699 citations · 2.5k across the 44 of their papers we have counts for
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Distributionally Robust Variational Quantum Algorithms with Shifted Noise
Zichang He, Bo Peng, Yuri Alexeev +1
Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been develo…
Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem
Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti +26
The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tac…
Fundamental causal bounds of quantum random access memories
Yunfei Wang, Yuri Alexeev, Liang Jiang +2
Quantum devices should operate in adherence to quantum physics principles. Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tas…
Quantum computing for finance
Dylan Herman, Cody Googin, Xiaoyuan Liu +5
Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehe…
Similarity-Based Parameter Transferability in the Quantum Approximate Optimization Algorithm
Alexey Galda, Eesh Gupta, Jose Falla +4
The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. A…
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 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…
QuYBE -- An Algebraic Compiler for Quantum Circuit Compression
Sahil Gulania, Zichang He, Bo Peng +2
QuYBE is an open-source algebraic compiler for the compression of quantum circuits. It has been applied for the efficient simulation of the Heisenberg Hamiltonian on quantum comput…
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…
Constructing Optimal Contraction Trees for Tensor Network Quantum Circuit Simulation
Cameron Ibrahim, Danylo Lykov, Zichang He +2
One of the key problems in tensor network based quantum circuit simulation is the construction of a contraction tree which minimizes the cost of the simulation, where the cost can…
Sampling Frequency Thresholds for Quantum Advantage of Quantum Approximate Optimization Algorithm
Danylo Lykov, Jonathan Wurtz, Cody Poole +3
In this work, we compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers such as Gurobi and MQLib to solve the comb…
Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer
Michelle Chalupnik, Hans Melo, Yuri Alexeev +1
The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount o…
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…
Performance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs
Danylo Lykov, Angela Chen, Huaxuan Chen +4
This work studies the porting and optimization of the tensor network simulator QTensor on GPUs, with the ultimate goal of simulating quantum circuits efficiently at scale on large…
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…
Localized Quantum Chemistry on Quantum Computers
Matthew Otten, Matthew R. Hermes, Riddhish Pandharkar +3
Quantum chemistry calculations of large, strongly correlated systems are typically limited by the computation cost that scales exponentially with the size of the system. Quantum al…
A Survey of Quantum Computing for Finance
Dylan Herman, Cody Googin, Xiaoyuan Liu +5
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, part…
Quantum time dynamics of 1D-Heisenberg models employing the Yang-Baxter equation for circuit compression
Sahil Gulania, Bo Peng, Yuri Alexeev +1
Quantum time dynamics (QTD) is considered a promising problem for quantum supremacy on near-term quantum computers. However, QTD quantum circuits grow with increasing time simulati…
Importance of Diagonal Gates in Tensor Network Simulations
Danylo Lykov, Yuri Alexeev
In this work we present two techniques that tremendously increase the performance of tensor-network based quantum circuit simulations. The techniques are implemented in the QTensor…
Transferability of optimal QAOA parameters between random graphs
Alexey Galda, Xiaoyuan Liu, Danylo Lykov +2
The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. I…
VQE Method: A Short Survey and Recent Developments
Dmitry A. Fedorov, Bo Peng, Niranjan Govind +1
The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues and eigenvalues of a Hamiltonian. VQE has been…
Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
Thomas Ayral, François-Marie Le Régent, Zain Saleem +2
Our recent work (Ayral et al., 2020 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)) showed the first implementation of the Quantum Divide and Compute (QDC) method, which a…
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
Xiaoyuan Liu, Anthony Angone, Ruslan Shaydulin +3
Combinatorial optimization on near-term quantum devices is a promising path to demonstrating quantum advantage. However, the capabilities of these devices are constrained by high n…