11 citations · 13 across the 11 of their papers we have counts for
14 papers
Out-of-control Hamiltonian Learning
Weiyuan Gong, Muzhou Ma, Sitan Chen +2
Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable guarantees assume some level o…
Adaptivity is all you need: Optimal stabilizer learning using just single-copy measurements
L. Bittel, J. Eisert, W. Gong +2
Stabilizer states are central to quantum computing, underlying quantum error correction, benchmarking, and efficient classical simulation. Yet their learnability exhibits a strikin…
Quantum memory advantage for quantum process tomography
Carlos Bravo-Prieto, Weiyuan Gong, Antonio Anna Mele
Quantum process tomography, the task of learning an unknown quantum channel from black-box access, is a central problem in quantum information. In this setting, protocols with quan…
The log log jam in Gaussian state tomography
Sitan Chen, Weiyuan Gong, Qi Ye +1
Unlike in finite dimensions, quantum information in continuous-variable systems has the peculiar feature that without imposing physical constraints, the sample complexity of state…
Learning and Generating Mixed States Prepared by Shallow Channel Circuits
Fangjun Hu, Christian Kokail, Milan Kornjača +5
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 m…
Ansatz-Free Learning of Lindbladian Dynamics In Situ
Petr Ivashkov, Nikita Romanov, Weiyuan Gong +3
Characterizing the dynamics of open quantum systems at the level of microscopic interactions and error mechanisms is essential for calibrating quantum hardware, designing robust si…
Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach
Senrui Chen, Weiyuan Gong, Sisi Zhou
We study the sample complexity of shadow tomography in the high-precision regime under realistic measurement constraints. Given an unknown -dimensional quantum state and a k…
Noisy Quantum Learning Theory
Jordan Cotler, Weiyuan Gong, Ishaan Kannan
We develop a framework for learning from noisy quantum experiments in which fault-tolerant devices access uncharacterized systems through noisy couplings. Introducing the complexit…
Information-Computation Gaps in Quantum Learning via Low-Degree Likelihood
Sitan Chen, Weiyuan Gong, Jonas Haferkamp +1
In a variety of physically relevant settings for learning from quantum data, designing protocols that can computationally efficiently extract information remains largely an art, an…
On the sample complexity of purity and inner product estimation
Weiyuan Gong, Jonas Haferkamp, Qi Ye +1
We study the sample complexity of the prototypical tasks quantum purity estimation and quantum inner product estimation. In purity estimation, we are to estimate of an un…
Exponential Separations between Quantum Learning with and without Purification
Zhenhuan Liu, Weiyuan Gong, Zhenyu Du +1
In quantum learning tasks, quantum memory can offer exponential reductions in statistical complexity compared to any single-copy strategies, but this typically necessitates at leas…
Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation
Sitan Chen, Weiyuan Gong, Qi Ye +1
We study the task of agnostic tomography: given copies of an unknown -qubit state which has fidelity with some state in a given class , find a state which has fidelit…
Optimal tradeoffs for estimating Pauli observables
Sitan Chen, Weiyuan Gong, Qi Ye
We revisit the problem of Pauli shadow tomography: given copies of an unknown -qubit quantum state , estimate for some set of Pauli operators to within ad…
One Gate Scheme to Rule Them All: Introducing a Complex Yet Reduced Instruction Set for Quantum Computing
Jianxin Chen, Dawei Ding, Weiyuan Gong +2
The design and architecture of a quantum instruction set are paramount to the performance of a quantum computer. This work introduces a gate scheme for qubits with coupling…