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Our recent work (Ayral et al., 2020 IEEE Computer Society Annual Symposium onVLSI (ISVLSI)) showed the first implementation of the Quantum Divide andCompute (QDC) method, which allows to break quantum circuits into smallerfragments with fewer qubits and shallower depth. QDC can thus deal with thelimited number of qubits and short coherence times of noisy, intermediate-scalequantum processors. This article investigates the impact of different noisesources -- readout error, gate error and decoherence -- on the successprobability of the QDC procedure. We perform detailed noise modeling on theAtos Quantum Learning Machine, allowing us to understand tradeoffs andformulate recommendations about which hardware noise sources should bepreferentially optimized. We describe in detail the noise models we used toreproduce experimental runs on IBM's Johannesburg processor. This work alsoincludes a detailed derivation of the equations used in the QDC procedure tocompute the output distribution of the original quantum circuit from the outputdistribution of its fragments. Finally, we analyze the computational complexityof the QDC method for the circuit under study via tensor-networkconsiderations, and elaborate on the relation the QDC method withtensor-network simulation methods.

Quantum Divide and Compute: Exploring The Effect of Different Noise Sources