For decades theoretical physicists and computer scientists have explored what might be possible if we took a fundamentally quantum approach to computation. They’ve discovered algorithms that are theoretically able to accomplish tasks no classical algorithm has historically proven capable of. In order to execute those algorithms we require usable quantum computers that are able to perform operations on quantum bits. While a truly general purpose quantum computer is still some time in the future, the pace of advances in quantum hardware in the past few years has been astonishing.

At Balderton, we have been following the rapid emergence of quantum computing with tremendous curiosity (and a healthy dose of skepticism) for several years.

While the future scope of disruption is potentially vast, many applications of quantum computing are contingent on clearing major engineering challenges, largely around scaling the number of error-corrected qubits required to perform many quantum algorithms.

However, there are areas emerging, in particular, quantum chemistry and quantum machine learning, where quantum computing may have a disproportionate impact sooner than anticipated. Why? In chemistry, classical computers have encountered intractable problems that cannot be solved, unless we use quantum approaches to model quantum phenomena. For example, today we fix nitrogen and create ammonia using the Haber-Bosch process. The Haber-Bosch process is only 15% efficient with each pass and we use it to produce 450 million tons of nitrogen fertilizer each year. Currently a ton of fertilizer goes for around $500. Plants are able to fix nitrogen more efficiently but we don’t fully understand how because we can’t simulate nitrogenase. Using simulations to help better understand nitrogen fixation is an opportunity of tremendous scale.

Within this backdrop, we were lucky enough to come across the team at Rahko. Leo, Ed, Miriam and Ian have gathered a small but world-class team in London. They are taking unique approaches towards unlocking quantum discovery for chemical simulation, with techniques rooted in quantum machine learning that don’t require fully error-corrected quantum computers. Their goal on the product side is to build a robust quantum chemistry platform that provides best-in-class toolboxes for running quantum algorithms. Their work cuts across an entire spectrum: from deploying classical machine learning techniques and quantum-inspired methods on classical computers, to hybrid approaches using both classical and noisy intermediate-scale quantum computers (so-called “NISQ” devices), and in time techniques that will utilize quantum computers exclusively. Academically, there is a growing body of research exploring the intersection of machine learning techniques and quantum circuits. Rahko is well positioned to help companies leverage breakthroughs in this area as they unfold.

We couldn't be more proud to be working with the entire team at Rahko and are looking forward to growing and learning together in the years to come.