Wang Liao and colleagues at University of Tokyo have created KOVAL-Q, a new electronic design automation (EDA) kernel that verifies and optimises surface-code logical operations by translating them into a satisfiability problem. The approach enables greater flexibility in surface-code encodings and expands the possibilities for advanced layouts, such as fast blocks. Demonstrations show KOVAL-Q can identify the quickest way to perform key logical operations, reducing the execution time of established quantum computing applications by approximately 10% under a simplified model. Its modular design enables integration with larger heuristic frameworks. KOVAL-Q optimises fault-tolerant quantum computation via satisfiability-driven surface-code logic A new electronic design automation kernel, KOVAL-Q, has decreased execution times for widely studied fault-tolerant quantum computing applications by approximately 10%. This improvement arises from KOVAL-Q’s ability to discover and optimise logical operations on two-qubit surface-code patches, a capability previously unavailable with methods like LaSsynth. The framework achieves this by formulating these operations as a satisfiability problem. This is a standard approach in computer science where the goal is to determine if there exists an assignment of variables that satisfies a given Boolean formula. This broadening of the search space enables more flexible surface-code encodings. Surface codes are a leading candidate for error correction in quantum computers, and optimising their implementation is crucial for building practical devices. Its modular design also allows seamless integration into larger heuristic frameworks, providing a key tool for optimising and validating core fault-tolerant quantum computing subroutines. The significance of this lies in the potential to reduce the overhead associated with quantum error correction, a major hurdle in scaling up quantum computers. The framework determines the minimum execution time of logical CNOT gates and patch rotations, requiring only ‘d’ and ‘2d’ stabiliser measurement cycles respectively, where ‘d’ represents the distance parameter of the surface code, a key metric