Quantum Machine Learning Goes Photonic

Anonymous

Bhaskara Narottama, Zina Mohamed, Sonia Aı̈ssa

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed.

Nir Rotenberg

Soumaya Cherkaoui

Future 6G networks are expected to surpass the advances made in 5G, by providing the faster speeds, lower latency and extended coverage needed for emerging transformative applications, while at the same time achieving greater energy and spectral efficiency, as well as enhanced security and reliability. While 6G is currently in its initial phases of development and standardization, it is already foreseen to incorporate not just incremental technical enhacements but also pioneering innovations compared to its forerunner, 5G. Indeed, quantum technologies are expected to play an important role in 6G. This goes beyond mechanisms such as quantum key distribution for secure communications; it includes the integration of quantum computing for advanced data processing within 6G networks. As 6G networks are set to integrate artificial intelligence and machine learning even more intrinsically into their operation, the concept of quantum machine learning (QML) emerges as a promising opportunity to enable swift data processing, network optimization, and increased security and privacy. In this presentation, we will look at the fundamentals of quantum computing and quantum machine learning, explore the possibilities they offer for future 6G networks, and the potential for revolutionary advances they offer, while presenting some the important challenges associated with their integration into 6G networks.

Yong Wang, Zhenghao Yin, Tobias Haug +6 more

Abstract Photons are promising candidates for quantum information technology due to their high robustness and long coherence time at room temperature. Inspired by recent advances in photonic computing techniques, research has increasingly turned to quantum machine learning (QML) on photonic platforms. Although photons provide a high-dimensional quantum feature space suitable for computation, a general understanding of how photon number can be harnessed for learning tasks remains limited. Here, we establish both theoretically and experimentally a learning-capacity advantage of multi-photon states over single-photon states in photonic QML. We prove that the learning capacity of linear optical circuits, quantified by the rank of the data quantum Fisher information matrix, scales polynomially with the photon number. This scaling enables multi-photon models to generalize from fewer training states than corresponding single-photon models and to achieve lower test loss under the same architecture and learning protocol. Moreover, we experimentally corroborate these findings through unitary learning and metric learning tasks, by performing online training on a fully programmable photonic integrated platform. Our work highlights the potential of photonic QML and paves the way for achieving quantum enhancement in practical machine learning applications.