8 papers
Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency
Yuqing Li, Poonam Bala Nehru, Yunpeng Zhang +4
Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the n…
Self-Modifying Lean Proof Agents with Verifier-Grounded Benchmark Coevolution
Yuqing Li, Zeguan Wu, Yu Gan +1
Designing effective Lean proof agents is a central challenge in formal mathematical reasoning. Beyond building stronger provers, recent work emphasizes the workflow around Lean: ho…
When AI meets quantum information: A comprehensive review
Min Chen, Yu Gan, Xin Jin +15
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum syste…
Benchmarking and Resource Analysis for Augmented-Lagrangian Quantum Hamiltonian Descent
Zeguan Wu, Mingze Li, Muqing Zheng +6
Quantum Hamiltonian Descent (QHD) is a continuous optimization algorithm based on simulating a time-dependent quantum Hamiltonian whose potential energy encodes the objective funct…
Infeasibility Aware Large Language Models for Combinatorial Optimization
Yakun Wang, Min Chen, Zeguan Wu +3
Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation an…
Artificial Entanglement in the Fine-Tuning of Large Language Models
Min Chen, Zihan Wang, Canyu Chen +3
Large language models (LLMs) can be adapted to new tasks using parameter-efficient fine-tuning (PEFT) methods that modify only a small number of trainable parameters, often through…
Provably Efficient Quantum Algorithms for Solving Nonlinear Differential Equations Using Multiple Bosonic Modes Coupled with Qubits
Yu Gan, Hirad Alipanah, Jinglei Cheng +7
Quantum computers have long been expected to efficiently solve complex classical differential equations. Most digital, fault-tolerant approaches use Carleman linearization to map n…
A parity obstruction to degree-optimal syndrome extraction in bivariate bicycle codes
Guangyi Li, Yu Gan, Zeguan Wu +3
Syndrome extraction for a CSS code has depth at least the Tanner-graph degree , attained by edge colouring if gate order is ignored; correctness adds a parity constraint. We pro…