Strong empiricalBasic circuitsBenchmark circuitQuantum machine learning3 q2,304–76,032 physical qubits ≈
Phase feature-map benchmark · 3 qubits
A fixed-data feature-map scaffold with Hadamards, local phase rotations, and pairwise ZZ encodings.
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.
4 entries · 5 records, sized variants folded
Atlas stars stay in this public list. Saving an entry to your workspace starts an unstarred private copy.
A fixed-data feature-map scaffold with Hadamards, local phase rotations, and pairwise ZZ encodings.
Detect the edges of a digital image: the pixel positions at which the image values change sharply. Processing digital images keeps growing in volume, with matching demands on data storage, transmission and processing power.
Detect anomalous, potentially fraudulent, transactions in a credit-card dataset, framed as an anomaly-detection task and compared against classical kernel-based benchmarks such as one-class support vector machines.
A quantum feature-map record paired with a classical SVM so model quality and data costs stay visible.