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arXiv:1505.00113 · Quantum Information and Computation

The quantum complexity of approximating the frequency moments

Ashley Montanaro

Published Oct 1, 2016• 2 citations
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Abstract

The k’th frequency moment of a sequence of integers is defined as F_k = Sum_ j n^k _j , where n_j is the number of times that j occurs in the sequence. Here we study the quantum complexity of approximately computing the frequency moments in two settings. In the query complexity setting, we wish to minimise the number of queries to the input used to approximate F_k up to relative error epsilon. We give quantum algorithms which outperform the best possible classical algorithms up to quadratically. In the multiple-pass streaming setting, we see the elements of the input one at a time, and seek to minimise the amount of storage space, or passes over the data, used to approximate F_k. We describe quantum algorithms for F_0, F_2 and F_∞ in this model which substantially outperform the best possible classical algorithms in certain parameter regimes.

DOI
10.26421/qic16.13-14-5

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The k’th frequency moment of a sequence of integers is defined as F_k = Sum_ j n^k j , where n_j is the number of times that j occurs in the sequence. Here we study the quantum complexity of approximately computing the frequency moments in two settings. In the query complexity setting, we wish to minimise the number of queries to the input used to approximate F_k up to relative error epsilon. We give quantum algorithms which outperform the best possible classical algorithms up to quadratically. In the multiple-pass streaming setting, we see the elements of the input one at a time, and seek to minimise the amount of storage space, or passes over the data, used to approximate F_k. We describe quantum algorithms for F_0, F_2 and F∞ in this model which substantially outperform the best possible classical algorithms in certain parameter regimes.
We have seen in the previous chapters that quantum computers seem to be more powerful than classical computers for certain problems. There are limits on the power of quantum computers, however. Since a classical computer can simulate a quantum computer, a quantum computer can only compute the same set of functions that a classical computer can. The advantage of using a quantum computer is that the amount of resources needed by a quantum algorithm might be much less than what is needed by the best classical algorithm. In Section 9.1 we briefly define some classical and quantum complexity classes and give some relationships between them. Most of the interesting questions relating classical and quantum complexity classes remain open. For example, we do not yet know if a quantum computer is capable of efficiently solving an NP-complete problem (defined later). One can prove upper bounds on the difficulty of a problem by providing an algorithm that solves that problem, and proving that it will work within in a given running time. But how does one prove a lower bound on the computational complexity of a problem? For example, if we wish to find the product of two n-bit numbers, computing the answer requires outputting roughly 2n bits and that requires Ω(n) steps (in any computing model with finite-sized gates). The best-known upper bound for integer multiplication is O(n log n log log n) steps. It has proved extremely difficult to derive non-trivial lower bounds on the computational complexity of a problem. Most of the known non-trivial lower bounds are in the ‘black-box’ model (for both classical and quantum computing), where we only query the input via a ‘black-box’ of a specific form. We discuss the black-box model in more detail in Section 9.2. We then sketch several approaches for proving black-box lower bounds. The first technique has been called the ‘hybrid method’ and was used to prove that quantum searching requires Ω(√n) queries to succeed with constant probability. The second technique is called the ‘polynomial method’. We then describe a technique based on ‘block sensitivity’, and conclude with a technique known as the ‘adversary method’.
In this paper we provide new quantum algorithms with polynomial speed-up for a range of problems for which no such results were known, or we improve previous algorithms. First, we consider the approximation of the frequency moments FkF_kFk​ of order k≥3k \geq 3k≥3 in the multi-pass streaming model with updates (turnstile model). We design a PPP-pass quantum streaming algorithm with memory MMM satisfying a tradeoff of P2M=O~(n1−2/k)P^2 M = \tilde{O}(n^{1-2/k})P2M=O~(n1−2/k), whereas the best classical algorithm requires PM=Θ(n1−2/k)P M = Θ(n^{1-2/k})PM=Θ(n1−2/k). Then, we study the problem of estimating the number mmm of edges and the number ttt of triangles given query access to an nnn-vertex graph. We describe optimal quantum algorithms that perform O~(n/m1/4)\tilde{O}(\sqrt{n}/m^{1/4})O~(n​/m1/4) and O~(n/t1/6+m3/4/t)\tilde{O}(\sqrt{n}/t^{1/6} + m^{3/4}/\sqrt{t})O~(n​/t1/6+m3/4/t​) queries respectively. This is a quadratic speed-up compared to the classical complexity of these problems. For this purpose we develop a new quantum paradigm that we call Quantum Chebyshev's inequality. Namely we demonstrate that, in a certain model of quantum sampling, one can approximate with relative error the mean of any random variable with a number of quantum samples that is linear in the ratio of the square root of the variance to the mean. Classically the dependency is quadratic. Our algorithm subsumes a previous result of Montanaro [Mon15]. This new paradigm is based on a refinement of the Amplitude Estimation algorithm of Brassard et al. [BHMT02] and of previous quantum algorithms for the mean estimation problem. We show that this speed-up is optimal, and we identify another common model of quantum sampling where it cannot be obtained. For our applications, we also adapt the variable-time amplitude amplification technique of Ambainis [Amb10] into a variable-time amplitude estimation algorithm.
This paper investigates the computability of mathematical relations in a quantum query model. The important task in complexity theory is to find examples with a large gap between classical and quantum algorithm complexity of the same computational problem. We present new results in quantum query algorithm design that allow achieving a large separation between classical and quantum query complexity of a specific relation. We demonstrate an example where quantum query algorithm for a finite relation needs more than two times fewer queries than the best possible classical analogue. We also show that relation can be extended to infinite family of relations with an input of general size N.
Let X = (z,, , z,-,) be a sequence of n numbers.For 6 > 0, we say that 5; is an e-approximate median if the number of elements strictly less than zi and the number of elements strictly greater than zi are each less than (1 + 6):.We consider the quantum query complexity of computing an c-approximate median, given the sequence X as an oracle.We prove a lower bound of n(min{t,n}) queries for any quantum algorithm that computes an r-approximate median with any constant probability greater than l/2.We also show how an c-approximate median may be computed with 0( $ log(t) log log( $)) oracle queries, which rep resents an improvement over an earlier algorithm due to Grover [ll, 121.Thus, the lower bound we obtain is essentially optimal.The upper and the lower bound both hold in the comparison tree model as well.Our lower bound result is an application of the polynomial paradigm recently introduced to quantum complexity theory by Be& et ol.[l].The main ingredient in the proof is a polynomial degree lower bound far real multilinear polynomials that "approximate" symmetric partial boolean functions.The degree bound extends a result of Patti[15] and also immediately yields lower bounds for the problems of approximating the kth-smallest element, approximating the mean of a sequence of numbers, and approximately counting the number of ones of a boolean function.All bounds obtained come within a polylogarithmic factor of the optimal (as we show by presenting algorithms where no such optimal or near optimal algorithms were known), thus demonstrating the power of the polynomial method.
Near-term quantum devices with limited qubits motivate the study of space-bounded quantum computation in the data stream model. We show that Shannon entropy estimation exhibits an exponential separation between quantum and classical space complexity in this setting. Technically, we develop a two-stage quantum streaming algorithm based on a quantum procedure with an explicitly constructed oracle derived from the streaming input. This algorithm achieves logarithmic space complexity in the accuracy parameter over the data stream, whereas any classical streaming algorithm under the same pass complexity requires polynomial space. In sharp contrast, existing results for Shannon entropy estimation in the quantum query model achieve only a quadratic speedup. Our work establishes a natural problem with practical applications in computer networking that admits an exponential quantum space advantage, revealing a fundamental gap between quantum query complexity and streaming space complexity.