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20182022
most citedPerformance Evaluation and Acceleration of the QTensor Quantum Circuit Simulator on GPUs

22 citations · 30 across the 5 of their papers we have counts for

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quant-ph2022

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…

quant-ph2022

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…

quant-ph202222 cited

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…

quant-ph20214 cited

The fixed angle conjecture for QAOA on regular MaxCut graphs

Jonathan Wurtz, Danylo Lykov

The quantum approximate optimization algorithm (QAOA) is a near-term combinatorial optimization algorithm suitable for noisy quantum devices. However, little is known about perform…

quant-ph20214 cited

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…

quant-ph2021

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…