Qoro Quantum: Shaping the Future of Distributed Quantum Computing

Quantum computers hold incredible potential, but they won’t operate in isolation.?

Different hardware platforms—such as superconducting qubits, photonics, or neutral atoms—excel at specific applications. In addition, quantum processors will still work alongside classical processors, GPUs, FPGAs, and ASICs to tackle complex problems.?

Qoro Quantum, founded in 2024 by Stephen DiAdamo and Dan Holme, is building the network stack for distributed quantum computing. Their technology enables scaling quantum algorithms across heterogeneous computing networks, automating processing pipelines, and orchestrating diverse hardware environments.?

Being part of the IBM Quantum Startup Network, NVIDIA Inception program, Creative Destruction Lab, and the Berlin Quantum Pioneer program, Qoro Quantum is paving the way for the next generation of scalable, hybrid computing solutions.

Learn more about the future of distributed quantum computing from our interview with the co-founder and CTO, Stephen DiAdamo:?

Why Did You Start Qoro Quantum?

Many years ago, I studied computer science for my bachelor's degree, but I was also interested in physics. That’s when I read a book about quantum computing and learned that it’s a combination of software engineering, math, and physics.?

While I had considered becoming a software developer, in my third year of studies, I decided to focus exclusively on quantum computing going forward—I would no longer learn Javascript but Python!?

I did an internship in quantum computing in Singapore and studied quantum information theory, but when I graduated in 2014, there weren’t a lot of opportunities to work in quantum computing. Instead, I moved from Toronto to Munich for my Master's and PhD. My main focus was to get a foundation in quantum computing, thinking on a big time horizon: what would it take to establish myself in quantum computing over the next ten years?

During my PhD, I worked at E.On’s quantum team in Munich and Riverlane in the UK. I learned about the challenges and that quantum computing needs not just better hardware but also better UX to become useful.?

Cloud platforms still require a lot of end-user resources to create a classical application and make it scalable. The same is true for quantum applications: They have to scale to many qubits, even across different quantum processors and classical processors. These systems have to be networked together and combined with central control systems.?

When I finished my PhD studies, I worked for some time at Cisco in their quantum network research group. However, I had many ideas about networked quantum computing systems and wanted the freedom to pursue my own ideas. So, I quit my day job to found a startup and work on distributed quantum computing.

Luckily, I met my co-founder while working at Cisco. I was in R&D, and he was in business partnerships, and we both shared a passion for quantum technologies. At some point, we even worked on a research paper together on linking satellites and ground-based quantum networks, and we started a podcast together. Eventually, we decided to embark on that journey together and founded Qoro Quantum.?

How Do You Run Distributed Quantum Algorithms?

Everyone in the quantum industry today knows that we’ll need a lot of qubits to run practical quantum algorithms and that it will be impossible to put many qubits in a single quantum processor. Also, quantum processors can be very different depending on the hardware platform. Some are faster, some are more accurate, and some are bigger than others. The real challenge is using all these processors at once in the best way.?

We’ll need either modular or distributed architectures, and looking at the software stack, this also means that quantum applications will need to run in a distributed manner.

However, efficiently distributing them across many quantum processors (and classical processors) is challenging. It doesn’t come for free, but it requires breaking down monolithic quantum programs and orchestrating the execution of program parts across many different processors.

As an end user interested in using quantum computers, this is a challenging task that varies from problem to problem and doesn’t contribute to developing the application. In the end, you just want to get the job done as quickly and of the highest quality possible. That’s why we’re building a platform that makes it easy for end users to run quantum algorithms on distributed systems with classical and quantum processors. Coro means “choir” in Italian, and with Qoro Quantum, we’re like the orchestrators of quantum applications.?

Currently, we are at the prototype stage, having built software that automates the parallelization of quantum computations across different processors. We have shown a demo of how it would run if deployed on real hardware while simulating quantum hardware. Our prototype system allows users to specify their problem (from a set of problems we have implemented so far) as a set of parameters, never having to write quantum circuits or program logic. The circuit generation of program logic, the optimization, and execution flow are handled by our system.

Quantum processors are the weak link right now because they lack processing power, are expensive, and are scarce. That’s why we now simulate their performance on classical computers, validating our system and preparing for when quantum computers are deployed in higher numbers. As quantum processors mature, we use this approach to prepare and learn how to use them effectively.?

What Are the Challenges of Distributed Quantum Computing?

