Quantum simulated annealing
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.
Every quantum algorithm worth knowing about, written down the same way: what it takes, what it returns, what it costs, and who proved it.
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14 public entries
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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.
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)).
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.
Given three n×n matrices A, B and C over a field, decide whether AB = C, rather than computing the product and comparing it entry by entry.
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.
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 N items carrying no promise about the underlying function, find two items that are equal, or establish that all N are distinct.
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.
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.
A coin-and-shift circuit that spreads a walker's position amplitude ballistically, in contrast to the diffusive spread of a classical random walk.
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.