Every quantum algorithm worth knowing about, written down the same way: what it takes, what it returns, what it costs, and who proved it.
The Map draws our corpus as one connected structure: Open the Map
Every source behind both surfaces: See the papers
A speedup class on a record is quoted: See whose claim it is
Every record is classified by how it was verified. The badge shows the strongest tier of evidence; the chips list each method that applies.
The defining behavior was checked exactly: a mathematical identity, a full statevector or stabilizer simulation, or an exhaustive basis-state truth table.
The design was verified by construction plus measured evidence: statistical re-execution, small-instance analytic agreement, sub-block, echo, or invariant checks. Scale-specific bugs can still survive.
The record rests on external authority: peer-reviewed papers, standard textbooks, expert review, or evidence carried over from related verified entries. Nothing here was re-executed by this catalog.
Only automated (LLM-assisted) review or an unreviewed community submission backs this record so far. Treat it as a starting point, not evidence.
7 entries · 8 records, sized variants folded
Atlas stars stay in this public list. Saving an entry to your workspace starts an unstarred private copy.
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.
A fixed-angle p=1 MaxCut-style QAOA layer on a cycle graph, including cost and mixer blocks.
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.
A quantum algorithm that produces approximate solutions for combinatorial optimization problems, tunable by a positive integer p.
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.