7 papers
Coherent-disorder-driven complexity transitions in a quantum-advantage architecture
Sung-Bin B. Lee, Chae-Yeun Park, Changhun Oh +1
While decoherence is known to erode classical hardness in quantum random sampling, the impact of coherent spatial disorder remains an open question. We study a square-lattice insta…
Strictly Local Tile-Code Architectures on Two-Dimensional Planar Lattices
Yoonjin Bae, Chae-Yeun Park
Tile codes are a family of planar quantum low-density parity-check (qLDPC) codes with weight-6 stabilizers and open boundary conditions, offering an encoding efficiency of…
HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance
Semin Park, Chae-Yeun Park
Group-equivariant quantum machine learning has emerged as a promising paradigm by incorporating symmetry into quantum models. However, constructing models simultaneously equivarian…
Toward the Goldilocks blind compression of quantum states
Hyunho Cha, Chae-Yeun Park, Jungwoo Lee
Quantum autoencoders (QAEs) are learning architectures that compress quantum data into a low-dimensional latent state while preserving the information needed for reconstruction. We…
All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
Richard D. P. East, Guillermo Alonso-Linaje, Chae-Yeun Park
Variational algorithms require architectures that naturally constrain the optimization space to run efficiently. Geometric quantum machine learning achieves this goal by encoding g…
Quadratically Shallow Quantum Circuits for Hamiltonian Functions
Youngjun Park, Minhyeok Kang, Chae-Yeun Park +1
Many quantum algorithms for ground-state preparation and energy estimation require the implementation of high-degree polynomials of a Hamiltonian to achieve better convergence rate…