Embedding Learning in Hybrid Quantum-Classical Neural Networks. - Minzhao Liu; Junyu Liu; Rui Liu; Henry Makhanov; Danylo Lykov; Anuj Apte; Yuri Alexeev (QCE, 2022)

Estimating the frame potential of large-scale quantum circuit sampling using tensor networks up to 50 qubits. - Minzhao Liu; Junyu Liu; Yuri Alexeev; Liang Jiang (CoRR, 2022)

Complexity of Gaussian boson sampling with tensor networks. - Minzhao Liu; Changhun Oh; Junyu Liu; Liang Jiang; Yuri Alexeev (CoRR, 2023)

Towards provably efficient quantum algorithms for large-scale machine-learning models. - Junyu Liu; Minzhao Liu; Jin-Peng Liu; Ziyu Ye; Yuri Alexeev; Jens Eisert; Liang Jiang (CoRR, 2023)

Supercomputing tensor networks for U(1) symmetric quantum many-body systems. - Minzhao Liu; Changhun Oh; Junyu Liu; Liang Jiang; Yuri Alexeev (CoRR, 2023)

Certified Randomness implies Secure Classical Position-Verification. - Omar Amer; Kaushik Chakraborty; David Cui; Fatih Kaleoglu; Charles Lim; Minzhao Liu; Marco Pistoia (IACR Cryptol. ePrint Arch., 2024)

Applications of Certified Randomness. - Omar Amer; Shouvanik Chakrabarti; Kaushik Chakraborty; Shaltiel Eloul; Niraj Kumar 005; Charles Lim; Minzhao Liu; Pradeep Niroula; Yash Satsangi; Ruslan Shaydulin; Marco Pistoia (CoRR, 2025)

Certified randomness using a trapped-ion quantum processor. - Minzhao Liu; Ruslan Shaydulin; Pradeep Niroula; Matthew DeCross; Shih-Han Hung; Wen Yu Kon; Enrique Cervero Martín; Kaushik Chakraborty; Omar Amer; Scott Aaronson; Atithi Acharya; Yuri Alexeev; et al. (Nat., 2025)