Thermodynamic hardware cuts mRNA optimization energy 1Mx

Thermodynamic computing, which harnesses natural thermal fluctuations as a computational resource rather than suppressing them, promises orders-of-magnitude energy savings for probabilistic and combinatorial tasks. Now, researchers Andraz Jelincic and Ross C. Walker have demonstrated the first concrete pharmaceutical application on such hardware. They reduce mRNA codon optimization—a combinatorial problem routinely solved in drug development—to sampling from an Ising model, making it directly executable on a thermodynamic sampling unit (TSU). This mapping allows the hardware's inherent thermal noise to accelerate optimization without the energy penalties of conventional digital circuits.

Testing three approaches (Potts sampling, Ising sampling, and a genetic algorithm baseline) on the SARS-CoV-2 spike protein, all achieved comparable optimization quality with scores around 234–240. Crucially, energy estimates based on validated hardware models indicate that a TSU could solve this problem using roughly 10^6 times less energy than a standard GPU. The authors have released all code under an open-source license. This work paves the way for ultra-energy-efficient computational biology, where complex optimizations can run at a fraction of the current power cost.