The quantum computing industry is shifting away from the race to build ever-larger single machines and toward a fundamentally different architecture: linking smaller quantum processors together like building blocks. This modular approach, which was largely unexplored just a few years ago, now appears on the roadmaps of major companies including Google, IBM, IonQ, and Quantinuum.

Why Are Researchers Abandoning the "Bigger Is Better" Approach?

For years, quantum computing progress was measured by qubit count. IBM's current processors include the 127-qubit Eagle and the 133- to 156-qubit Heron family, built on superconducting qubit technology. But raw qubit numbers tell only part of the story. The real challenge is that as quantum systems grow larger, they become exponentially harder to control and maintain, a problem known as scaling noise.

Modular quantum computing sidesteps this bottleneck by avoiding the technical difficulties of directly scaling single quantum processing units (QPUs). Instead of trying to build one massive, error-prone machine, researchers network smaller, more reliable quantum modules together to achieve greater combined computing power. Most quantum experts now agree this is the approach most likely to yield quantum computers with the largest practical advantage.

What Has the Midwest Quantum Hub Already Accomplished?

The University of Illinois Urbana-Champaign-led NSF Quantum Leap Challenge Institute for Hybrid Quantum Architectures and Networks (NSF HQAN) has just been renewed for a second five-year phase with $37.5 million in funding. The center, which brings together 45 senior researchers from six institutions, has already demonstrated several breakthrough achievements that lay the groundwork for practical modular systems.

In its first phase, NSF HQAN researchers achieved a series of technical milestones that were previously considered difficult or impossible:

• Entangled quantum networks: Created entangled states in a four-node superconducting circuit quantum network, proving that quantum information can be reliably shared across separate modules.
• Quantum transduction: Demonstrated quantum-limited millimeter-wave to optical transduction using cold atoms coupled to a superconducting resonator, enabling communication between different types of quantum hardware.
• Reconfigurable modules: Built the first reconfigurable superconducting quantum computing modules, allowing researchers to reprogram how quantum processors work together.
• Remote entanglement stabilization: Achieved the first autonomous stabilization of remote entanglement in a network, a critical step for maintaining quantum information across distances.
• Neutral atom arrays: Implemented the first algorithms on small neutral atom arrays and created the first atom-array modules with over 1,000 sites, demonstrating scalability in a different quantum platform.

The center has published over 210 peer-reviewed articles and placed 27 alumni into high-profile industry positions, 17 into faculty roles, and nine into national laboratories. Educational programs have brought quantum science to over 12,000 participants across the country.

"The first phase of HQAN has made substantial progress in terms of both research advances and building the quantum workforce of the future. We have set the stage for modular quantum computing, which was largely unexplored when we started but now appears on the quantum roadmaps of major companies," said Brian DeMarco, Illinois physics professor and NSF HQAN director.

Brian DeMarco, Director of NSF HQAN and Physics Professor at University of Illinois Urbana-Champaign

What Makes Quantum Computing Different From Classical Computers?

Understanding why modular approaches matter requires grasping how quantum systems fundamentally differ from the computers we use every day. Classical computers encode data as binary bits, which are either 0 or 1. Quantum computers use qubits, which can exist in a superposition of both states simultaneously, like a coin spinning in mid-air rather than resting on heads or tails.

When multiple qubits are combined, the total computational state space grows exponentially. This exponential expansion is what gives quantum computers their theoretical advantage for specific problems. However, this power comes with a catch: quantum states are extremely fragile and easily disturbed by their environment. Even tiny sources of interference can degrade coherence and introduce errors as computations grow more complex.

Today's quantum machines are still classified as noisy intermediate-scale quantum (NISQ) devices, meaning they have tens to hundreds of qubits but lack comprehensive error correction. Achieving true fault tolerance, where quantum computers can run longer and more complex computations reliably, requires larger numbers of qubits operating with incredibly low error probabilities. This remains a significant engineering challenge.

How Will the Second Phase of NSF HQAN Build on This Foundation?

The renewed funding will focus on closing the remaining gaps between current demonstrations and fully implemented modular quantum computing. The second phase will emphasize demonstrating basic operations, or application primitives, on modular platforms and laying the foundations for software implementations such as algorithms, error correction, and compilers.

Researchers will also advance the interconnects used to link QPU modules, explore chip-scale integration of quantum architectures, develop more energy-efficient quantum photonics, and create compact methods for establishing entanglement between separate quantum nodes. The goal is to deliver a comprehensive pathway to modular quantum computing with integrated hardware and software that is ready to translate into industry-ready solutions.

The center's 16 industry partners, including Google, IBM, IonQ, and Quantinuum, are actively involved in shaping the research roadmap. This partnership between academia and industry suggests that modular quantum computing is moving from theoretical research into practical engineering.

What Problems Can Quantum Computers Actually Solve?

It is a common misconception that quantum computers are generally faster or better for all types of problems. In reality, quantum computing is valuable only for targeted applications where the mathematical structure of the problem aligns well with how quantum hardware operates. Quantum computers are not supposed to replace conventional computing or solve everyday workloads like database queries or routine number crunching.

Quantum computing shows the greatest potential for problems where the number of possible outcomes grows exponentially. These problems often share common characteristics: they involve enormous numbers of possible configurations, rely on probabilistic behavior, or are rooted in physical processes that are already quantum in nature. Optimization is one of the most actively explored areas of quantum applications, largely because it quickly exposes the limits of classical approaches.

Modern quantum platforms are typically accessed through the cloud and integrated into hybrid quantum-classical workflows. They function best as specialized accelerators that depend on classical infrastructure for control and most computation, rather than as standalone replacements for classical processors.

"Illinois has made a bold commitment to becoming a global leader in quantum technology, and Grainger Engineering is proud to help turn that vision into reality. Together with our partners and NSF, we will advance the fundamental architectures needed to make quantum computing scalable and useful, while strengthening the talent, partnerships and innovation ecosystem that will drive the industry forward," stated Rashid Bashir, dean of Illinois' Grainger College of Engineering.

Rashid Bashir, Dean of Grainger College of Engineering at University of Illinois

Steps to Understanding Quantum Computing's Role in Your Industry

• Assess your optimization challenges: Identify problems in your organization where the number of possible configurations grows exponentially, such as supply chain routing, portfolio optimization, or molecular simulation. These are the domains where quantum computing offers genuine advantage over classical methods.
• Monitor modular quantum developments: Follow announcements from NSF HQAN and its industry partners regarding interconnects, transducers, and modular testbeds. These technical breakthroughs will determine when quantum systems become practical for real-world applications.
• Invest in quantum literacy: Develop internal expertise by engaging with educational programs like TeachQuantum or participating in industry partnerships. Understanding quantum fundamentals will help your organization recognize opportunities when modular quantum systems become available.
• Plan for hybrid workflows: Begin designing systems that can integrate quantum accelerators alongside classical computing infrastructure. Quantum computers will function as specialized tools within larger computational ecosystems, not as standalone replacements.

The shift toward modular quantum computing represents a maturation of the field. Rather than chasing headlines with ever-larger qubit counts, the industry is now focused on improving reliability, stability, and practical utility. With $37.5 million in renewed funding and the backing of major technology companies, the modular approach is transitioning from research curiosity to engineering roadmap. For enterprises and researchers watching quantum computing, the next few years will reveal whether this architectural shift finally delivers the practical quantum advantage that has been promised for decades.