Resource quantification for programming low-depth quantum circuits

Noisy intermediate-scale quantum (NISQ) devices pave the way to implement quantum algorithms that exhibit supremacy over their classical counterparts. Due to the intrinsic noise and decoherence in the physical system, NISQ computations are naturally modeled as large-size but low-depth quantum circuits. Practically, to execute such quantum circuits, we need to pass commands to a programmable quantum computer. Existing programming approaches, dedicated to generic unitary transformations, are inefficient in terms of the computational resources under the low-depth assumption and remain far from satisfactory. As such, to realize NISQ algorithms, it is crucial to find an efficient way to program low-depth circuits as the qubit number $N$ increases. Here, we investigate the gate complexity and the size of quantum memory (known as the program cost) required to program low-depth brickwork circuits. We unveil a $\sim N \text{poly} \log N$ worst-case program cost of universal programming of low-depth brickwork circuits in the large $N$ regime, which is a tight characterization. Moreover, we analyze the trade-off between the cost of describing the layout of local gates and the cost of programming them to the targeted unitaries via the light-cone argument. Our findings suggest that faithful gate-wise programming is optimal in the low-depth regime.