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arXiv:quant-ph/9806078 · Physical Review A

Nested quantum search and structured problems

Nicolas J. Cerf, Lov K. Grover, Colin P. Williams

Published Feb 9, 2000• 104 citations
arXivHTMLPDF

Abstract

A quantum algorithm is known that solves an unstructured search problem in a number of iterations of order $\sqrt{d},$ where d is the dimension of the search space, whereas any classical algorithm necessarily scales as $O(d).$ It is shown here that an improved quantum search algorithm can be devised that exploits the structure of a tree search problem by nesting one quantum search within another. The average number of iterations required to find the solution of a typical hard instance of a constraint satisfaction problem is found to scale as $\sqrt{{d}^{\ensuremath{\alpha}}},$ with the constant $\ensuremath{\alpha}<1$ depending on the nesting depth and the type of problem considered. This corresponds to a square-root speedup over a classical nested search algorithm, of which our algorithm is the quantum counterpart. When applying a single nesting level to a problem with constraints of size 2 such as the graph coloring problem, $\ensuremath{\alpha}$ is estimated to be around 0.62.

DOI
10.1103/physreva.61.032303

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A quantum algorithm is known that solves an unstructured search problem in a number of iterations of order d,\sqrt{d},d​, where d is the dimension of the search space, whereas any classical algorithm necessarily scales as O(d).O(d).O(d). It is shown here that an improved quantum search algorithm can be devised that exploits the structure of a tree search problem by nesting one quantum search within another. The average number of iterations required to find the solution of a typical hard instance of a constraint satisfaction problem is found to scale as d\ensuremathα,\sqrt{{d}^{\ensuremath{\alpha}}},d\ensuremathα​, with the constant \ensuremathα<1\ensuremath{\alpha}<1\ensuremathα<1 depending on the nesting depth and the type of problem considered. This corresponds to a square-root speedup over a classical nested search algorithm, of which our algorithm is the quantum counterpart. When applying a single nesting level to a problem with constraints of size 2 such as the graph coloring problem, \ensuremathα\ensuremath{\alpha}\ensuremathα is estimated to be around 0.62.
The study of quantum computation has been motivated by the hope of finding efficient quantum algorithms for solving classically hard problems. In this context, quantum algorithms by local adiabatic evolution have been shown to solve an unstructured search problem with a quadratic speedup over a classical search, just as Grover's algorithm. In this paper, we study how the structure of the search problem may be exploited to further improve the efficiency of these quantum adiabatic algorithms. We show that by nesting a partial search over a reduced set of variables into a global search, it is possible to devise quantum adiabatic algorithms with a complexity that, although still exponential, grows with a reduced order in the problem size.
In this paper we study search problems that arise very often in cryptanalysis: nested search problems, where each search layer has known degrees of freedom and/or constraints. A generic quantum solution for such problems consists of nesting Grover's quantum search algorithm or amplitude amplification (QAA) by Brassard et al., obtaining up to a square-root speedup on classical algorithms. However, the analysis of nested Grover or QAA is complex and introduces technicalities that in previous works are handled in a case-by-case manner. Moreover, straightforward nesting of l layers multiplies the complexity by a constant factor (pi/2)^l. In this paper, we aim to remedy both these issues and introduce a generic framework and tools to transform a classical nested search into a quantum procedure. It improves the state-of-the-art in three ways: 1) our framework results in quantum procedures that are significantly simpler to describe and analyze; 2) it reduces the overhead factor from (pi/2)^l to sqrt(l); 3) it is simpler to apply and optimize, without needing manual quantum analysis. We give generic complexity formulas and show that for concrete instances, numerical optimizations enable further improvements, reducing even more the gap to an exact quadratic speedup. We demonstrate our framework by giving a tighter analysis of quantum attacks on reduced-round AES.
Abstract Lattice-based cryptographic regimes are the most promising quantum-resistant cryptographic algorithms, whose security depends on the difficulty of the SVP. Enumeration algorithms are the most basic and hopeful algorithms instantiated for solving SVPs, and are often used in public key cryptanalysis or as subroutines of lattice reduction algorithms. In this paper, we show how to speed up enumeration with the idea of nested search: if N is the number of nodes in the enumeration tree and T is the time required to process a node, our quantum enumeration can find the shortest vector on the lattice in time T⋅N\sqrt{T\cdot N}T⋅N​ . This is applied to the two most efficient types of enumeration: cylinder pruning as well as discrete pruning. The core idea of the algorithm is to reduce the size of the search space by transforming the original problem into a constraint-satisfiable problem, using the pruning conditions of the above two enumerations as constraints, and embedding the search for all possible solutions at the next level of the enumeration tree into the space of partially satisfied solutions at the previous level. Next, we accelerate this process using a quantum search algorithm. Compared to the work of bib24, we further reduce the time complexity of the algorithm with multiplication factor $\sqrt{T} $ by the quantum nested search algorithm.
We develop three new quantum algorithms for searching the desired target state in the unstructured database of size N. The first algorithm requires Log N iterative steps. It constructs two quantum bags of equal size in terms of two quantum states, out of which exactly one quantum state will have nonzero overlap with the target state. This determination of overlap is done by taking the inner product, in Log N time [2], of the implicitly known target state with any one of these two quantum states. The second algorithm requires just one single step which uses a new suitable operator and the choice of this operator is problem dependent, i.e. it depends upon the number of qubits required to be used to represent an element in the index set. The third algorithm again requires only a single step and this algorithm makes use of a fixed (same) operator. It is known that algorithm for unstructured database search can be easily adaptable for solving NP-Complete problems. However, the computational complexity of NP-Complete problems after the adaptations of both the classical as well as quantum [1] search algorithms remains of the exponential order as the exponent for quantum [1] algorithm changes only to one-half times the exponent for classical algorithm. But for our quantum algorithms the exponent falls substantially so that our new quantum algorithms for unstructured search are capable if reducing the computational complexity of NP-Complete problems to polynomial order!
We introduce a quantum dynamic programming framework that allows us to directly extend to the quantum realm a large body of classical dynamic programming algorithms. The corresponding quantum dynamic programming algorithms retain the same space complexity as their classical counterpart, while achieving a computational speedup. For a combinatorial (search or optimization) problem P\mathcal PP and an instance III of P\mathcal PP, such a speedup can be expressed in terms of the average degree δδδ of the dependency digraph GP(I)G_{\mathcal{P}}(I)GP​(I) of III, determined by a recursive formulation of P\mathcal PP. The nodes of this graph are the subproblems of P\mathcal PP induced by III and its arcs are directed from each subproblem to those on whose solution it relies. In particular, our framework allows us to solve the considered problems in O~(∣V(GP(I))∣δ)\tilde{O}(|V(G_{\mathcal{P}}(I))| \sqrtδ)O~(∣V(GP​(I))∣δ​) time. As an example, we obtain a quantum version of the Bellman-Ford algorithm for computing shortest paths from a single source vertex to all the other vertices in a weighted nnn-vertex digraph with mmm edges that runs in O~(nnm)\tilde{O}(n\sqrt{nm})O~(nnm​) time, which improves the best known classical upper bound when m∈Ω(n1.4)m \in Ω(n^{1.4})m∈Ω(n1.4).