233 citations · 727 across the 26 of their papers we have counts for
26 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…
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…
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…
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…
Hardware-efficient ansatz without barren plateaus in any depth
Chae-Yeun Park, Minhyeok Kang, Joonsuk Huh
Variational quantum circuits have recently gained much interest due to their relevance in real-world applications, such as combinatorial optimizations, quantum simulations, and mod…
Hybrid quantum programming with PennyLane Lightning on HPC platforms
Ali Asadi, Amintor Dusko, Chae-Yeun Park +5
We introduce PennyLane's Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applicati…
The hardness of quantum spin dynamics
Chae-Yeun Park, Pablo A. M. Casares, Juan Miguel Arrazola +1
Recent experiments demonstrated quantum computational advantage in random circuit sampling and Gaussian boson sampling. However, it is unclear whether these experiments can lead to…
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…
Backpropagation scaling in parameterised quantum circuits
Joseph Bowles, David Wierichs, Chae-Yeun Park
The discovery of the backpropagation algorithm ranks among one of the most important moments in the history of machine learning, and has made possible the training of large-scale n…
Hamiltonian variational ansatz without barren plateaus
Chae-Yeun Park, Nathan Killoran
Variational quantum algorithms, which combine highly expressive parameterized quantum circuits (PQCs) and optimization techniques in machine learning, are one of the most promising…
Complexity phase transitions in instantaneous quantum polynomial-time circuits
Chae-Yeun Park, Michael J. Kastoryano
We study a subclass of the Instantaneous Quantum Polynomial-time (IQP) circuit with a varying density of two-qubit gates. In addition to a known anticoncentration regime, we identi…
Efficient ground state preparation in variational quantum eigensolver with symmetry-breaking layers
Chae-Yeun Park
Variational quantum eigensolver (VQE) solves the ground state problem of a given Hamiltonian by finding the parameters of a quantum circuit ansatz that minimizes the Hamiltonian ex…
Scalable Neural Decoder for Topological Surface Codes
Kai Meinerz, Chae-Yeun Park, Simon Trebst
With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations…
Expressive power of complex-valued restricted Boltzmann machines for solving non-stoquastic Hamiltonians
Chae-Yeun Park, Michael J. Kastoryano
Variational Monte Carlo with neural network quantum states has proven to be a promising avenue for evaluating the ground state energy of spin Hamiltonians. However, despite continu…
Classical restrictions of generic matrix product states are quasi-locally Gibbsian
Yaiza Aragonés-Soria, Johan Åberg, Chae-Yeun Park +1
We show that the norm squared amplitudes with respect to a local orthonormal basis (the classical restriction) of finite quantum systems on one-dimensional lattices can be exponent…
Geometry of learning neural quantum states
Chae-Yeun Park, Michael J. Kastoryano
Combining insights from machine learning and quantum Monte Carlo, the stochastic reconfiguration method with neural network Ansatz states is a promising new direction for high-prec…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…
Correlations in local measurements and entanglement in many-body systems
Chae-Yeun Park, Jaeyoon Cho
While entanglement plays an important role in characterizing quantum many-body systems, it is hardly possible to directly access many-body entanglement in real experiments. In this…
Coherence, Asymmetry, and Quantum Macroscopicity
Hyukjoon Kwon, Chae-Yeun Park, Kok Chuan Tan +2
We investigate a measure of quantum coherence and its extension to quantify quantum macroscopicity. The coherence measure can also quantify the asymmetry of a quantum state with re…
Disturbance-Based Measure of Macroscopic Coherence
Hyukjoon Kwon, Chae-Yeun Park, Kok Chuan Tan +1
We propose a measure of macroscopic coherence based on the degree of disturbance caused by a coarse-grained measurement. Based on our measure, we point out that recently proposed c…
Disappearance of macroscopic superpositions in perfectly isolated systems
Chae-Yeun Park, Hyunseok Jeong
Schrödinger's illustration of an imaginary cat in a box, neither alive nor dead, leads to a question of whether and how long a macroscopic quantum superposition can exist in variou…
A Unified View of Quantum Correlations and Quantum Coherence
Kok Chuan Tan, Hyukjoon Kwon, Chae-Yeun Park +1
In this paper, we argue that quantum coherence in a bipartite system can be contained either locally or in the correlations between the subsystems. The portion of quantum coherence…
Quantum macroscopicity measure for arbitrary spin systems and its application to quantum phase transitions
Chae-Yeun Park, Minsu Kang, Chang-Woo Lee +3
We explore a previously unknown connection between two important problems in physics, i.e., quantum macroscopicity and the quantum phase transition. We devise a general and computa…