State Reconstruction

Brian G. Swingle, Isaac H. Kim

We consider the problem of reconstructing global quantum states from local data. Because the reconstruction problem has many solutions in general, we consider the reconstructed state of maximum global entropy consistent with the local data. We show that unique ground states of local Hamiltonians are exactly reconstructed as the maximal entropy state. More generally, we show that if the state in question is a ground state of a local Hamiltonian with a degenerate space of locally indistinguishable ground states, then the maximal entropy state is close to the ground state projector. We also show that local reconstruction is possible for thermal states of local Hamiltonians. Finally, we discuss a procedure to certify that the reconstructed state is close to the true global state. We call the entropy of our reconstructed maximum entropy state the "reconstruction entropy," and we discuss its relation to emergent geometry in the context of holographic duality.

Bruce Mills

Jincheng Zhang

Quantum State Adaptive Reconstruction addresses the challenge of accurately reconstructing quantum states from incomplete data through a novel algorithm that leverages the inherent adaptability of quantum states. This paper proposes a quantum state reconstruction algorithm that dynamically adjusts the reconstruction process based on input data, leading to improved precision and robustness compared to existing approaches. The core mechanism centers on exploiting the quantum state's self-correction properties to iteratively refine the reconstructed state. The investigation highlights the algorithm's ability to effectively mitigate errors and enhance fidelity in various quantum state reconstruction scenarios.

Jincheng Zhang

Quantum state vector reconstruction is a fundamental problem in quantum information theory, crucial for tasks like quantum state tomography and quantum error correction. Traditional methods often struggle with noisy measurements and lack a principled framework for uncertainty quantification. This paper introduces a novel algorithm, Quantum State Vector Reconstruction via Bayesian Inference, that leverages Bayesian inference to reconstruct a quantum state vector from a set of measurements, enhancing accuracy and offering a more robust approach compared to existing methods. We detail the core mechanism, emphasizing the integration of Bayesian estimation for uncertainty quantification and refinement of the reconstruction process. The algorithm incorporates a probabilistic approach to estimate the state vector distribution, improving reconstruction by incorporating uncertainty estimates directly into the reconstruction process. This results in a more reliable and accurate state vector reconstruction, particularly when dealing with noisy measurements. This work addresses the limitations of conventional methods by providing a principled Bayesian framework for state vector reconstruction, offering a significant advancement in this field.

Jan Hamhalter