arXiv:2604.01197v4 Announce Type: replace-cross Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study the problem of learning to generate mixed states on a finite-dimensional lattice. Motivated by recent developments in mixed state phases of matter, we focus on arbitrary states in the trivial phase. A state belongs to the trivial phase if there exists a shallow preparation channel circuit under which local reversibility is preserved throughout the preparation. We prove that any mixed state in thi
The paper was published on arXiv, contributing to ongoing research in quantum information and computational complexity, specifically in learning quantum states.
This research advances the fundamental understanding of learning and generating quantum states, which is crucial for the development of quantum computing and simulation.
It provides a new theoretical framework and proof for learning specific types of mixed quantum states, potentially improving the efficiency and accuracy of quantum state reconstruction and preparation methods.
- · Quantum computing researchers
- · Quantum algorithm developers
- · Theoretical physicists
- · None
Improved methods for characterizing and preparing quantum states in experimental quantum systems.
Faster development and optimization of quantum algorithms and error correction techniques.
Accelerated progress towards fault-tolerant quantum computers and novel quantum material design.
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