UK quantum algorithm start-up targets first opportunity 
Friday, June 21, 2019 at 9:43AM
Roy Rubenstein in Google, IBM, Intel, Riverlane, Steve Brierley, computational chemistry, quantum computing , qubit, superconducting, trapped ion

A UK start-up developing software for quantum computers has received £3.25 million ($4.1 million) in funding. 

Riverlane, based in Cambridge, is working with leading quantum computing hardware companies as well as large corporates interested in benefiting from the technology.

The start-up will use the funding to grow the company and has already identified the most promising applications for the technology.



“A lot of people are building hardware using various technologies such as iron trap or supercomputing qubits,” says Steve Brierley, CEO of Riverlane. “What we are trying to do is make that [hardware] useful as soon as possible.” A qubit is the shorthand term for a quantum bit.

Riverline’s core expertise is quantum-computing algorithms and its goal is to demonstrate quantum computing in high-value applications.

“Some of our techniques are orders of magnitude faster than existing quantum software,” says Brierley. “We take the same quantum computer but it runs ten times or one hundred times faster.”


Quantum computing 

Riverlane’s focus is to work with circuit-based quantum computers. Such quantum computers are capable of solving any type of problem including those where there is no advantage using such technology in preference to classical computers. “That would be an expensive way of solving classical problems,” says Brierley. 

But the great interest in quantum computing is its ability to solve certain problems that are beyond the capabilities of classical computing. Riverlane’s focus is to identify what it says are $1 million computational problems where quantum computing outperforms CPU-based platforms.

One early task the start-up undertook was working through key quantum algorithms and identifying the associated industry applications. “How big a quantum computer would you need to run that calculation and how valuable would the results be,” says Brierley. 

The undertaking helped the start-up develop a roadmap for quantum computing.

One large application area is the factoring of very large numbers that can be used to break cryptosecurity systems. Such an application area is coming but maybe 10-15 years in the future, says Brierley.

Other applications include solving optimisation and scheduling problems. Here, Riverlane can estimate how big a quantum computer - measured in the number of qubits - is needed to outperform high-performance computing.      


One application area that stands out from all the computational problems Riverlane has investigated is computational chemistry



One application area that stands out from all the computational problems Riverlane has investigated is computational chemistry, a discipline that can be used to model new materials for battery technology and solar cells, and protein-drug interactions for the discovery of medicines.  

“Essentially what we are doing is modelling physics at the molecular level,” says Brierley. “And the reason quantum computers are so good at that is that they themselves are quantum machines.”

In this context, a quantum computer can be viewed as an atomic system that is controllable, modelling some other atomic system.  This is a class of problem where quantum computers shine and are exponentially better at solving than traditional computers.   

Brierley also cites the example of the Haber-Bosch process used to make fertiliser for food production. This, he says, is an extremely energy-inefficient process that accounts for some two percent of the world’s energy consumption. In contrast, nature has its own nitrogen-fixing process that is performed at room temperature and requires almost no energy. 

“So there exists a process, we just don’t know what it is,” he says. “Modelling that process is a way to develop a new industrial process for production fertiliser.” 

Some five years ago, the size of quantum computer thought necessary to model such a problem required billions of qubits, says Brierley. Such a large machine will be possible in the future but represents a hugely challenging problem. 

“But through better algorithms and software, we are now down to solving that same problem with millions of qubits and I see us continuing that trend,” says Brierley. 

Once the problem can be reduced to requiring several hundreds of thousands of qubits to solve, practical systems ‘just might be possible’ in the next decade, he says.  


Essentially what we are doing is modelling physics at the molecular level. And the reason quantum computers are so good at that is that they themselves are quantum machines.


Quantum hardware

Companies developing quantum computing hardware, the likes of IBM, Google and Intel, have systems but they are small-scale, says Brierley: “They are pushing this technology along but no one is claiming they have got a quantum computing.”

The challenge facing the industry is scaling such systems. Twenty-qubit devices are generally available and next-generation 128-qubit devices are on the hardware companies’ roadmaps. 

“The mid-term ambition is to get to 1,000 qubits and in five years’ time you will see multiple companies with thousands of physical qubits,” says Brierley. But he points out that such devices are still ‘noisy’. “If you could produce perfect qubits without any noise, 1000 would be a huge number,” he says.

And while there are quantum error-correction schemes being developed, the issue of noise is pushing out the time frame of quantum computing.   

Riverlane is working with hardware companies such as Google and IBM but has yet to announce the end-user companies that it is partnering with. The typical engagements are with the R&D departments of large multinationals that want to understand the new technology and how it will benefit their research.

“I think what you will see next is one of the great quantum hardware companies showing off their technology but it will be running our software,” says Brierley. “And it will be achieving more because it is running our software.”


Venture funding 

Riverlane says it will use the seed funding to grow the company. The goal is to recruit between 20 and 25 staff by the end of 2020. Riverlane’s staff will number 14 by September. 

The start-up is seeking mathematicians, physicists, computer scientists and computational chemists.  

One challenge is to ensure employees communicate with one another.

“We all come from different fields and one of the most important things we have done as a company is how we all work on the same problem,” he says.

Brierley says securing the funding was relatively straightforward. “The most important thing was to find the right match with the investors that would see the potential of the company and understand the timescales,” he says. The key was finding such partners. 

The venture capital investors in Riverlane are Cambridge Innovation Capital and Amadeus Capital Partners. Cambridge Enterprise, a wholly-owned subsidiary of the University of Cambridge, is another backer.


Further Information

Do quantum computers obey Moore's law?, click here

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