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20182026
most citedEmpirical performance bounds for quantum approximate optimization

60 citations · 178 across the 36 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

math.CO2022

Extending Grundy domination to -Grundy domination

Rebekah Herrman, Stephen G. Z. Smith

The Grundy domination number of a graph is the length of the longest sequence of unique vertices satisfying $N[v_i] \setminus \cup_{j=1}^{i-1}N…

quant-ph2022★ 3 cited

Approximate Boltzmann Distributions in Quantum Approximate Optimization

Phillip C. Lotshaw, George Siopsis, James Ostrowski +4

Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain…

quant-ph2022★ 18 cited

Multi-Angle QAOA Does Not Always Need All Its Angles

Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin +3

Introducing additional tunable parameters to quantum circuits is a powerful way of improving performance without increasing hardware requirements. A recently introduced multiangle…

quant-ph2022★ 2 cited

Relating the multi-angle quantum approximate optimization algorithm and continuous-time quantum walks on dynamic graphs

Rebekah Herrman

In this work, we show that ma-QAOA is equivalent to a restriction of continuous-time quantum walks on dynamic graphs. We then show it is universal for computation by finding the ap…

math.CO2022

Upper bound for the -leaky forcing number of and -leaky forcing number of

Rebekah Herrman

Leaky-forcing is a recently introduced variant of zero-forcing that has been studied for families of graphs including paths, cycles, wheels, grids, and trees. In this paper, we ext…

quant-ph2022★ 38 cited

Scaling Quantum Approximate Optimization on Near-term Hardware

Phillip C. Lotshaw, Thien Nguyen, Anthony Santana +5

The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial opt…

quant-ph2022

Solving MaxCut with Quantum Imaginary Time Evolution

Rizwanul Alam, George Siopsis, Rebekah Herrman +3

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state i…