3 citations · 7 across the 17 of their papers we have counts for
20 papers
Lean-QuantumAlg: A Framework for AI Agents to Produce Quantum Algorithms that Humans Can Trust
Lei Zhang, Mingrui Jing, Yusheng Zhao +2
AI agents are increasingly capable of constructing quantum algorithms and proofs, yet machine-checked proofs alone cannot guarantee that the intended problem is solved or that reso…
Multivariate quantum signal processing with optimal query complexity
Mingrui Jing, Xin Wang, Zhan Yu +1
Polynomial transformations of data encoded in quantum operations are a building block of quantum algorithms, and their query complexity measures how often those operations are used…
Learning Quantum Matter through Attention in Complex Space
Mingrui Jing, Erdong Huang, Jizhe Lai +3
Magnetic many-electron wavefunctions require amplitude and phase to be optimized together. Whether a complex internal representation improves this variational search is a practical…
An exchange-assisted entangling gate between 87Rb and 171Yb Rydberg atoms
Han Wang, Erdong Huang, Mingrui Jing +3
Neutral-atom tweezer arrays support scalable quantum information processing. Dual-species -- arrays combine long-lived ytterbium nuclear-spin d…
Exact Virtual Channel Programming with Vanishing Excess Overhead
Mingrui Jing, Mengbo Guo, Hongshun Yao +1
A finite-dimensional physical processor cannot exactly program a continuous family of distinct unitary channels. We show that this obstruction becomes quantitative when the target…
Algebraic Speedups for Exact Inversion of Hamiltonian Evolutions
Jizhe Lai, Mingrui Jing, Erdong Huang +1
Deterministic exact inversion of an arbitrary -dimensional unitary requires {} coherent forward calls in the worst case. We ask how this cost changes for Hamiltonian evo…
Benchmarking Agents for Proving Theorems in Quantum Algorithms and Quantum Information
Lei Zhang, Yusheng Zhao, Yimeng Cao +10
Formal verification is becoming increasingly practical for quantum computing, yet the ability of AI agents to construct machine-checkable proofs in this domain remains unmeasured.…
An Agentic Formalization for Certified Quantum Neural Network Design
Mingrui Jing, Lei Zhang, Yusheng Zhao +2
A central model in quantum machine learning is the quantum neural network (QNN), whose design requires balancing expressivity and trainability. Technically, expressivity is studied…
Programmable Open Quantum Systems
Mingrui Jing, Mengbo Guo, Lin Zhu +2
Programmability is a unifying paradigm for enacting families of quantum transformations via fixed processors and program states, with a fundamental role and broad impact in quantum…
Structure, Optimality, and Symmetry in Shadow Unitary Inversion
Guocheng Zhen, Yu-Ao Chen, Mingrui Jing +3
Reversing unitary operations is a key task in quantum computing and quantum control. In this work, we introduce and develop the framework of shadow unitary inversion, a relaxed var…
LCQNN: Linear Combination of Quantum Neural Networks
Hongshun Yao, Xia Liu, Mingrui Jing +2
Quantum neural networks combine quantum computing with advanced data-driven methods, offering promising applications in quantum machine learning. However, the optimal paradigm for…
Quantum Recurrent Embedding Neural Network
Mingrui Jing, Erdong Huang, Xiao Shi +2
Quantum neural networks have emerged as promising quantum machine learning models, leveraging the properties of quantum systems and classical optimization to solve complex problems…
Circuit Knitting Faces Exponential Sampling Overhead Scaling Bounded by Entanglement Cost
Mingrui Jing, Chengkai Zhu, Xin Wang
Circuit knitting, a method for connecting quantum circuits across multiple processors to simulate nonlocal quantum operations, is a promising approach for distributed quantum compu…
Virtual Quantum Markov Chains
Yu-Ao Chen, Chengkai Zhu, Keming He +2
Quantum Markov chains generalize classical Markov chains for random variables to the quantum realm and exhibit unique inherent properties, making them an important feature in quant…
Enhancement of non-Stabilizerness within Indefinite Causal Order
Yin Mo, Chengkai Zhu, Zhiping Liu +2
In quantum computing, the nonstabilizerness of quantum operations is crucial for understanding and quantifying quantum speedups. In this study, we explore the phenomena of nonstabi…
Computable and Faithful Lower Bound on Entanglement Cost
Xin Wang, Mingrui Jing, Chengkai Zhu
Quantifying the minimum entanglement needed to prepare quantum states and implement quantum processes is a key challenge in quantum information theory. In this work, we develop com…
Quantum sequential scattering model for quantum state learning
Mingrui Jing, Geng Liu, Hongbin Ren +1
Learning probability distribution is an essential framework in classical learning theory. As a counterpart, quantum state learning has spurred the exploration of quantum machine le…
Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
Hao-kai Zhang, Chenghong Zhu, Mingrui Jing +1
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantu…
Retrieving non-linear features from noisy quantum states
Benchi Zhao, Mingrui Jing, Lei Zhang +4
Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, such as entanglement spectroscopy, entropy est…
Ground state preparation with shallow variational warm-start
Youle Wang, Chenghong Zhu, Mingrui Jing +1
Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground s…