How to Combine Classical Code with Quantum Workflows

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

We’ve reached an important milestone on the journey to realizing our vision of quantum-centric supercomputing: a new demo showcasing how Qiskit’s C API now enables end-to-end compiled-language quantum + HPC workflows. Learn more on the IBM Quantum blog (https://lnkd.in/eN-g9nuA) or explore the demo on GitHub (https://lnkd.in/eZHcbduA). Progress toward quantum advantage is accelerating and the first demonstrations of it will likely leverage quantum to accelerate classical HPC. To cross that finish line, and facilitate our collaboration with the HPC community, we need to enable full quantum workflows in compiled languages like C++ and Fortran, which power the lion’s share of today’s HPC data centers. This new demo is the result of incredible collaboration both across IBM and with our partners in the IBM Quantum Network. It would not be possible without the work we’ve done on the recent Qiskit SDK v2.2 release, which introduces a new standalone C API transpiler (https://lnkd.in/eGcCu45a), a key ingredient in our compiled-language workflows. The demo also leverages capabilities from open-source projects like the SBD eigensolver built by our partners at Japanese national laboratory RIKEN (https://lnkd.in/e8Gg7mJx), Qiskit C++ (https://lnkd.in/eKYS5yK8), the quantum resource management interface or QRMI (https://lnkd.in/eBa39cX2), the qiskit-ibm-runtime C client (https://lnkd.in/exXZgByC), and the new HPC-ready version of Qiskit’s SQD addon (https://lnkd.in/e2KC5niH). Together, these capabilities allow you to compile a hybrid QCSC workflow into a single binary executable that uses MPI in a way that is native for a broad cross-section of HPC environments—a remarkable achievement given that our C API is less than a year old. I hope you’ll explore the resources and GitHub repos linked above to test drive the QCSC workflows of the future, and to see how you can contribute to these open-source community efforts. 

Stop thinking of #Quantum #Computing as a distant, isolated machine. That's the mindset preventing enterprise adoption. The biggest obstacle to achieving Quantum Utility isn't the hardware itself; it's the integration gap. Quantum Processors (#QPUs) are highly specialized accelerators, not standalone systems. They are virtually useless to a business if they cannot speak fluently with your existing classical computing environment, Cloud infrastructure, and data pipelines. This is the key distinction: The path to production-ready Quantum is #hybrid orchestration. This approach makes it realistically achievable for the enterprise by treating Quantum as an extension of your current infrastructure, not a costly replacement. Here is how that integration is built on practical foundations: 👉 Cloud-Enabled Access (QaaS): The Cloud abstracts the immense complexity and cost of housing a QPU, delivering it as a simple, pay-as-you-go Quantum-as-a-Service (#QaaS) resource. This immediately shifts QC from a lab expense to an accessible compute utility. This aligns with a Cloud-First, AI-Enhanced, Quantum-Aware strategy. 👉 The Hybrid Algorithm Loop: The most relevant near-term applications (optimization, materials science) are intrinsically hybrid. This means the classical computer (#HPC) handles the data preparation, parameter optimization, and post-processing, while the QPU performs the single, impossible quantum calculation. They work in a continuous, high-speed loop. Without this tight integration, the theoretical quantum advantage is lost. 👉 Governance & Management: Classical High-Performance Computing (HPC) environments are critical for managing the QPU's extreme fragility. They handle real-time decoding for error correction and autonomous system calibration, ensuring the quantum resource is stable enough for actual business workloads. Think of it this way: The QPU is an ultra-high-performance Formula1 engine, and the classical computing environment is the pit crew, telemetry analysts, and fuel. The engine (QPU) cannot win the race alone. It needs the high-speed pit stop (HPC integration) to process data in milliseconds—adjusting pressure, flow, and direction in real-time. Without this integration, the engine is just an impressive, but unleveraged, piece of engineering. Quantum Computing isn't a replacement for classical IT; it's becoming its most powerful accelerator. Embracing this hybrid, Cloud-centric view is the most efficient way for executives to move past the "hype" and translate these complex technical implications into tangible business value. What is the first real-world business problem in your industry that you believe a hybrid quantum/AI model could solve to generate measurable ROI? Share your insight below. #QuantumComputing #AI #HybridCloud #DigitalTransformation #B2BStrategy