At Riverlane, we’re making quantum computing useful far sooner than previously imaginable. To achieve this, we're building a novel software-hardware stack – the quantum error correction stack - that enables quantum computing to scale from today’s small machines that fail quickly under an avalanche of data errors to large-scale machines that correct errors in real time.
This quantum error correction stack is being built using classical computer science and engineering technology. It will comprehensively control all qubit types and decode the errors contained in their quantum information. It does this using wholly classical components of analogue and digital electronics implemented using scalable semiconductor chips, driven by a small runtime to drive the silicon.
To build this quantum error correction stack, we need to recruit “full stack” front-end, embedded software, hardware design, and hardware verification team members.
But we often get asked: “I don’t know any quantum mechanics; can I still work at Riverlane?”.
The answer is simple: “Yes, you absolutely can.”
There is so much learning to apply from the last 60 years in classical maths, computing and engineering to the world of quantum computing.
Let me give you two examples of why the quantum industry needs classical maths, computing and engineering graduates.
Quantum error correction
The first is quantum error correction. Without quantum error correction, there are no stable qubits and thus no reliable computation. In short, there are no useful quantum computers.
Building an error-corrected quantum computer is incredibly hard, a real moon-landing effort. Millions of qubits need to be controlled and calibrated; the decoding cycle and control loops must happen incredibly quickly and at scale meaning terabytes of data must be processed every second. This is a massive real-time information processing problem that we solve.
We need to develop a vast range of technologies to achieve quantum error correction. One is developing novel error correction algorithms.
The basic idea behind most error correction algorithms is to use a cluster of errors to build a spanning tree. If you know some graph theory, you can easily implement this in Python or Rust. But qubits have a very high decoherence rate (with error rates of between 1 in 10,000 to 1 in 100) compared to classical bits – requiring the quantum decoder to process terabytes of data every second. This data must then be decoded as fast as it’s acquired to stop errors propagating and rendering calculations useless.
Implementing error correction algorithms in the software could work - especially on QPUs that use slower qubits like ion traps – but there’s still a lot of work to do, adapting this software to work with error-corrected quantum computers. This is where the skills of classical computer engineers are not only useful but very necessary.
Kauser Johar is our Head of Silicon. With a background in electronics and communications, Kauser worked at chip pioneer Arm for more than a decade designing high-performance CPUs. A large part of Kauser’s work was related to classical error correction. This ties in nicely with Riverlane’s work to solve quantum error correction.
Now, Kauser works to practically implement quantum error correction and the hardware associated with it. This work recently resulted in the first step on our quantum error correction roadmap and with the release of the world’s most powerful quantum decoder.
At the other side of quantum error correction, Senior Quantum Scientist, Gyorgy Geher develops software to help users explore different quantum error correction ideas. His work includes exploring which quantum error correcting codes are best to use on different hardware types. Using this software, Gyorgy can give guidance to Riverlane’s hardware partners on what aspect of their qubits is worth improving on, and what they can expect after these aspects are improved.
Second, we need to use classical engineering techniques to help scale today’s quantum computers to the size where they have enough qubits to do “useful” work.
Today’s machines have, at most, a few hundred qubits. But experts predict we need thousands, maybe millions, of qubits to unlock the potential of quantum computing. Riverlane does not build this qubit layer – but we do work with a range of quantum hardware companies to help them make their qubits “look better”.
Many system engineering practices, such as user-based product design, low-coupling, high-coherence, functional APIs, automated testing, early full-system integration and failure resilience are now well-established ways to scale a technology – and this includes quantum computing.
We can apply these classical techniques to help scale the quantum stack. For example, our engineers already use industry-standard methodologies to accelerate our work on quantum decoders. This includes the early software modeling of hardware and UVM-based (Universal Verification Methodology) verification of hardware.
Senior Quantum Engineer Kenton Barnes comes from a classical computer science background. He uses classical software modelling techniques in his work at Riverlane to help iterate on hardware design ideas:
Senior Hardware Verification Engineer Rojalin Mishra has worked for a range of big tech companies, including Intel and Samsung Electronics, as a software engineer and verification engineer. Functional verification is a staple of all hardware companies, allowing them to identify and fix bugs in the early product design stages.
She explains how functional verification helps with the embedded system design for our control systems and silicon IP, helping Riverlane deliver bug-free designs in the shortest times possible:
Quantum computing is computing’s next great paradigm shift and will enable applications that are currently impossible on any classical computer. But it is accessed via many classical computing methods and technologies that have been around for decades.
By definition, we can only control classical artifacts; we have no real control over the quantum world. The only control we have is through the classical proxy of digital design, runtimes, compilers, hardware verification, distributed systems, algebra, graph theory, error detection and correction, runtimes and protocols.
That’s why we need all types of classical system engineers, computer scientists and mathematicians to help accelerate our work building the quantum computers that will transform our world.