ORCA Computing Collaborates with ST Engineering to Explore Quantum Technology for Advancing Cybersecurity

DEC 10 2025ORCA has announced a collaboration with ST Engineering to advance cybersecurity outcomes by applying quantum technology to threat detection. The project focuses on developing cyber anomaly detection using quantum machine learning (QML), a next-generation approach to identifying and mitigating malicious activity in complex digital environments.As cyber threats grow in frequency and sophistication, traditional detection methods face increasing limitations in processing the scale and complexity of real-world data. This collaboration leverages ORCA’s photonic quantum processors, enabling ST Engineering to deploy quantum-accelerated anomaly detection models that can identify subtle patterns of malicious behavior that would otherwise be missed by classical systems.The algorithm development applies quantum-enhanced machine learning and optimization techniques to cybersecurity scenarios such as intrusion detection, data exfiltration prevention, and real-time monitoring of large-scale networks. Running these applications on ORCA’s PT Series photonic quantum systems will potentially shorten timelines for industrial relevance, moving quantum cybersecurity from theoretical concept to operational reality.“Our collaboration with ST Engineering highlights how quantum acceleration is already being applied to practical, high-value use cases,” said Richard Murray, PhD, Co-founder and CEO of ORCA Computing. “By combining ORCA’s photonic quantum processors with ST Engineering’s deep cybersecurity expertise, we are laying the groundwork for scalable, deployable and commercially relevant quantum solutions in anomaly detection and beyond.”“At ST Engineering, we are committed to staying ahead of evolving cyber threats by advancing next-generation security technologies. We will initially evaluate quantum machine learning algorithms for detecting anomalies across diverse datasets and network architectures. Over time, we anticipate expanding quantum applications into broader security domains, reinforcing resilience in critical infrastructure, transportation, and defense systems,” said Vrizlynn Thing, Senior Vice President and Head of the Cybersecurity Strategic Technology Centre at ST Engineering.ORCA Computing’s partnership with ST Engineering, demonstrates how real-world use cases in machine learning and optimization are accelerating the timeline for quantum industrial relevance, and driving next-generation cybersecurity solutions closer to real-world deployment.David Hall DPhilHead of DeliveryProf. Ian Walmsley is Chairman of the ORCA Computing Board and a leading figure in quantum optics, quantum memories and waveguide circuits. He is Provost of Imperial College, London, an Honorary Fellow at St Hugh's College, Oxford and a Fellow of the Royal Society, The Optical Society, the Institute of Physics and the American Physical Society. Previously, he was President of the Optical Society of America, Pro-Vice-Chancellor for Research and Innovation, Hooke Professor of Experimental Physics at the University of Oxford and Director of the NQIT (Networked Quantum Information Technologies) hub. Prof. Walmsley is recognised for developing the SPIDER technique for characterising ultra-fast laser pulses.Enhance renewable energy optimisation and accelerate the development of biofuels. Investigating molecular structures is an important pursuit in computational chemistry, especially in fields likes biofuel formulation, material innovation, and pharmaceutical development where research acceleration is critical. The specific problem considered here is significant across the energy industry, as molecule’s possible structures directly determine many of its physical and chemical traits. However, the vast array of possible configurations and high computational requirements make it difficult for traditional methods to find low-energy conformations for certain molecules. ORCA partnered has with bp to explore a hybrid quantum-classical approach using generative adversarial network (GAN) algorithms. This approach aims to generate low-energy conformations of small and medium size hydrocarbon molecules, offering a potential solution to the computational hurdles faced in molecular exploration.