Exploring the optimality of approximate state preparation quantum
  circuits with a genetic algorithm
We study the approximate state preparation problem on noisy
intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to
generate quantum circuits for state preparation. The algorithm can account for
the specific characteristics of the physical machine in the evaluation of
circuits, such as the native gate set and qubit connectivity. We use our
genetic algorithm to optimize the circuits provided by the low-rank state
preparation algorithm introduced by Araujo et al., and find substantial
improvements to the fidelity in preparing Haar random states with a limited
number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum
processor with limited qubit connectivity and significant noise levels (IBM
Falcon 5T), the maximal fidelity for Haar random states is achieved by a short
approximate state preparation circuit instead of the exact preparation circuit.
We also present a theoretical analysis of approximate state preparation circuit
complexity to motivate our findings. Our genetic algorithm for quantum circuit
discovery is freely available at https://github.com/beratyenilen/qc-ga .
Tom Rindell Berat Yenilen Niklas Halonen Arttu Pönni Ilkka Tittonen Matti Raasakka