by antibodies or immune cell receptors. These are central to the development of immunotherapies, vaccines, and diagnostics. However, the rational design of synthetic epitope libraries is challenging due to the large combinatorial sequence space, $20^n$ combinations for linear epitopes of n amino acids, making screening and testing unfeasible, even with high throughput experimental techniques. In this study, we present a large language model, epiGPTope, pre-trained on protein data and specifically fine-tuned on linear epitopes, which for the first time can directly generate novel epitope-like sequences, which are found to possess statistical properties analogous to the ones of known epitopes. This generative approach can be used to prepare libraries of epitope candidate sequences. We further train statistical classifiers to predict whether an epitope sequence is of bacterial or viral origin, thus narrowing the candidate library and increasing the likelihood of identifying specific epitopes. We propose that such combination of generative and predictive models can be of assistance in epitope discovery. The approach uses only primary amino acid sequences of linear epitopes, bypassing the need for a geometric framework or hand-crafted features of the sequences. By developing a method to create biologically feasible sequences, we anticipate faster and more cost-effective generation and screening of synthetic epitopes, with relevant applications in the development of new biotechnologies.

This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication systems, particularly focusing on 5G networks and beyond. The application of QML and QI algorithms is investigated, comparing their performance with classical Machine Learning (ML) approaches. The present study employs a hybrid framework combining quantum and classical computing leveraging the strengths of QML and QI, without the penalty of quantum hardware availability. This is particularized for the optimization of the Quality of Experience (QoE) over cellular networks. The framework comprises an estimator for obtaining the expected QoE based on user metrics, service settings, and cell configuration, and an optimizer that uses the estimation to choose the best cell and service configuration. Although the approach is applicable to any QoE-based network management, its implementation is particularized for the optimization of network configurations for Cloud Gaming services. Then, it is evaluated via performance metrics such as accuracy and model loading and inference times for the estimator, and time to solution and solution score for the optimizer. The results indicate that QML models achieve similar or superior accuracy to classical ML models for estimation, while decreasing inference and loading times. Furthermore, potential for better performance is observed for higher-dimensional data, highlighting promising results for higher complexity problems. Thus, the results demonstrate the promising potential of QML in advancing network optimization, although challenges related to data availability and integration complexities between quantum and classical ML are identified as future research lines.

Multiverse Computing and Mila just announced a partnership designed advance artificial intelligence (AI) and machine learning (ML) using quantum computing and quantum-inspired methods. The partnership will also focus on developing new leaders in the high-tech fields of quantum computing and ML. Mila represents a global hub of scientific advancement in Montreal with approximately 1000 researchers specializing in AI and ML. While Mila researchers and students gain access to Multiverse quantum-inspired ML technology used by its customers in mobility, energy, life sciences and industry 4.0 segments, Multiverse will tap into the tensor networks and machine learning expertise at Mila. Tensor networks use models based on quantum physics and increase the speed and precision of training ML models.

Multiverse Computing, a leading developer of value-added quantum computing applications, and MILA, an artificial intelligence research institute specializing in deep learning, announced a new partnership on October 25 that will use quantum methods to advance AI and machine learning. The partnership will also focus on training new talent in the fields of advanced technologies, including quantum computing and machine learning. Founded by experts in quantum computing and finance in 2019, deeptech Multiverse Computing has fully owned subsidiaries in Toronto, Paris and Munich. Its expertise in quantum algorithms and quantum inspiration allows it to achieve optimal results with current quantum devices as well as high-performance classical computers. Its flagship product, the Singularity SaaS platform, enables professionals in all industries to leverage quantum computing with mainstream software tools.