One key challenge is scheduling tasks for distributed quantum computers to use the system effectively. For example, a quantum processor with 50 qubits can also run smaller quantum circuits of size 10, but then the other 40 qubits are doing nothing, and we could use them to run other quantum computations or functions.?

Qubits are expensive, and it costs millions to host quantum computers, so one must use them as much as possible. This is true for all compute resources, but it’s especially true for quantum computers due to their high development and operational costs.?

Also, the speed at which a quantum computer runs and how many shots you need to get a satisfactory end result all make a difference. A shot is a single execution of a quantum circuit on a quantum processor. However, due to the probabilistic nature of quantum mechanics, a single shot provides just one possible outcome, and multiple shots—often thousands—are needed to build a reliable statistical distribution of results and determine the most likely answer, which is the end result of a quantum computation.?

However, if a single shot takes a minute, running thousands of shots becomes expensive and impractical. Typically, the scheduling overhead of distributing a quantum computation across many processors is minute compared to the time it takes to execute the computations and achieve the desired accuracy. Accuracy is the problem, not overhead. The ability to use each type of quantum computer for its strengths is important when orchestrating jobs across multi-modal quantum computing networks.?

What’s Your Product Strategy?

We offer two possible ends. On the application side, we offer a Python package, a collection of quantum and hybrid quantum-classical programs allowing users to specify different features. The package is high-level, so you don’t have to write Qiskit code yourself; we generate the code for you.?

The quantum circuits are then sent to our cloud system, which executes the compute job, performing scheduling, resource allocation, and optimization. The results are then displayed nicely on a dashboard, which allows the end users to see the results, which hardware platforms the job ran on, and which choices produced the best results.?

On the other hand, we’re helping HPC center operators integrate QPUs with their existing CPU and GPU computing infrastructure. We provide a scheduling platform that they can integrate into their cloud service and that schedules the execution of quantum algorithms for their processors.?

Similarly, we also work directly with quantum hardware developers to ensure their processors can be networked and utilized effectively. At the moment, quantum hardware developers are pressed to deliver the best possible hardware. It’s hard for them to develop the entire software stack, particularly on the network side, not just for their own processors but also for integrating with HPC centers and with other vendors.?

That’s where we come in: We help develop the software stack needed to network different quantum devices and distribute workloads across them. We’re hardware agnostic and can thus work with any hardware developer.?

What Applications Are You Targeting?

The question everyone asks but no one knows how to answer: What will be the killer application for quantum computing that will finally make it commercially viable and useful??

There are some common applications for algorithms like VQE or QAOA since those algorithms involve a lot of trial and error to find an optimal solution, and you can parallelize the different trials across different processors or even split up the Hamiltonian to analyze sub-problems, among some parallelizations we use. We have demonstrated how to analyze the graph structure of QAOA problems, generating subproblems, running them independently in parallel, and merging the pieces for the final solution while maintaining high-quality results. Our system automates this flow.

I don't know whether QAOA algorithms will be the best application for quantum computers, but they’re well-suited for parallelization and are widely used in industry. We’re developing unique, proprietary algorithms to solve them efficiently.?

There are other libraries that allow users to build up VQE and QAOA problems, but the end-to-end workflow is missing. Using quantum computers currently requires a lot of custom engineering by the user, and our platform removes this burden.

What’s the Opportunity??

Looking back at when mainframes were popular and assuming that history is repeating, we know the software stack we’re building will be necessary to run quantum algorithms.?

Our vision is not just focused on computing but also, more broadly, on scaling applications across quantum networks, from QKD to entanglement distribution, ensuring quantum security, network monitoring, and interfacing. Then there’s also quantum sensing and entangling quantum sensors through quantum networks with quantum processors.

We’re building the foundation for distributed quantum computing, enabling modular quantum computing in the short term, bringing quantum computers to quantum networks in the longer term, and helping to build the quantum internet.?

What Advice Would You Give Fellow Deep Tech Founders?

It’s a long road to building a successful startup, and there are many unknowns. Be open-minded and don’t fear failure. There will be many more failures than successes, so persistence and willingness to adapt and change are key.?

Having the right team is important. Coming from a technology background, my interests primarily lie in building, but running a business takes much more than that. The most important part of the initial stages is to find customers willing to use and pay for your solution. It can take many iterations to find the right product to build that solves a real problem, and can do so sooner rather than later. Having the team to support every aspect of both building technology and selling it is important.?

The early stages are tough, and we are still in the early stages. But if you’re on the right track, things will trend upward and improve. My general advice is always to try. Be smart, take acceptable risks, but always find a way to try!