Every record is classified by how it was verified. The badge shows the strongest tier of evidence; the chips list each method that applies.
≡Exact & formal
The defining behavior was checked exactly: a mathematical identity, a full statevector or stabilizer simulation, or an exhaustive basis-state truth table.
Direct mathematics
An analytic identity or closed-form derivation proves the defining property (e.g. H² = I, Y = iXZ).
Unitary / matrix equivalence
The circuit's unitary was computed and compared against the reference matrix.
Exact statevector simulation
A full statevector simulation reproduced the exact expected state or distribution.
Reversible classical logic was checked exhaustively (or at edge cases) on computational-basis inputs.
✓Strong empirical
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.
Statistical re-execution
Measured counts from independent executions agreed within statistical tolerance (e.g. TVD bounds).
Small-instance agreement
The same generator matched analytic results at tractable sizes; large instances are inferred.
The circuit follows a specification whose correctness argument is standard; the construction was audited against it.
◐Attested & literature
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.
Peer-reviewed paper
The record's claims trace to one or more peer-reviewed publications cited on the entry.
Textbook / standard citation
The behavior is standard material (e.g. Nielsen & Chuang, OpenQASM spec) and is cross-referenced, not re-derived.
Expert review
A named human reviewer with domain expertise checked the record.
Tangential evidence
Correctness is supported indirectly through related verified entries (e.g. a gate verified inside a verified algorithm).
○Automated & unreviewed
Only automated (LLM-assisted) review or an unreviewed community submission backs this record so far. Treat it as a starting point, not evidence.
LLM-assisted review
An LLM checked the record for internal consistency. Useful screening, not evidence of correctness.
Community submission
Submitted by the community and not yet through the review pipeline.
14 public entries
Atlas stars stay in this public list. Saving an entry to your workspace starts an unstarred private copy.
◐Attested & literatureAlgorithmsAlgorithmQuantum walk
Backtracking is the general classical technique for exploiting problem structure in constraint satisfaction: explore a tree of partial assignments and prune the branches a predicate rules out. The question is whether an arbitrary backtracking algorithm — any predicate and any branching heuristic — can be sped up quantumly, rather than replaced by brute-force search over the whole assignment space.
Given oracle access to a weighted graph on N vertices of maximum degree d whose edge weights are conductances, so that an edge of weight w carries resistance 1/w, estimate the effective resistance between a chosen pair of vertices s and t to within a factor of 1 + ε. Wang poses it as ENA-ER: for a network with |V| = N, deg(G) ≤ d, edge conductances normalized to 1 ≤ w_e ≤ c for every edge — equivalently edge resistances in [1/c, 1] — and spectral gap λ2(L_G) ≥ λ > 0, estimate R_eff(s, t) up to multiplicative error ε, succeeding with probability at least 2/3, given a procedure Pv that on input a vertex index and a number k returns the k-th edge incident to that vertex and a procedure Pe that on input an edge index returns that edge's two endpoints and its weight, both of which Wang assumes can be implemented in time poly(log(N)).
effective resistanceelectrical networkquantum walk
Determine the role quantum dynamical effects play in the efficiency of exciton (energy) transfer within photosynthetic molecular arrays — such as the Fenna-Matthews-Olson (FMO) protein complex, whose long-lived coherence had recently been demonstrated experimentally — that interact with a thermal bath.
We are given an undirected graph G on n vertices, known explicitly in advance, together with oracle access to a labeling of the vertices by 1 and 0; the graph collision problem is to decide, by querying that labeling, whether there exist a pair of vertices, connected by an edge, both of which are labeled 1. Only the labeling is queried and never the graph, which is why Magniez, Santha and Szegedy can restate the task as deciding whether the set of vertices of value 1 forms an independent set in G. It is not either of the two problems it is easily confused with: collision finding asks for two arguments of a two-to-one function that share a value, and element distinctness drops that promise and asks whether any two of N items are equal. Those two share a single other Zoo entry, Collision Finding and Element Distinctness, and a single record here, element-distinctness; neither of them has a graph or an edge relation in it.
Given oracle access to k matrices of size n × n, where a query names a matrix index x together with a pair of indices i, j and returns the ij entry of the x-th matrix, decide whether all k of the matrices commute with one another.
Determine the value at the root of a read-once Boolean formula, canonically a NAND tree on N variables, given only oracle access to the variables and using as few queries as possible.
Predict the three-dimensional structure of a protein from its amino-acid sequence, addressed here by parameterizing the protein in terms of the torsion angles of its amino acids rather than by a lattice-model simplification.
Sample from the final limiting distribution πₙ of a slowly varying sequence of Markov chains, where πₙ is chosen to concentrate on good solutions of a combinatorial optimization problem.
In L-subset finding, a black box f maps a domain D of size N into a range R, and a property P picks out some L-element subsets by their arguments and values. The task is to find an L-subset {x₁, …, x_L} of D whose pairs ((x₁, f(x₁)), …, (x_L, f(x_L))) satisfy P, or to reject if none exists, using as few queries as possible. Unstructured search is the case L = 1 and element distinctness the case L = 2.
Given integers x₁, x₂, …, xₙ and s, decide whether some subset I of {1, 2, …, n} satisfies the sum of xᵢ over I equal to s. The authors take as the typical hard case that the xᵢ are independent uniform random integers in {0, 1, …, 2ⁿ}. Subset-sum was one of the first problems shown NP-complete, so the question the paper asks is not whether it can be solved in polynomial time but how far below the 2ⁿ cost of searching every subset an exponential-time algorithm can get.
Given oracle access to a graph built by joining two depth-n binary trees with a random weld, so that every node except the two roots has degree three, start from the label of one root and find the label of the other root.