A smarter way to build quantum algorithm subroutines cuts circuit depth in half

Quantum oracles are the hidden subroutines inside algorithms like Grover's search and quantum machine learning, and their inefficiency bottlenecks the whole computation. This work introduces a formal modeling framework and an optimization algorithm that provably minimizes gate count for a fixed number of qubits. On 10–20 variable problems, the approach cuts average circuit depth by 54% compared to a standard baseline — a meaningful gain given how precious coherence time is on today's hardware.