84 citations · 443 across the 18 of their papers we have counts for
18 papers
Tight bounds on Pauli channel learning without entanglement
Senrui Chen, Changhun Oh, Sisi Zhou +2
Quantum entanglement is a crucial resource for learning properties from nature, but a precise characterization of its advantage can be challenging. In this work, we consider learni…
Constant-Overhead Fault-Tolerant Quantum Computation with Reconfigurable Atom Arrays
Qian Xu, J. Pablo Bonilla Ataides, Christopher A. Pattison +7
Quantum low-density parity-check (qLDPC) codes can achieve high encoding rates and good code distance scaling, providing a promising route to low-overhead fault-tolerant quantum co…
Fundamental causal bounds of quantum random access memories
Yunfei Wang, Yuri Alexeev, Liang Jiang +2
Quantum devices should operate in adherence to quantum physics principles. Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tas…
Exploring Shallow-Depth Boson Sampling: Towards Scalable Quantum Supremacy
Byeongseon Go, Changhun Oh, Liang Jiang +1
Boson sampling is a sampling task proven to be hard to simulate efficiently using classical computers under plausible assumptions, which makes it an appealing candidate for quantum…
Classical algorithm for simulating experimental Gaussian boson sampling
Changhun Oh, Minzhao Liu, Yuri Alexeev +2
Gaussian boson sampling is a promising candidate for showing experimental quantum advantage. While there is evidence that noiseless Gaussian boson sampling is hard to efficiently s…
Efficacy of virtual purification-based error mitigation on quantum metrology
Hyukgun Kwon, Changhun Oh, Youngrong Lim +2
Noise is the main source that hinders us from fully exploiting quantum advantages in various quantum informational tasks. However, characterizing and calibrating the effect of nois…
Towards Symmetry-Aware Efficient Simulation of Quantum Systems and Beyond
Min Chen, Minzhao Liu, Changhun Oh +3
The efficient simulation of complex quantum systems remains a central challenge due to the exponential growth of Hilbert space with system size. Tensor network methods have long be…
Towards provably efficient quantum algorithms for large-scale machine-learning models
Junyu Liu, Minzhao Liu, Jin-Peng Liu +5
Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-…
Quantum-inspired classical algorithm for graph problems by Gaussian boson sampling
Changhun Oh, Bill Fefferman, Liang Jiang +1
We present a quantum-inspired classical algorithm that can be used for graph-theoretical problems, such as finding the densest -subgraph and finding the maximum weight clique, w…
Simulating lossy Gaussian boson sampling with matrix product operators
Minzhao Liu, Changhun Oh, Junyu Liu +2
Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and is claimed to surpass the classi…
Spoofing cross entropy measure in boson sampling
Changhun Oh, Liang Jiang, Bill Fefferman
Cross entropy (XE) measure is a widely used benchmarking to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconductin…
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…
Data centers with quantum random access memory and quantum networks
Junyu Liu, Connor T. Hann, Liang Jiang
In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, an…
Laziness, Barren Plateau, and Noise in Machine Learning
Junyu Liu, Zexi Lin, Liang Jiang
We define \emph{laziness} to describe a large suppression of variational parameter updates for neural networks, classical or quantum. In the quantum case, the suppression is expone…
The learnability of Pauli noise
Senrui Chen, Yunchao Liu, Matthew Otten +3
Recently, several quantum benchmarking algorithms have been developed to characterize noisy quantum gates on today's quantum devices. A well-known issue in benchmarking is that not…
Estimating the randomness of quantum circuit ensembles up to 50 qubits
Minzhao Liu, Junyu Liu, Yuri Alexeev +1
Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole inf…
Analytic theory for the dynamics of wide quantum neural networks
Junyu Liu, Khadijeh Najafi, Kunal Sharma +3
Parameterized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problem…
Clifford-deformed Surface Codes
Arpit Dua, Aleksander Kubica, Liang Jiang +2
Various realizations of Kitaev's surface code perform surprisingly well for biased Pauli noise. Attracted by these potential gains, we study the performance of Clifford-deformed su…