arXiv subjects

Shu Tian

Publications and source records attributed to Shu Tian.

3 recordsLinked to original sources

arXiv2026

Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians

We propose a training-free quantum generative paradigm, which is fundamentally different from current generative models, which demand substantial computational power, face practical scalability limits, and often function as opaque black boxes, despite their remarkable success. We enable image and text generation without parameter training, by constructing a local parent Hamiltonian whose ground state encodes the target distribution and then solving the global Hamiltonian. Rooted directly in quantum mechanical principles, this approach establishes a new pathway for generative modeling that leverages superposition and entanglement to maintain global consistency.

quant-ph↗

arXiv2026

Z2 Lattice Gauge Theory on Non-trivial Topology and Its Quantum Simulation

Wegner duality is essential for Z2 lattice gauge theory, yet the duality on non-trivial topologies has remained implicit. We extend Wegner duality to arbitrary topology and dimension, obtaining a new class of Ising models, in which topology is encoded in non-local domain-wall patterns. Without the overhead of gauge constraints, simulating this model on an L*L torus requires only L*L qubits with two-body couplings, halving the conventional four-body coupled 2L*L qubits, enabling full experimental realization of Z2 lattice gauge theory on near-term devices.

hep-lat↗

arXiv2020

End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining

License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particular, small-sized and oblique license plates, mainly caused by the distant and mobile camera, are difficult to detect. In this work, we propose a novel and applicable method for degraded license plate detection via vehicle-plate relation mining, which localizes the license plate in a coarse-to-fine scheme. First, we propose to estimate the local region around the license plate by using the relationships between the vehicle and the license plate, which can greatly reduce the search area and precisely detect very small-sized license plates. Second, we propose to predict the quadrilateral bounding box in the local region by regressing the four corners of the license plate to robustly detect oblique license plates. Moreover, the whole network can be trained in an end-to-end manner. Extensive experiments verify the effectiveness of our proposed method for small-sized and oblique license plates. Codes are available at https://github.com/chensonglu/LPD-end-to-end.

cs.CV↗