Reinforcement learning disentangles multiqubit quantum states

Researchers have developed a novel deep reinforcement learning approach to create efficient disentangling circuits for quantum states. This method utilizes partial observations, specifically two-qubit reduced density matrices, to guide the application of two-qubit gates on systems with up to 16 qubits. The system demonstrated its ability to autonomously identify qubit permutations and adapt protocols, showing resilience to noise and potential for real-world quantum computing applications. AI

IMPACT This research demonstrates a novel application of reinforcement learning in quantum computing, potentially advancing state preparation and control.

RANK_REASON Academic paper detailing a new methodology for quantum state manipulation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• controlled NOT gate
• noisy intermediate-scale quantum era
• Pavel Tashev

• paper
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →