Denise Ruffner, VP Business Development and Commercial Operations Worldwide at Haiqu, is a seasoned technology executive with extensive experience commercializing quantum computing and other emerging technologies. At Haiqu, she leads business development and worldwide expansion, building on previous leadership roles including Chief Business Officer at Atom Computing and Cambridge Quantum Computing, Vice President of Business Development at IonQ and CATALOG, and nearly two decades at IBM. Her work has spanned enterprise sales, strategic partnerships, go-to-market strategy, ecosystem development, and bringing complex technologies from research environments into commercial markets. She has also co-founded DiviQ, an organization focused on building a more diverse quantum workforce, and advises early-stage quantum and emerging-technology companies on commercialization, partnerships, fundraising, and customer acquisition.
Haiqu is a quantum software company developing what it describes as an Agentic Operating System for quantum R&D teams, combining AI-powered quantum research agents with a proprietary software stack designed to make quantum application development and execution more practical. Its platform includes an agentic R&D layer for turning research ideas into executable prototypes, the Haiqu SDK for data loading, circuit compression, optimization and error mitigation, and the Haiqu Runtime for orchestrating workloads across quantum hardware. The technology is designed to operate across multiple quantum processing unit architectures and support applications in areas including financial modeling, quantum simulation, computational fluid dynamics, quantum machine learning, and optimization. Haiqu positions the platform as a way for enterprise and scientific teams to move beyond experimental “toy problems” toward larger, more commercially relevant quantum workloads.
You recently joined Haiqu as Vice President of Business Development and Commercial Operations Worldwide. What attracted you to the company, and what are your immediate priorities in this new role?
I have known Haiqu’s founders and followed the company since its early days, and what attracted me was the progress they have made in a relatively short period of time. In three short years, Haiqu has moved from a fledgling startup to a company that has an impressive software product, HaiquOS, that delivers meaningful results now, even before fully fault tolerant quantum computers are available.
My immediate priority is to commercialize HaiquOS to a wider global audience, introducing the platform to a broader customer base.
You have helped IBM Quantum, Cambridge Quantum Computing, IonQ, and Atom Computing secure customers and strategic partners. Which lessons from those experiences will most directly shape Haiqu’s commercialization strategy?
One of the biggest lessons I have learned is that successful commercialization requires patience; it is very hard to introduce a product and achieve immediate success. Before companies launch their products, it is important to have a runway that creates awareness and credibility in the marketplace. Credibility comes from beta testing, publications in academic journals and in the media, conversations with potential customers, user endorsements, and scientific advisory boards.
Something else I have seen is that many startups fall into the trap of overhyping what their products can do. Over-claiming a product’s capabilities before they can fully support those claims is one of the biggest mistakes a company can make, as it ruins credibility.
I want to continue Haiqu’s tradition of accurately representing what the product can and cannot do. Haiqu has been very aware of the mistakes made by other quantum startups and has already built a strong following using scientific and technical credibility. My goal is to bolster Haiqu’s worldwide customer base because I feel that it is time that a wider geography of customers experience the benefits of HaiquOS.
Haiqu’s Agentic Operating System combines agentic intelligence, a software development kit, and a runtime orchestration layer. How do these components work together to move a research team from an initial idea to an executable quantum experiment?
The three layers map onto the three stages where quantum R&D usually stalls: designing the right application, running it efficiently, and iterating on results.
Agentic Intelligence is where a research idea enters. A team describes a business question or an exploratory research direction in natural language, and the agentic layer, built on our proprietary algorithm research, domain-specific workflows, and a curated quantum theory knowledge base, structures that intent, clarifies constraints, and maps the use case to the right algorithmic approach. What comes out is an execution-ready prototype.
The Haiqu SDK then turns that prototype into a performant hardware-ready workflow. It applies data loading, circuit compression, algorithmic optimization, error mitigation, and hardware-aware design so the team can extract real value from every quantum operation on today’s noisy hardware — up to 100x more usable operations on current NISQ devices.
Haiqu Runtime then handles execution: orchestrating the middleware infrastructure and execution flow to run the application on real hardware at the lowest wall-clock time and cost per experiment. That’s what let us reproduce a quantum dynamics simulation that previously cost roughly $30,000 and nine hours for about $25 in around 30 seconds.
Together they close the loop — intent in, executable experiment out, fast enough to iterate. The team moves from a general idea to a working prototype in days rather than months.
Agentic AI is increasingly being applied to complex scientific workflows. Which parts of quantum research can be delegated to intelligent agents, and where must experienced scientists remain firmly in control?
Agents are well suited to the work that is expertise-heavy but patterned: structuring a research question, mapping it to candidate algorithms, generating the scaffolding of an application, and handling the optimization and orchestration layers, which include data loading, circuit compression, error mitigation, hardware selection, and execution tuning. This is precisely the work that consumes a scientist’s time, taking away from the science itself, and it is where our agentic layer removes the “where do I even start” problem.
What stays firmly with experienced scientists is judgment. Defining what is worth investigating, deciding whether a result is physically meaningful rather than merely well-formed, validating outputs against known physics, and interpreting significance are not delegable. The right framing is that agents compress the path to a result and experts direct the inquiry and stand behind the conclusions. Accountability for the science does not move.
Haiqu argues that organizations should not have to wait for fully fault-tolerant quantum computers to conduct meaningful experiments. What can enterprises realistically accomplish with today’s hardware, and where should expectations remain measured?
