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arXiv:quant-ph/0408013

A quantum algorithm for examining oracles

Hiroo Azuma

Published Aug 3, 2004• 1 citation
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Abstract

In this paper, we consider a quantum algorithm for solving the following problem: ``Suppose $f$ is a function given as a black box (that is also called an oracle) and $f$ is invariant under some AND-mask. Examine a property of $f$ by querying the oracle.'' We compare the efficiency of our quantum algorithm with that of classical algorithms by evaluating the expected number of queries for each algorithm. We show that our quantum algorithm is more efficient than any classical algorithm in some cases. However, our quantum algorithm does not exhibit an exponential speedup in the size of an input, compared with the best classical algorithm. Our algorithm extracts a global property of $f$ (that is, invariance of $f$) while it neglects local properties of $f$ (that is, outputs of $f$). We can regard our algorithm as an application of Simon's algorithm.

DOI
10.48550/arxiv.quant-ph/0408013

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In this paper, we consider a quantum algorithm for solving the following problem: ``Suppose fff is a function given as a black box (that is also called an oracle) and fff is invariant under some AND-mask. Examine a property of fff by querying the oracle.'' We compare the efficiency of our quantum algorithm with that of classical algorithms by evaluating the expected number of queries for each algorithm. We show that our quantum algorithm is more efficient than any classical algorithm in some cases. However, our quantum algorithm does not exhibit an exponential speedup in the size of an input, compared with the best classical algorithm. Our algorithm extracts a global property of fff (that is, invariance of fff) while it neglects local properties of fff (that is, outputs of fff). We can regard our algorithm as an application of Simon's algorithm.
This presentation gives a comprehensive overview of quantum query algorithms, beginning with an introduction to the query model of computation where input is accessed via an oracle function. It then details several foundational quantum algorithms that demonstrate a quantum advantage over classical approaches: Deutsch's problem is solved with a single query using phase kickback to determine if a function is constant or balanced; the Deutsch-Jozsa algorithm extends this to n-bit inputs, efficiently distinguishing constant from balanced functions with one query versus an exponential need classically; the Bernstein-Vazirani algorithm identifies a hidden string with one query, outperforming classical linear requirements; and Simon's algorithm, which finds a hidden period in a two-to-one function with a linear number of queries, showcases an exponential speedup over the best classical probabilistic algorithms.
We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with a simplification of recent classical work of Lee, Sidford, and Vempala gives our efficient separation oracle. This in turn implies, via a known algorithm, that O~(n) quantum queries to a membership oracle suffice to implement an optimization oracle (the best known classical upper bound on the number of membership queries is quadratic). We also prove several lower bounds: Ω(n) quantum separation (or membership) queries are needed for optimization if the algorithm knows an interior point of the convex set, and Ω(n) quantum separation queries are needed if it does not.
Let a classical algorithm be determined by sequential applications of a black box performing one step of this algorithm. If we consider this black box as an oracle which gives a value F(a) for any query a, we can compute T sequential applications of F on a classical computer relative to this oracle in time T. It is proved that if T=O(2^{n/7}), where n is the length of input, then the result of T sequential applications of F can not be computed on quantum computer with oracle for F for all possible F faster than in time Ω(T). This means that there is no general method of quantum speeding up of classical algorithms provided in such a general method a classical algorithm is regarded as iterated applications of a given black box. For an arbitrary time complexity T a lower bound for the time of quantum simulation was found to be Ω(T^{1/2}).
We study the problem of learning an unknown graph provided via an oracle using a quantum algorithm. We consider three query models. In the first model (OR queries), the oracle returns whether a given subset of the vertices contains any edges. In the second (parity queries), the oracle returns the parity of the number of edges in a subset. In the third model, we are given copies of the graph state corresponding to the graph. We give quantum algorithms that achieve speedups over the best possible classical algorithms in the OR and parity query models, for some families of graphs, and give quantum algorithms in the graph state model whose complexity is similar to the parity query model. For some parameter regimes, the speedups can be exponential in the parity query model. On the other hand, without any promise on the graph, no speedup is possible in the OR query model. A main technique we use is the quantum algorithm for solving the combinatorial group testing problem, for which a query-efficient quantum algorithm was given by Belovs. Here we additionally give a time-efficient quantum algorithm for this problem, based on the algorithm of Ambainis et al. for a gapped version of the group testing problem. We also give simple time-efficient quantum algorithms based on Fourier sampling and amplitude amplification for learning the exact-half and majority functions, which almost match the optimal complexity of Belovs' algorithms.
The search for ``a quantum needle in a quantum haystack'' is a metaphor for the problem of finding out which one of a permissible set of unitary mappings---the oracles---is implemented by a given black box. Grover's algorithm solves this problem with quadratic speedup as compared with the analogous search for ``a classical needle in a classical haystack.'' Since the outcome of Grover's algorithm is probabilistic---it gives the correct answer with high probability, not with certainty---the answer requires verification. For this purpose we introduce specific test states, one for each oracle. These test states can also be used to realize ``a classical search for the quantum needle'' which is deterministic---it always gives a definite answer after a finite number of steps---and 3.41 times as fast as the purely classical search. Since the test-state search and Grover's algorithm look for the same quantum needle, the average number of oracle queries of the test-state search is the classical benchmark for Grover's algorithm.