Quantum computing’s future runs on classical systems

Quantum computing’s promise depends on more than quantum processors. Classical computers will remain essential for controlling qubits, correcting errors, and coordinating the workloads that turn experimental quantum systems into useful tools.

Quantum bits, or qubits, can occupy both “0” and “1” at the same time, unlike the binary bits used by classical computers. That capability could accelerate drug discovery, optimize global supply chains, and advance materials research for clean energy. But quantum machines are not expected to replace CPUs and GPUs. They will operate alongside them as specialized accelerators.

How hybrid quantum-classical systems work

Quantum computing already relies on extensive classical infrastructure. Classical systems generate the control signals that operate quantum hardware, calibrate the equipment, and continuously retune thousands of system parameters as conditions change.

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They also run compute-intensive algorithms for decoding errors during quantum error correction, a requirement for stable computation and larger quantum systems. Hybrid algorithms go further by alternating between quantum and classical processing to tackle complex problems.

That model is beginning to appear in major demonstrations. RIKEN and IBM scientists recently performed 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.

The expected architecture is a data center where quantum processing units, or QPUs, sit beside CPUs and GPUs. Each type of processor would handle the problems it is best suited to solve, with classical systems managing much of the surrounding workflow.

Latency, software and the path to useful quantum computing

The connection between quantum and classical hardware will determine how effective this arrangement can be. Latency and bandwidth are critical because qubits retain their state only briefly. The faster a classical system can read, process, and respond to quantum data, the more complex a calculation can be completed before that state is lost.

Software is equally important. Developers will need infrastructure, compilers, tools, and applications that can orchestrate hybrid workflows while delivering both high performance and a practical development experience. Orchestration layers should make it easier to calibrate and control quantum resources, much as developers manage high-performance computing systems today, while hiding much of the underlying hardware complexity.

The same infrastructure could eventually connect quantum computing with artificial intelligence. Quantum hardware might generate new data for AI models or provide new engines for inference, while AI tools could help reduce some of the abstraction layers that currently limit how quantum systems are used.

The transition from laboratory prototypes to practical computers still faces major challenges, including hardware scaling, large-scale error correction, and identifying suitable applications. For enterprises and IT leaders, the number of qubits is therefore only one measure. Control systems, error correction, and software orchestration will determine whether a quantum platform can deliver reliable, repeatable results.