Enterprises can realistically build and run working prototypes today: molecular and materials simulation, optimization problems, quantum machine learning, and probability-distribution modeling for applications like risk and derivatives pricing. We have shown this through concretely preparing simulations of the single-impurity Anderson model from scratch, and building a pipeline that reproduced experimentally observed signatures in neutron-scattering experiments on one-dimensional quantum magnets. We have also demonstrated other applications that are relevant to the finance, automotive and energy industries. The value today is in developing the expertise, workflows, and application readiness that pay off as systems scale.
Hardware is still in the Near-Intermediate Scale Quantum (NISQ) era, so the work that can be done today is genuine R&D and prototyping, not broad commercial quantum advantage. Users should keep in mind that results need validation, not every problem benefits from quantum yet, and the honest positioning is building capability now so teams are ready when the hardware matures.
Quantum projects can struggle to progress beyond proofs of concept. What separates an experiment with genuine commercial potential from an impressive technical demonstration that is unlikely to deliver business value?
The difference between genuine commercial potential and an impressive technical demonstration is whether the work efficiently addresses a real-world problem that organizations care about solving. A quantum solution could yield incredible results, but if it is highly expensive and difficult to replicate and scale, it will likely not deliver real business value.
Many such proofs of concept stall after these expensive technical demonstrations, as it is not efficient for businesses to pursue that solution for their workflows. This is why Haiqu’s capability in reducing the cost and time it takes to run meaningful experiments is so valuable; if business can run through more experiments within a given budget, they can more quickly identify experiments and projects that could provide real value.
When introducing quantum application software to an enterprise, who typically becomes the internal champion, and how do you build a business case when the technology’s long-term return may still be difficult to quantify?
When introducing quantum application software like HaiquOS into an enterprise, the internal champion can come from a number of places. It might be a scientist or researcher who is getting better results with the software, a manager who sees an opportunity to reduce costs or improve efficiency, or a research leader who sees the value in improving performance and accelerating the work their team is doing.
The business case really starts with demonstrating value today. We need to show customers what the software can do with the quantum hardware that exists now, while also helping them understand how those capabilities will grow as the hardware improves. HaiquOS allows customers to run meaningful experiments today and accelerate application development rather than waiting for fully fault-tolerant quantum computers.
From there, the business case becomes much more practical: Can we improve performance? Can we reduce costs? Can we help developers get to results faster? And can we give the customer confidence that what they are investing in today will continue to deliver value as quantum computing matures?
That combination of near-term value, a clear roadmap, and strong customer support is what helps make the investment easier to justify, even when the long-term ROI of quantum computing is still difficult to quantify.
Haiqu is developing a hardware-agnostic platform that can work across different quantum systems. How important is that flexibility to enterprise customers, and how does the company avoid reducing performance to accommodate multiple hardware architectures?
Enterprise customers are not necessarily committed to one type of quantum hardware. Most are experimenting with different systems to understand their performance, cost, and where each technology may have an advantage.
That makes it important for the software to be both hardware-agnostic and hardware-aware. Hardware-agnostic means it can work across different modalities—superconducting, trapped ion, neutral atom, and others. Hardware-aware means it understands the characteristics of the specific system it is running on.
The important point is that hardware-agnostic does not mean treating every quantum computer the same. Each architecture has different characteristics, so when a new system is added, the software needs to be tuned and optimized for that hardware.
The objective is to give customers flexibility in the hardware they use without sacrificing the platform-specific optimization needed to get the best performance from each system.
Which industries or application categories appear closest to gaining measurable value from Haiqu’s technology, and what technical or economic milestones would indicate that a use case is ready to scale?
We are seeing interesting results in several application areas. Haiqu has worked with HSBC on financial risk modeling and simulation, and with Quanscient on computational fluid dynamics, which has potential applications across industries including aerospace, automotive, and energy.
What these projects show is that we can run increasingly complex problems on today’s quantum hardware through capabilities such as data loading and circuit compression.
I don’t think we are yet at the point where we can say these use cases are ready to scale broadly. The key milestones will be fairly practical: better hardware performance, the ability to run larger and more complex problems, repeatable results, and ultimately an economic benefit compared with classical approaches.
Hardware is still the primary limitation. Our role is to help customers get as much as possible from current systems while continuing to push toward problems that can deliver measurable business value.
Through Women in Quantum and DiviQ (Diversity in Quantum), you have worked extensively on inclusion and workforce development. As quantum computing moves toward commercialization, what skills will be most urgently needed, and how can the industry build a broader talent pipeline?
When I began my career in quantum computing in 2015, it was an overwhelmingly male industry. Early on, I recognized that bringing more diverse talent into the industry would strengthen the workforce and improve outcomes. I have always believed that diverse teams make better decisions and foster more success in a business.
Part of building a broader talent pipeline is getting the message out that you do not need to be a quantum PhD to contribute to quantum computing. Many people are scared away by the perceived complexity of the science when, in reality, they already possess relevant skills.
The industry needs programmers, engineers, mathematicians, scientists and communications professionals and more. Employers should focus on identifying transferable skills and helping people adapt those skills to quantum computing, rather than overcomplicating the field and making it seem inaccessible.
People are often surprised by the opportunities available once they enter the industry, and our challenge is making sure they know those opportunities exist.
Thank you for the great interview. Readers who wish to learn more should visit Haiqu. Those who identify as LGBTQIA+, belong to a gender minority, are people of color, or live with a visible or invisible physical, mental, or psychiatric disability can also learn more about the community and initiatives at DiviQ.
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