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At AceCloud, we see quantum processing units (QPUs) as the next step in the compute stack, especially for simulation, optimization, and AI workloads. While the commercial adoption curve in India will be slower than in GPUs, we have already begun internal research-based projects for AI acceleration use cases. We are already digging deep into research papers for hybrid workloads where CPUs, GPUs, and QPUs work together seamlessly.
We plan to build APIs, SDK hooks, and workflow adapters so developers can submit quantum jobs, stage data, and orchestrate pre/post classical workloads (simulations, pre/post-processing) in the same pipeline. We’ve also begun upskilling engineers on quantum-safe cryptography, hybrid algorithms, and risk models. Our security design will include data isolation for quantum workloads and governance that maps to existing regulatory requirements.
Our approach is pragmatic. Just as we offered enterprises NVIDIA A100/H200 GPUs with fractionalization for affordability, we will ensure QPU workloads are available on demand without prohibitive costs. We are also mapping compliance under the DPDP Act and MeitY norms to ensure quantum workloads stay data-sovereign within India.
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