How modern domain-specific compilers transform standard NumPy and PyTorch code into optimal, leveled homomorphic circuits.
Software engineers and data scientists understand matrix algebra and control flow, not cyclotomic rings, RNS bases, or relinearization keys. Bridging this gap requires domain-specific compilers that automatically manage ciphertext parameters, memory slot batching, and noise budgets.
We analyze compiler intermediate representations (HEIR, MLIR) that analyze computation graphs to minimize the required multiplication depth, schedule modulus-switching points, and insert relinearization instructions at mathematically optimal intervals.
Step-by-step trace of how a standard SQL aggregation query (SELECT AVG(salary) WHERE department = 'R&D') is compiled into a leveled BGV circuit executing over an encrypted employee database in under 200 milliseconds.