New research published on April 28, 2025, reveals how controlled noise can improve quantum computing performance. The study “Stochastic quantum adiabatic algorithm with fractional Brownian motion” by researchers Osanda Chinthila, Pani W. Fernando, Anuradha Mahasinghe, Kaushika De Silva, and Sarath Kumara demonstrates that incorporating specific types of stochastic noise can enhance quantum adiabatic algorithms.
The team developed a framework using a stochastic Schrödinger equation with semimartingale approximation to handle non-Markovian dynamics. Their approach leverages fractional Brownian motion (fBm) to manage noise in quantum systems. Simulations using the Exact Cover-3 problem showed that success probabilities improved significantly as the Hurst parameter approached zero, suggesting better scaling potential despite current limitations to small qubit systems.
Integrating stochastic calculus with quantum computing represents a significant step forward in addressing inherent noise challenges. The research bridges quantum principles like superposition and entanglement with advanced mathematical models, potentially leading to more robust quantum algorithms.
Applications could span multiple industries, including finance, logistics, and materials science. While promising, the approach still faces implementation challenges that require further research to fully realize its potential.
The work exemplifies how interdisciplinary approaches—combining quantum physics with advanced probability theory—can create unexpected advantages, turning what was once considered an obstacle (noise) into a performance enhancement tool.
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🗞 Stochastic quantum adiabatic algorithm with fractional Brownian motion
🧠 DOI: https://doi.org/10.48550/arXiv.2504.19801
