Grover’s algorithm [3] interrogates an N-element unstructured database in √N searches as opposed to the best classical algorithm, which requires N/2. The original version is non-deterministic, with a significant probability of failing that varies with the size of the database and the number of marked elements. A recent version is deterministic, in principle achieving 100% success probability for any database size, as long as the number of marked elements are ¼ or less of the database size. We implement both versions of Grover’s on programmable quantum photonic processors in silicon nitride. The realised unitaries vary considerably from the ideal due to intrinsic circuit noise: a major issue is thermal cross-talk between the heaters used to program the circuit. We explored three regimes: 1) using the native device settings; then further correcting for circuit imperfections by 2) fast sequential vary and measure characterisation; and 3) scalable machine learning-assisted clear-box characterisation [5]. Regime 1 saw poor performance, regimes 2 and 3 improved considerably upon this with regime 3 achieving near-ideal performance. In every regime we find that the deterministic version is considerably more robust against device imperfections. Our work confirms the operational robustness of the deterministic Grover’s algorithm and the pathway for quantum photonic processors to move towards relevant quantum algorithmic implementations.

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