Quantum computing has fascinated the tech world for several decades. That’s because, while classical computing is binary, quantum bits (qubits), made from atoms, electrons and superconductors, can be in both “0” and “1” at the same time, providing computational power that is inaccessible to classical computers.
CTO & Co-Founder of Quantum Machines.
The implications of this computational power will be far-reaching, such as accelerating drug discovery, optimizing global supply chains, and revolutionizing material science for clean energy. If that’s not interesting enough, the new frontier of hybridizing quantum and classical computing generates yet another source of excitement for the field.
Humanity has embedded computing into the fundamental fabric of daily life. It underpins our progress in an enormous number of fields, as well as impacts our day-to-day lives more than almost anything else.
While quantum computers are fascinating on their own, it is becoming increasingly clear that their integration into data centers – treating them as a crucial piece of the broader computing puzzle – represents one of the most interesting frontiers in science and technology today.
Hybrid computing: already a reality and key in the future
The idea that quantum and classical computing will work together is not a future vision. Quantum computers already rely on tight integration with classical computing, which plays a critical role in enabling their operation.
For example, extensive classical computing is used for generating the control signals that operate the quantum hardware and to calibrate quantum hardware and control systems. In this scenario, classical computers are finding thousands of different system parameters and retuning them constantly as they drift over time.
Very compute-intensive classical algorithms are also used for decoding errors during quantum error correction, which is critical for enabling stable quantum computation and the scaling of quantum systems. Another key use case is that hybrid quantum-classical algorithms rely on interleaving quantum and classical computation to solve complex problems.
In the future, this hybridization will have a growing impact on computing applications. QPUs will act as specialized accelerators within classical HPC and AI workflows, complementing CPUs and GPUs rather than replacing them. Quantum systems will sit alongside CPUs and GPUs in data centers and be deployed for exactly the type of problems they are best suited to solve.
In fact, this is already starting to happen – some of the most important recent quantum demonstrations have relied on quantum and classical systems operating in close coordination. For example, RIKEN and IBM scientists recently achieved one of the largest quantum simulations of iron-sulfur clusters through closed-loop data exchange between a co-located IBM Quantum Heron processor and RIKEN’s Fugaku supercomputer.
This kind of hybrid exchange between quantum and classical systems, rather than quantum computing operating as an isolated, standalone resource, is the model we expect to become standard as the technology matures.
The final, and perhaps most intriguing frontier in quantum-classical hybridization is its relationship to Artificial Intelligence. Compute infrastructure is being used to train AI models, while hardware capabilities limit what models can do in various ways. Integrating quantum hardware into this compute fabric may allow new data to be generated for AI models as well as new engines for AI inference.
Moreover, AI tools may help us break some of the abstraction layers we have created, which can remove further limitations on how we use quantum hardware to solve problems.
Quantum computers don’t run themselves
Given the above, as quantum computing moves from laboratory demonstrations toward practical applications, success will depend not only on better quantum processors but on how effectively quantum and classical computing work together.
The latency and bandwidth of the quantum-classical links will play a critical role in how performant this integration is and will determine how deeply connected the quantum and classical processing can become. This matters because qubits hold their state only briefly – the faster the classical system can read, process and respond, the more complex a calculation it can support before that state is lost.
Another critical aspect is software. Building infrastructure, tools, compilers and applications that orchestrate hybrid quantum-classical workflows efficiently, with high performance as well as developer experience in mind, will be critical for the industry to succeed in its mission to deliver real-world value in a timely manner.
This requires investment in the orchestration layers that allow developers to calibrate and control quantum resources as easily as they would HPC today, abstracting away much of the underlying hardware complexity.
The next phase of quantum computing will depend on bringing together advances in physics, engineering and computer science to cross the barrier from lab prototypes to useful computers.
There are many challenges ahead, from scaling the hardware and performing error correction at scale to finding the right applications to push for. The clear message is that hybrid quantum-classical systems are not a temporary stage in this journey. They are the foundation on which practical quantum computing will be built.
Enterprises and IT leaders exploring quantum shouldn’t focus on the number of qubits, but on how systems integrate with classical infrastructure. When it comes to scaling useful quantum computing and implementing hybrid computing, the control systems, error correction and software orchestration will determine whether a system can deliver reliable performance and repeatable results.
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