Comcast, in collaboration with Classiq and AMD, has successfully completed trials demonstrating that quantum computing can significantly boost network resilience. The groundbreaking work, finalized last year, tackled the complex challenge of identifying backup network paths to ensure seamless connectivity during maintenance and unexpected failures. By applying quantum algorithms alongside AMD’s high-performance computing, the teams proved the potential for real-time optimization of large, dynamic networks. “We launched these trials with Classiq last year with the goal of understanding how quantum software and technology could tackle real network challenges,” said Elad Nafshi, Chief Network Officer, Comcast Connectivity and Platforms. “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.”
Comcast & Classiq Trial: Quantum Routing Resilience Challenge
The company partnered with Classiq and AMD to test whether quantum algorithms could dramatically improve network routing resilience, specifically during maintenance windows where single points of failure become acute. The trial focused on identifying truly independent backup paths—a task that escalates in complexity as networks expand—ensuring seamless traffic rerouting even if a secondary site fails unexpectedly. Operators need to identify paths that are fast, resilient to simultaneous link failures, and optimized for low latency, a problem that grows exponentially with network size. The collaborative effort didn’t rely solely on nascent quantum hardware; AMD Instinct™ GPUs were integral to accelerated simulations.
This allowed for rapid iteration and validation of algorithms, bridging the gap between theoretical potential and practical implementation. Nir Minerbi, co-founder and CEO of Classiq, emphasized the need for “rapid iterations and repeatable workflows” in enterprise quantum R&D.
AMD Instinct™ GPUs Accelerate Quantum Algorithm Simulation
The pursuit of resilient, high-speed internet delivery is now leveraging advancements in quantum computing, though not solely through quantum hardware. A recent trial, completed last year, demonstrated the power of combining quantum algorithms with the computational capacity of AMD Instinct™ GPUs to address complex network routing challenges. Comcast’s network, described as “large and dynamic,” presented a significant test case for identifying truly independent backup paths—critical for maintaining connectivity during maintenance or unexpected failures. The teams aimed to find paths that were not only fast but also resistant to simultaneous link failures, a task that escalates in difficulty with network expansion.
This collaborative effort didn’t rely on fully-scaled quantum computers; instead, AMD Instinct™ GPUs provided the “meaningful computational capacity” needed for accelerated simulations. Madhu Rangarajan, corporate vice president, Compute and Enterprise AI Products, AMD, added, “The future of computing is a convergence of classical and quantum computing.” Classiq contributed quantum software and engineering support, facilitating rapid modeling and execution across both simulated and quantum environments.
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.
Elad Nafshi, Chief Network Officer, Comcast Connectivity and Platforms
Qmod Language Enables Scalable Quantum Circuit Development
Classiq is pioneering a new approach to quantum circuit development with its Qmod language, designed to address the challenges of scaling quantum algorithms for real-world applications. The company’s platform utilizes artificial intelligence coding and this high-level modeling language to automatically generate optimized, scalable quantum circuits ready for deployment on existing hardware. This innovation is particularly relevant given the computational intensity of optimization problems within global telecommunications networks, which experience exponential growth in complexity with network size. Classiq’s technology allows quantum teams to accelerate algorithm development, optimize for both cost and performance, and maintain portability across diverse quantum hardware platforms.
The development of Qmod directly supports Classiq’s vision of making quantum computing accessible to enterprises and researchers, as evidenced by partnerships with companies like Rolls Royce, BMW, and Deloitte. This emphasis on iterative design is facilitated by the ability to model complex optimization problems and execute them efficiently across both GPU-accelerated simulations and actual quantum hardware.
