Riverlane is building the quantum error correction layer that all quantum computers need to achieve the huge scale to effectively tackle many of the world’s most challenging problems. We’re making fast progress but still have further to go.

But how exactly will error corrected quantum computers design new drugs or develop more efficient batteries for clean energy storage, for example? These are the kinds of questions Riverlane’s ‘Discover’ team works on every day.

Partnering with global innovation leaders in their respective industries like AstraZeneca, Merck, Rolls-Royce and Johnson Matthey, we develop these new quantum computing applications in the most efficient way possible.

Specifically, we develop the quantum ‘algorithms’ that instruct the quantum computer what operations and calculations to perform.

Our work is not in silo and relies on the other layers of the quantum stack. Applications sit above Riverlane’s error correction layer, Deltaflow.OS. Together, these layers make the qubits work as efficiently as possible and complete the necessary calculations. We don’t directly work on this qubit layer, but we do partner with many of the world’s leading quantum computing hardware companies, labs and governments. Current hardware partnerships, for example, include the IUK-funded projects UQCorrect to develop error correction for aerospace applications on trapped-ion architectures, syndrome extraction on superconducting qubits with Rigetti and The Quantum Data Centre of the Future for silicon quantum computing company ORCA Computing.

There are many potential applications for quantum computers, many of which we have not even thought of yet. So, how does a quantum computing company choose the right algorithms to focus on?

It’s not as complicated as you might expect – we already have a strong idea of the early use cases and are also exploring long-term applications.

Quantum computing and materials science

The simulation of molecules and materials is hugely complex using classical methods. We often reach a computational limit where it takes too long for any meaningful results to be uncovered, leaving scientists to resort to long, real-world test and development lifecycles.

This is where quantum computers can help. Quantum computers essentially “think” on the atomic scale, making them perfectly placed to simulate molecules and materials in new and efficient ways.

Riverlane’s CEO, Steve Brierley, believes quantum computers will, one day, solve chemistry. He’s not wrong. But there’s still a lot of work to do within quantum computing and materials science. Our team is currently focusing on the following areas:

  • Electron correlation: this is an important phenomenon to understand the properties of molecular and solid-state systems and how electrons “glue” molecules and compounds together.
  • Hydrogen molecules: hydrogen is a simple molecule, but better simulations will pave the way towards future breakthrough applications in chemistry. We have two upcoming hydrogen simulation papers – one is an educational resource and is already on arXiv, comparing classical and quantum simulations.
  • Complex materials: quantum computers will be capable of discovering increasingly complex molecules and materials, helping us uncover new fertilisers, superconductors and batteries. Some recent research with sustainable technologies leader Johnson-Matthey Technology Centre successfully adapted classical simulation techniques to run on a quantum computer, paving the way for future practical simulations on error-corrected quantum computers.
  • Pharmaceutical chemistry: We are working closely with partners to optimise workflows and find ways to discover new drugs faster than conventional calculations allow us.
  • Classical-to-quantum methods: We are investigating if existing, classical algorithms can be adapted to quantum computing because many use cases will require classical and quantum machines to work together.

Quantum computing and computational fluid dynamics

Understanding fluid and gas processes is essential for many industries and applications. The right quantum algorithms could help us develop more efficient engines and fuels or better predict the weather, for example, allowing us to simulate the fluid motions at the heart of such systems.

These computational fluid dynamic (CFD) simulations rely on a highly complex set of partial differential equations, which are computationally intractable even on the world’s most powerful supercomputers. This is because classical methods are polynomial in nature. So, as the problem gets bigger, so does the size of the calculation.

Quantum algorithms are logarithmic and promise an exponential speed up for these calculations, caveat to I/O requirements.

We’re investigating how to better feed data into quantum computers to improve our CFD quantum algorithms. We’re also developing new algorithms to tackle CFD problems in more efficient ways, including industry-specific algorithms and more general quantum algorithms that are important in many industrial processes and energy-related applications.

Our code base and future directions

There are two other, important areas that we’re working on. First, our code base is a valuable resource, allowing the team to build on all this vital work and (where possible) reuse or repurpose existing algorithms for new, exciting applications.

Second, we also dedicate our time to a lot of exploration work, considering new areas of potential research based on their scientific merit or commercial value. This includes financial applications, how to better print quantum circuits and non-Abelian anyons, to name a few.

The Discover team is constantly exploring applications, developing algorithms and understanding end users to help unlock useful quantum computing, sooner.

We cannot do it alone – we also need industry experts to tell us their “unsolvable” problems so we can explore how quantum algorithms can help.

If you would like to reach out to the team to find out what quantum algorithms could do for you, please contact us at team@riverlane.com.