Hybrid Approach Drives Progress

Quantum computing is on the verge of delivering real-world value, driven by its integration with artificial intelligence and high-performance computing. Early adopters in healthcare and materials science are already reporting breakthroughs that were impossible with classical computers alone, signaling a shift from theoretical research to practical application.

At the Quantum Tech World conference in Boston in late June, Lara Jehi, chief research information officer at Cleveland Clinic, highlighted how far the technology has come. In fall 2024, the largest quantum simulation possible was just ten atoms. Industry roadmaps predicted it would take five to seven years to surpass 10,000 atoms. Yet this year, the clinic simulated protein complexes of up to 12,635 atoms – a feat unachievable with classical methods.

"We would not have been able to do the same analysis classically," Jehi said. However, she noted that clinically relevant simulations require about one million atoms. She expressed confidence that this threshold could be reached within one to two years, thanks to the accelerating pace of improvement.

The key to current progress is a hybrid approach: quantum computers handle the most computationally intensive parts of a problem, while AI on classical systems identifies where precision is needed. For example, simulating how a compound binds to a protein in real time is too large for either AI or quantum alone. AI can identify the critical regions in a large molecule, and then quantum computers zoom in with higher resolution for accurate simulation.

Mitsubishi Chemical has been experimenting with quantum computing since 2018 for quantum chemical calculations and optimization. Qi Gao, distinguished scientist at the company, reported that they aim to have quantum in production by the end of this year or early next year. Their first use case is designing advanced semiconductor materials for two-nanometer chips. "Two-nanometer chips require high energy resolution, which is impossible for classical computer simulations," Gao explained. The company plans to simulate metal oxide, a photo-resistant material used in chip etching. Although full algorithm development will take a couple of years, the industry is targeting 2028 and 2029 as pivotal years for quantum computing.

SoftBank Corp. is also pursuing a hybrid strategy. The company connects customers to IBM and Quantinuum quantum machines via its AI data center, with 21 pilot projects underway. Nobushige Oguri, director of quantum business planning at SoftBank, stated: "Within our AI data center, we have already built the supercomputer level. It’s a world-class supercomputer, but it’s just set up for processing AI. The quantum computer will be the new accelerator to enhance current AI capability." This hybrid use of AI and quantum together is seen as the fastest path to commercial adoption.

Juliette Peyronnet, US general manager at Alice & Bob, emphasized that quantum processing units are specialized devices that cannot solve everyday problems. "They’re really bad at doing basic math," she said. Instead, quantum processors will handle challenges that traditional computers cannot tackle, much like GPUs accelerate AI tasks alongside CPUs.

Ecosystem Matures Rapidly

The quantum ecosystem is maturing rapidly. Marta Estarellas, CEO of Qilimanjaro Quantum Tech, noted that after 15 years in the field, she has seen dramatic acceleration. Quantum companies no longer need to build every component from scratch; a supply chain of spinoffs and startups now provides specialized layers. "Players like ours don’t have to think about building the full stack and can delegate to third parties – and that really helps push forward the technology," she said.

The Quantum Tech World conference itself reflected this growth. Over 1,300 attendees and more than 100 sponsors were present, including quantum computer makers like Quantum Computing Inc., which demonstrated a fraud detection algorithm that outperformed classical methods and scaled linearly with data set size. Software companies, consulting firms, and specialized providers also exhibited.

Jason Silbergleit, head of Americas at Classiq, an orchestration software company, reported that their booth was packed. "More and more users want to take advantage of the platform. Even in the past six months – three months – the amount of acceleration and interest is growing." His company provides an abstraction layer that simplifies quantum application development for non-scientists.

Celia Merzbacher, executive director of the Quantum Economic Development Consortium, observed that the field is shifting from fundamental exploration to scalable systems. "And within a timeframe that private investors and end users are willing to start to engage," she added. According to a report released in April, there are now 556 pure-play quantum companies and over 7,000 quantum-engaged organizations. The industry generated $1.9 billion in revenue in 2025, up 30% year-over-year. Government funding commitments reached $12.7 billion in 2025, a 300% increase from 2024, and private venture capital investment hit $4.9 billion, up nearly 200%.

The acceleration is visible on multiple fronts, Merzbacher said. Quantum companies are securing new investment rounds, and governments worldwide are making long-term commitments. The number of people actively working on advancing quantum hardware and software is growing rapidly.

In the near term, the hybrid AI-quantum model is the most promising pathway. As classical computing approaches its limits in areas like molecular dynamics, climate modeling, and cryptography, quantum computers will provide the necessary computational capacity. However, widespread fault-tolerant quantum computing is still years away. Current noisy intermediate-scale quantum (NISQ) devices are limited by error rates, but progress in error correction and qubit coherence is steady.

Companies like IonQ, Rigetti, and D-Wave continue to push hardware capabilities. Meanwhile, software platforms like Amazon Braket, IBM Qiskit, and Microsoft Azure Quantum are making quantum resources accessible to enterprises. The convergence of AI and quantum is also spurring research into quantum machine learning algorithms, which could further accelerate discoveries.

The implications for industries are profound. Healthcare could see faster drug development and personalized medicine. Materials science could design batteries, catalysts, and semiconductors with properties impossible to simulate classically. Finance could optimize portfolios and detect fraud with new levels of accuracy. Energy sector could improve grid management and carbon capture.

But challenges remain. Scalability, error correction, and the shortage of trained quantum programmers are hurdles. Education and workforce development are critical. Nevertheless, the early adopters cited above demonstrate that practical value can be extracted today, even with limited qubit counts and noise.

The hybrid approach, using AI to steer quantum resources, is likely to dominate until full fault-tolerant quantum computers arrive. As Lara Jehi put it, "We use classical computing up front to identify these highest tier fragments and then zoom in to those fragments with the higher resolution that quantum can provide for better simulation." This pragmatic strategy is already delivering results and will continue to evolve.

With billions of dollars flowing into quantum R&D and a growing ecosystem of providers, the next few years will be decisive. Mitsubishi Chemical's goal of production use by 2028, SoftBank's pilots, and Cleveland Clinic's rapid progress all point to a future where quantum computing is not just a research curiosity but a practical tool for solving humanity's hardest problems.

Source:Network World News