QAOA on qudit-encoded integer graph problems outperforms the Frieze-Jerrum SDP for Max-k-Cut at p≤4 in regimes k=3 d≤10 and k=4 d≤40, while a new degree-of-saturation heuristic beats both on GSet but may be overtaken by QAOA at p≤20.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3verdicts
UNVERDICTED 3representative citing papers
Quantum Gibbs samplers thermalize to Gibbs states in polynomial time at high temperatures for Lieb-Robinson bounded Hamiltonians and are BQP-complete at low temperatures via circuit-to-Hamiltonian reductions.
Except for a few specific cases, digital-analog quantum computation is disadvantageous compared to digital quantum computation based on scaling analysis across three quantum algorithms.
citing papers explorer
-
Quantum Approximate Optimization of Integer Graph Problems and Surpassing Semidefinite Programming for Max-k-Cut
QAOA on qudit-encoded integer graph problems outperforms the Frieze-Jerrum SDP for Max-k-Cut at p≤4 in regimes k=3 d≤10 and k=4 d≤40, while a new degree-of-saturation heuristic beats both on GSet but may be overtaken by QAOA at p≤20.
-
Efficient thermalization and universal quantum computing with quantum Gibbs samplers
Quantum Gibbs samplers thermalize to Gibbs states in polynomial time at high temperatures for Lieb-Robinson bounded Hamiltonians and are BQP-complete at low temperatures via circuit-to-Hamiltonian reductions.
-
Benchmarking Digital-Analog Quantum Computation
Except for a few specific cases, digital-analog quantum computation is disadvantageous compared to digital quantum computation based on scaling analysis across three quantum algorithms.