Envisioning a hybrid roadmap where CPUs, GPUs, and QPUs operate in seamless coordination

Dr Sangeeta Chhabra, Co-Founder & Executive Director, AceCloud

Making Quantum affordable for Enterprises

“At AceCloud, we see quantum processing units (QPUs) as the next step in 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. 

Competing globally without paying hyperscaler premiums 

Enterprises in India compete on speed, cost, and compliance. AceCloud is built keeping these factors in mind. Our customers achieve up to 60% lower cloud costs compared to hyperscalers, while still receiving 99.99% uptime, across our infrastructure. For AI and IT workloads, we provide NVIDIA GPUs on-demand, coupled with storage and compute VMs that scale instantly. 

Where hyperscalers push standardized bundles, we differentiate with 24×7 human support with strict SLAs. This matters for Indian CIOs and CTOs operating in BFSI, IT services, or healthcare where downtime is not an option. With focus on quality of service we have designed tiered SKUs and cost-optimized instance families let SMEs and large enterprises pick the correct CPU/GPU profile for ML, media, databases, or high-throughput workloads, reducing wasted spend. Our Mumbai and Noida regions keep customer data in-country for lower latency and easier compliance with Indian regulations.  

We intend to reduce operational overhead through Kubernetes-as-a-Service and DBaaS to object and block storage, plus Ace Insights (monitoring & alerting) so teams focus on product innovation, not infrastructure ops. Our curated GPU SKUs, pre-built golden images and an application marketplace let teams spin up training and inference environments quickly (including options across Ampere/Hopper/Ada families when needed). The migration playbooks, partner-run PoCs, and a Marketplace of golden images and application blueprints significantly shorten migration time and risk. We are also embedding DPDP Act readiness, ISO, and MeitY-aligned security into every layer, ensuring enterprises meet compliance without extra overhead.”