qBraid Is Unifying a Fragmented Quantum Computing World

As a technology that promises to revolutionize fields from encryption to drug discovery, quantum computing receives the kind of attention reserved for technologies that promise to change everything. But between the promise and the reality sits a complicated, fragmented landscape of hardware modalities, software frameworks, and developer workflows that still have no clear consensus. Recently, Solidigm’s Jeniece Wnorowski and I spoke with Kanav Setia, co-founder and CEO of qBraid, to explore where the field stands today and what it will take to make quantum computing practical for the organizations that want to leverage it.

A Field Still Finding Its Footing

Kanav came to quantum computing through a PhD in theoretical physics at Dartmouth College, where exposure to quantum mechanics led him toward quantum algorithms. When he started his PhD, he noted, there were no freely available quantum computers. Then about a decade ago, IBM made a two-to-five qubit machine publicly available. From that start, today systems are coming online regularly reaching 100 or more qubits.

Yet this progress, while real, has not resolved the fundamental question of what kind of hardware will win. While in classical computing silicon transistors underpin every device, quantum computing’s base infrastructure is still in flux, with many approaches to building “qubits” (quantum bits) in use. Superconducting circuits, neutral atoms, trapped ions, and photons all represent viable approaches, each with different trade-offs.

“If you are an end user utilizing quantum computers, you will be looking at different technologies,” Kanav explained. “And when you write your algorithms, you would want to run them on all the different kinds of hardware because you don’t know which of the hardware is going to perform the best.”

The Interoperability Problem

That diversity creates an immediate practical challenge for developers. Writing a quantum algorithm is only the beginning. Getting it to run reliably across different quantum processors requires navigating a maze of compilation layers, framework dependencies, and hardware-specific pipelines that do not yet work together cleanly.

“You write your algorithm once, and then you run it through one quantum computer pipeline,” Kanav said. “You need to make sure that the algorithm is supported by that quantum computer throughout the various frameworks. And many times, when you try out different algorithms, they have their own repositories, which are managed by certain companies that only support different hardware. So you have to zigzag to the end of the chain, which allows you to run on a quantum computer.”

qBraid’s platform addresses this directly by providing unified access to hardware from multiple providers alongside software from across the ecosystem. The goal is to let a developer come to one place and reach the full industry without spending time on integration work that does not advance their actual research or application.

Software as a Force Multiplier

While quantum computing hardware has seen significant advances in recent years, Kanav was clear that algorithmic and software progress can be a powerful accelerant. Early estimates suggested that breaking modern encryption would require billions of qubits running for years. Successive rounds of algorithmic improvement brought that estimate down to 20 million qubits, and then recently to around 10,000.

“All of those are algorithmic improvements along with a lot of software improvements,” Kanav noted. The lesson for enterprises is that the software layer is not a secondary concern waiting for hardware to mature. It is actively shaping what becomes possible and when.

qBraid is also working on what it calls QBraid OS, which aims to orchestrate the hybrid workflows that real quantum applications require. Because today’s quantum processors are error-prone, they depend heavily on classical compute for error correction and for handling portions of a computation better suited to central processing units or graphics processing units. Getting those workloads to move across processor types seamlessly, and to return coherent results, is the kind of infrastructure problem that will need to be solved before enterprises can rely on quantum for anything consequential.

What Signals Maturity

For organizations watching the space, Kanav offered a guideline for understanding when quantum will be ready for broader adoption. The key hardware milestone is error rates dropping below a threshold where results can be trusted without extensive post-processing. “Once it starts doing that…you will be sure that quantum computers are good enough to break encryption,” he said, “which means you will need to update all of the encryption infrastructure that we use.”

Beyond that threshold, the parallel question is how quickly new applications will emerge that justify the investment. Kanav drew a comparison to the early days of machine learning, when the relative merits of CPUs and GPUs were still being debated. The right architecture for certain workloads only became obvious over time, and the same pattern is likely to play out in quantum.

Drug molecule design, where quantum mechanics is directly relevant to simulating molecular behavior, is one area where intuition suggests quantum should eventually outperform classical approaches. But as Kanav was careful to point out, intuition is not proof.

The TechArena Take

Quantum computing remains early-stage, but the infrastructure decisions being made now will shape which organizations are positioned to benefit when the field matures. qBraid’s approach, standardizing access across hardware and software, while building the orchestration layer that hybrid classical-quantum workflows require, is creating the needed bridge to enable developers to focus on solving problems rather than managing their complexity. Technology decision makers who begin to leverage such platforms now will be far better positioned to move quickly when quantum hardware crosses the threshold from experimental to reliable.