Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

Abstract

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 .

22 pages, 4 figures; version 2 changes: title changed, numerical analysis extended to 1000 random states, references added, other minor improvements, conclusions remain unaltered

References in corpus (7)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum computational advantage using photons
• Synthesis of Quantum Logic Circuits
• Quantum-state preparation with universal gate decompositions
• Quantum State Preparation with Optimal Circuit Depth: Implementations and Applications
• Automatic design of quantum feature maps
• Numerical analysis of quantum circuits for state preparation and unitary operator synthesis

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• Probabilistic state synthesis based on optimal convex approximation
• Robustness of the quantum Mpemba effect against state-preparation errors
• T-Count Optimizing Genetic Algorithm for Quantum State Preparation
• Stabilization of symmetry-protected long-range entanglement in stochastic quantum circuits