GlaxoSmithKline (GSK) Evaluating Quantum Computing for Specific Workloads Including Genetic Algorithms
(NextPlatform) Drug maker GlaxoSmithKline (GSK) is evaluating quantum computing for specific workloads that hit scalability and complexity walls with traditional architectures and approaches, specifically genetic algorithms. While D-Wave showed the most promising results, GSK only used IBM’s quantum simulation environment to evaluate the gate model approach.
Specifically, the company wanted to understand how quantum computing might give more comprehensive results with both quantum annealing (D-Wave) and standard gate model systems (IBM’s quantum simulator in this case).
The mRNA codon optimization problem is a good fit for quantum annealing because it is an NP-hard optimization problem—the very problem set D-Wave has often discussed when describing potential use cases.
While the nature of the problem and how it was mapped to both the D-Wave and IBM systems is detailed in the paper, overall they found the D-Wave approach to be “competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical approaches.”
IBM’s gate model approach wasn’t fully tested on their cloud-based systems. Instead GSK researchers used the Qasm noisy simulator, which can simulate up to 24 fully connected qubits. “While current generations of devices lack the hardware requirements in terms of both qubit count and connectivity to solve realistic problems, future generation devices may be highly efficient,” they conclude.
GSK has been less bold with its proclamations of quantum ambitions, at least compared to other major drugmakers striking big visibility partnerships with IBM, Google, and others. Nonetheless, seeing quantum computing in drug discovery at a GSK-sized company and noting that the challenge is no longer accessing the hardware on-prem and is more a problem of carefully mapping the work is promising for quantum overall.