arXiv:2411.03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models. We obtain efficient protocols, both in sample and computational complexity, for the task of inferring the parameters of their underlying quadratic Hamiltonian under the assumption of bounded temperature, squeezing, displacement and maximal degree of the interaction graph. Our protocol only requires heterodyne measurements, which are o
The continuous advancements in quantum computing research necessitate improved methods for understanding and manipulating quantum states, making this work timely for practical applications.
Efficient learning protocols for quantum states are crucial for developing robust quantum computation and quantum sensing technologies, directly impacting the viability of future quantum systems.
This research provides more efficient methods for characterizing bosonic Gaussian states, which could accelerate the development and reliability of quantum hardware and algorithms.
- · Quantum computing researchers
- · Quantum hardware developers
- · Advanced AI research labs
- · Classical simulation methods
- · Inefficient quantum measurement techniques
Improved understanding and control of quantum systems, particularly bosonic Gaussian states, becomes possible with lower computational and sample costs.
Accelerated development of quantum algorithms and hardware, moving quantum computing closer to practical applications.
Potential for new functionalities in quantum sensing and communication, enabling breakthroughs in fields beyond computation.
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