Benchmarking Adaptive Genetic Pulse-Level Optimization on Standard Quantum Algorithms

Abstract

Noise in current quantum hardware (NISQ devices) poses a major obstacle by degrading gate operations and overall circuit fidelity toward FTQC. Traditional quantum error correction (QEC) is effective but resource-intensive, requiring several extra qubits and operations beyond the reach of near-term devices.

Quantum error mitigation (QEM) offers a complementary path by reducing the impact of error prior to full correction. We present an adaptive pulse-level QEM using genetic algorithms to dynamically adjust control pulses in real time, improving algorithm fidelity without modifying the logic structure of a quantum circuit.

By targeting pulse parameters directly, this method reduces the impact of various noise sources, improving algorithm resilience in quantum circuits. To demonstrate the effectiveness of our methodology, we apply our protocol to five key quantum algorithms, including Bernstein-Vazirani, Deutsch-Jozsa, Grover’s search, Quantum Fourier Transform (QFT), and its inverse (IQFT).

Experimental results show that our pulse-level optimization strategy provides a flexible and efficient solution for increasing fidelity during the noisy execution of quantum circuits. Our work contributes to advancing error mitigation techniques, essential for robust quantum computing, which will increasingly require HPC infrastructure as quantum systems scale.