Approximating Stochastic Quantum Noise Through Genetic Programming

Quantum computing’s potential for exponential speedups over classical computing has recently sparked considerable interest. However, quantum noise presents a significant obstacle to realizing this potential, compromising computational reliability. Accurate estimation and mitigation of noise are crucial for achieving fault-tolerant quantum computation. While current efforts focus on developing noise models tailored to specific quantum computers, these models often fail to fully capture the complexity of real quantum noise. To this end, we propose an approach that uses genetic programming (GP) to develop expression-based noise models for quantum computers. We represent the quantum noise model as a computational expression, with each function corresponding to a specific aspect of the noise behavior. By function nesting, we create a chain of operations that collectively capture the intricate nature of quantum noise. Through GP, we explore the search space of possible noise model expressions, gradually improving the quality of the solution. We evaluated the approach on five artificial noise models of varying complexity and a real quantum computer. Results show that our approach achieved an error difference of less than 2% in approximating artificial noise models and 15% for a real quantum computer.