August 20, 2025 -- IBM is treating quantum computing as strategically important as artificial intelligence, betting the technology will reshape industries in much the same way AI is transforming them.

According to an article in Global Venturing, IBM’s venture arm is targeting startups developing quantum software and algorithms that can complement its own hardware roadmap. Emily Fontaine, head of venture capital at IBM said the company believes that the next decade will decide not only which firms lead in AI, but also which ones dominate the transition to practical quantum computing.

IBM Ventures, which has invested across more than 20 portfolio companies currently, has been investing aggressively in AI since launching a $500 million Enterprise AI Venture Fund in 2024. At the same time, it has added quantum computing as the second pillar of its strategy. Fontaine, who took over as head of IBM Ventures last year, told Global Venturing that the firm views quantum will have financial impact on par with AI.

“We see quantum as the next frontier for computation, it’s going to unlock enormous financial benefits,” Fontaine said.

The timing reflects momentum both inside IBM and across the wider market. Funding for quantum startups has accelerated in 2025, Global Venturing reported, with investors drawn to a growing pipeline of applications and proof-of-concept projects. IBM itself has published a roadmap to build the world’s first large-scale, fault-tolerant quantum computer by 2029. The plan includes constructing quantum-focused data centers and linking them into a broader network.

“If you think about it, were we talking about quantum with startups 18 months ago?” Fontaine told Global Venturing. “One of the key insights we’re seeing in quantum right now is that funding for quantum startups has seen substantial growth year over year, particularly in the first half of 2025.”

Betting on the Software Layer

Much of IBM Ventures’ quantum strategy is aimed at startups building the tools to make quantum hardware more useful. The biggest obstacle remains error rates, which distort calculations in quantum processors. In July, IBM Ventures participated in a $26 million Series A round for Israeli startup Qedma, which is developing error mitigation software based on university research.

Other portfolio companies include QunaSys, a Japanese firm specializing in quantum chemistry algorithms, and Strangeworks, a U.S.-based platform that helps developers access quantum hardware. These investments are designed to reinforce IBM’s hardware efforts by ensuring customers have access to software that expands the scope of what quantum systems can accomplish.

The goal is to create a self-sustaining ecosystem around IBM hardware, an echo of how the company built networks around its mainframes and AI platforms. The company wants to be in position where its hardware is tightly linked to the software stack and developer community that drives commercial adoption.

“We’re very focused on IBM hardware, of course, but the hardware space is becoming more robust,” Fontaine said. “What we need to build is the algorithms, the software. We’ve got to create an ecosystem around that, and we want to be at the forefront — where we’ve not just got the best hardware on market…but we also want to help create that ecosystem.”

Universities as Anchors

Another differentiator in IBM’s approach is its focus on university partnerships. Qedma’s founders spun out of Technion and Hebrew University, illustrating how basic research is feeding into commercial ventures. IBM is also partnering with the University of Chicago on a quantum accelerator called Duality and collaborating on the creation of the National Quantum Algorithm Center in Chicago.

By building ties to universities, IBM Ventures aims to secure early access to talent and intellectual property while helping researchers move discoveries into the market. Fontaine said that quantum computing is still in its early stages, and developing a pipeline of ideas and people is as important as backing later-stage startups.

For all the investment and planning, commercialization of quantum computing may still be several years away. The priority now is connecting startups with IBM researchers and clients to run proof-of-concept trials, according to Fontaine. These projects are designed to show how quantum tools might eventually add value in areas like chemistry, materials, logistics, and finance.

She told Global Venturing: “I would say the quantum space is picking up extremely quickly and clients are actively looking to do POCs [to explore] how they drive [quantum computing] into their long-term roadmap. We’re bringing these startups with us when we talk about our hardware, in order to say ‘these are the things you could do’.”

The challenge is to keep startups alive long enough for the market to mature. IBM Ventures’ role is partly to ensure there are credible use cases emerging that can sustain interest and attract further funding. Client demand for these trials, Fontaine said, is rising quickly, which suggests a growing appetite for practical exploration even if full deployment is far off.

Parallel Push in AI

While quantum takes a larger share of IBM Ventures’ attention, artificial intelligence remains a central priority. IBM has been involved in major AI funding rounds, including last year’s backing of French startup Mistral, but the focus for its venture arm is enterprise adoption rather than consumer-facing applications.

“Enterprise AI adoption is really driving demand for the agent in applications,” Fontaine told Global Venturing. “We’re seeing that shift from augmentation to automation, which I don’t think is surprising, but it’s definitely exciting. And then we’re seeing domain-specific startups that are effectively building on increasingly advanced frontier models. Another interesting thing we’re seeing is that, as AI becomes increasingly embedded across all layers of software, engineers are embracing new tools for fine-tuning models.”

Global Venturing reported that IBM is targeting startups working on domain-specific AI tools, automation software and systems that link multiple models together. This reflects a belief that businesses will ultimately use a portfolio of AI models rather than rely on one dominant platform such as ChatGPT, Gemini, or Claude.

Examples include Not Diamond, a startup developing technology that routes queries across different models while adjusting prompts to maximize accuracy. Fontaine described this as part of a “fit-for-purpose AI strategy,” in which companies tailor their use of AI depending on the task.

Long-Term Alignment

IBM’s history in AI investing dates back to its $100 million Watson fund more than a decade ago. Watson has since evolved into the Watsonx suite, and IBM recently launched Watsonx AI Labs to connect with New York startups and universities. The aim is to replicate the ecosystem strategy it is pursuing in quantum, tying corporate ventures more closely to IBM’s own platforms.

The broader picture suggests that IBM Ventures is seeking long-term alignment rather than quick returns. That means building deep relationships with startups, integrating them into IBM’s client and research networks, and supporting their growth with strategic business development. The same approach is being applied in quantum and AI alike.

“We’re hyper focused on getting into the best companies, but following that investment, we actively engage our portfolio companies to help them scale, and we do that through targeted business development and deep integration into the global ecosystem,” Fontaine said, according to Global Venturing. “For me, what makes that so effective is this really strong team we have. Whether it’s on the strategy side, the investment side or the portfolio side, we’ve got deep experts and industry leaders in that space that have trusted relationships in the founder community and the investor community — which I think is huge in getting us into the best deals.”