84 citations · 275 across the 15 of their papers we have counts for
15 papers
Digital signatures with classical shadows on near-term quantum computers
Pradeep Niroula, Minzhao Liu, Sivaprasad Omanakuttan +15
Quantum mechanics provides cryptographic primitives whose security is grounded in hardness assumptions independent of those underlying classical cryptography. However, existing pro…
Certified randomness amplification by dynamically probing remote random quantum states
Minzhao Liu, Pradeep Niroula, Matthew DeCross +49
Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into n…
Spin-Boson Mapping of the Quantum Approximate Optimization Algorithm
Sami Boulebnane, Abid Khan, Minzhao Liu +4
The Quantum Approximate Optimization Algorithm (QAOA) achieves monotonically improving performance with circuit depth , yet the study of the high-depth regime has been obstructe…
Certified randomness using a trapped-ion quantum processor
Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula +29
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a…
Applications of Certified Randomness
Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty +8
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the ran…
Tensor networks for quantum computing
Aleksandr Berezutskii, Minzhao Liu, Atithi Acharya +25
In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications…
On the Equivalence between Classical Position Verification and Certified Randomness
Fatih Kaleoglu, Minzhao Liu, Kaushik Chakraborty +4
Gate-based quantum computers hold enormous potential to accelerate classically intractable computational tasks. Random circuit sampling (RCS) is the only known task that has been a…
The computational power of random quantum circuits in arbitrary geometries
Matthew DeCross, Reza Haghshenas, Minzhao Liu +49
Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimens…
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…
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-…
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…
Stochastic Approach For Simulating Quantum Noise Using Tensor Networks
William Berquist, Danylo Lykov, Minzhao Liu +1
Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In thi…
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…
Embedding Learning in Hybrid Quantum-Classical Neural Networks
Minzhao Liu, Junyu Liu, Rui Liu +4
Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning par…