Attested & literatureQAOA
The Quantum Approximate Optimization Algorithm
A quantum algorithm that produces approximate solutions for combinatorial optimization problems, tunable by a positive integer p.
Explore quantum algorithms and circuits, with resource estimates, verification evidence, and sources.
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A quantum algorithm that produces approximate solutions for combinatorial optimization problems, tunable by a positive integer p.
Find a better parameterized ansatz for the quantum approximate optimization algorithm (QAOA) applied to combinatorial optimization problems such as Max-Cut, where the standard, fixed-form QAOA ansatz is not known to be optimal and no systematic method exists for improving on it.
Evaluate candidate designs for a simplified cooling system within an engineering design process that normally requires numerous computationally intensive numerical simulations, in a way that avoids the data input/output overhead that otherwise erodes any quantum speedup on such simulation tasks.
Given the low autocorrelation binary sequences (LABS) problem, an optimization problem that is classically intractable even for moderately sized instances, determine whether QAOA can act as an algorithmic component that provides an advantage over the best classical exact solvers.
Solve large-scale Maximum Cut (MaxCut) problems on near-term quantum hardware by decomposing the graph into many subgraph problems that can be solved in parallel, and determine how such a decomposition compares to a purely classical alternative for the same task.
A p=1 QAOA circuit with a classical MaxCut comparison and reproducible simulation evidence.
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