Insider Brief
- Classiq, Comcast, and Advanced Micro Devices completed a joint trial demonstrating a quantum algorithm for improving network routing resilience and backup path identification.
- The project tested quantum techniques alongside GPU-accelerated classical simulation using AMD Instinct GPUs to identify independent, low-latency backup paths during network maintenance scenarios.
- The trial combined execution on quantum hardware and accelerated simulation environments to evaluate real-time optimization of complex telecommunications network challenges.
PRESS RELEASE — Classiq, the leading quantum computing software company, Comcast and AMD today announced the completion of a groundbreaking trial aimed at improving Internet delivery by leveraging quantum algorithms to supercharge network routing resilience.
“What our customers want is simple: fast, secure and reliable connectivity, but when you operate a network as large and dynamic as ours, delivering on that promise is complex, especially in the face of growing network demand,” said Elad Nafshi, Chief Network Officer, Comcast Connectivity and Platforms. “We launched these trials with Classiq last year with the goal of understanding how quantum software and technology could tackle real network challenges. Our results have shown that quantum computing for network optimization isn’t theoretical – it’s practical, scalable, and grounded in the needs of our customers.”
About the Trial
The joint trial tackled a fundamental network design challenge: identifying independent backup paths for network sites when implementing network maintenance and change management. The goal being that if a network site is taken offline for routine maintenance, should a second site unexpectedly fail during that window, network traffic could be seamlessly rerouted without any disruption or degradation to customer connectivity. To achieve this outcome, operators must identify unique backup paths that are fast, resilient to simultaneous link failures, and optimized for the lowest latency delivery, a task that becomes exponentially harder to identify as networks grow.
The trial applied quantum techniques, alongside high-performance classical computing, to test whether quantum algorithms could successfully identify unique network backup paths in real-time across change management scenarios. It comprised of execution on quantum hardware and in accelerated simulation environments that made use of AMD Instinct™ GPUs to achieve meaningful computational capacity (qubit scale) not yet possible through quantum hardware alone. With the GPU-accelerated simulations, the teams were able to iterate rapidly and validate algorithm behavior, together with runs executed on quantum hardware to assess implementation success. Review more detailed trial results in this scholarly article authored by the research team and this blog post for quantum developers.
Classiq provided quantum software and engineering support, empowering rapid modeling, optimized implementation and execution across both hardware and simulated environment executions.
Optimization problems in global telecommunications networks represent large combinatorial search spaces that grow exponentially with network size, making them computationally intensive to solve – the perfect challenge for quantum computing.
Enterprise quantum R&D requires rapid iterations and repeatable workflows,” said Nir Minerbi, co-founder and CEO of Classiq. “This collaboration demonstrates how teams can ideate, model complex optimization problems and then run them quickly and efficiently across different backends, including both GPU-accelerated simulation and quantum hardware, while keeping the work portable as the ecosystem evolves.”“The future of computing is a convergence of classical and quantum computing,” said Madhu Rangarajan, corporate vice president, Compute and Enterprise AI Products, AMD. “As a leader in high-performance classical computing, we’re exploring how we take our high-performance computing products and use them to support quantum. This collaboration shows a real-world example of how accelerated simulation and quantum execution can co-exist to solve a problem that matters to network operations.”
