Liu, H., Liu, X., Chen, Q., Qiu, Y., Vedral, V., Nie, X., ... & Lu, D. (2024). Quantum causal inference via scattering circuits in NMR. arXiv preprint arXiv:2411.06052.
This study aims to experimentally validate a quantum causal inference protocol that relies solely on coarse-grained projective measurements, implemented through scattering circuits, to determine causal structures in quantum systems.
The researchers employed a four-qubit Nuclear Magnetic Resonance (NMR) platform using 13C-labeled crotonic acid molecules. They implemented a quantum scattering circuit, where a probe qubit interacts with the system of interest, and its final measurement reveals information about the system's causal structure. Two types of channels were investigated: partial swap channels and a fully decohering channel. The team analyzed the negativity and time asymmetry of the experimentally constructed pseudo-density matrices (PDMs) and the corresponding Choi matrices to infer the underlying causal relationships.
The research validates the effectiveness of using coarse-grained projective measurements, implemented via scattering circuits, for inferring causal structures in quantum systems. The ability to extract causal information even from fully decohering channels highlights the robustness and potential broader applicability of this approach.
This study significantly contributes to the field of quantum causal inference by providing experimental validation for a minimally invasive protocol. It paves the way for exploring causal relationships in more complex quantum systems and could lead to novel quantum channel tomography protocols.
The experiment was limited by the number of qubits available in the NMR platform. Future research could explore the protocol's scalability by utilizing quantum simulators with a larger number of qubits. Additionally, investigating the protocol's effectiveness in inferring causal structures from more complex quantum channels and noisy environments would be valuable.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Hongfeng Liu... at arxiv.org 11-12-2024
https://arxiv.org/pdf/2411.06052.pdfDeeper Inquiries