Q/C Technologies develops proprietary photonic processing units that perform matrix multiplication through optical interference, delivering computational throughput and energy efficiency far beyond traditional electronic GPUs.
Funding
Funding not disclosed
Founders
Product
Problem
Current AI inference and blockchain processing rely on electronic GPUs that consume large amounts of energy, suffer from bandwidth bottlenecks, and exhibit high propagation latency, limiting scalability and increasing operational costs.
Solution
Q/C Technologies develops proprietary photonic processing units (OPUs) that execute matrix multiplication through optical interference. By encoding computation in light, the OPUs achieve propagation latency up to 1,000 × lower than electronic GPUs and quantum processing units, while delivering higher computational throughput per watt. The silicon‑based photonic architecture is designed for AI inference workloads, providing a sustainable path for high‑performance model serving. The same optical compute engine can be applied to blockchain validation and consensus tasks, addressing the sector’s energy‑intensity and scalability challenges. Q/C’s approach integrates with existing AI software stacks, allowing developers to offload matrix‑heavy kernels to the OPU without extensive code changes.
Target Audience
Primary customers are data‑center operators, AI inference hardware vendors, and blockchain platform developers that require high‑performance, low‑energy compute for large‑scale workloads.
Features
- Silicon photonic processing unit that performs matrix multiplication via native optical interference
- Light‑speed data propagation delivering up to 1,000× lower latency compared to GPUs and QPUs
- Energy‑efficient compute delivering higher FLOPs per watt for AI inference and blockchain workloads
- High‑throughput parallelism optimized for dense linear algebra kernels common in deep‑learning models
- Compatibility layer that maps standard AI frameworks (e.g., TensorFlow, PyTorch) to photonic kernels
- Scalable architecture designed for integration into data‑center racks or edge AI appliances