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Annual Symposium on Foundations of Computer Science

The Polynomial Method in Quantum and Classical Computing

Scott Aaronson

Published Oct 1, 2008• 17 citations
Publisher

Abstract

In 1889, A. A. Markov proved a powerful result about low-degree real polynomials: roughly speaking, that such polynomials cannot have a sharp jump followed by a long, relatively flat part. A century later, this result - as well as other results from the field of approximation theory - came to play a surprising role in classical and quantum complexity theory. In this article, the author tries to tell this story in an elementary way, beginning with classic results in approximation theory and ending with some recent applications.

DOI
10.1109/focs.2008.91

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In 1889, A. A. Markov proved a powerful result about low-degree real polynomials: roughly speaking, that such polynomials cannot have a sharp jump followed by a long, relatively flat part. A century later, this result - as well as other results from the field of approximation theory - came to play a surprising role in classical and quantum complexity theory. In this article, the author tries to tell this story in an elementary way, beginning with classic results in approximation theory and ending with some recent applications.
The ability (or inability) to represent or approximate Boolean functions by polynomials is a central concept in complexity theory, underlying interactive and probabilistically checkable proof systems, circuit lower bounds, quantum complexity theory, and more. In this book, the authors survey what is known about a particularly natural notion of approximation by polynomials, capturing pointwise approximation over This book covers recent progress on proving approximate degree lower and upper bounds and describes some applications of the new bounds to oracle separations, quantum query and communication complexity, and circuit complexity. The authors explain how several of these advances have been unlocked by a particularly simple and elegant technique, called dual block composition, for constructing solutions to this dual linear program. They also provide concise coverage of even more recent lower bound techniques based on a new complexity measure called spectral sensitivity. Finally, they show how explicit constructions of approximating polynomials have been inspired by quantum query algorithms. This book provides a comprehensive review of the foundational and recent developments of an important topic in both classical and quantum computing. The reader has a considerable body of knowledge condensed in an accessible form to quickly understand the principles and further their own research.
Quantum Hamiltonian Complexity (QHC) sits at the intersection of quantum complexity theory and quantum many-body physics. However, many results in QHC still remain disconnected across domains. The technique of polynomial approximations, derived from classical approximation theory methods, has recently seen remarkable success at proving results in QHC, and may change this detached paradigm. This thesis aims to better unify results in QHC by applying polynomial approximations to provide an alternative proof of the Lieb-Schultz-Mattis (LSM) theorem, a seminal result in condensed matter physics. The LSM theorem provides an upper bound on the spectral gap of quantum many-body systems under certain symmetry conditions, and is deeply related to topological quantum computing. We hope to provide a more elementary proof of LSM based in classical techniques, such as concentration bounds, and demonstrate new applications of the polynomial approximation technique. In this work, we outline the general approach for using polynomial approximations to prove the LSM theorem for a 1D periodic spin-1/2 chain, and characterize the valid ground states for this system. We also provide progress towards this end in the form of examples of unconcentrated LSM ground states, as well as discussing the uniform superposition ground state and alternate perspectives on a full proof or counterexample.
The approximate degree of a Boolean function f captures how well f can be approximated pointwise by low-degree polynomials. This monograph surveys what is known about approximate degree and illustrates its applications in theoretical computer science. A particular focus of the survey is a method of proving lower bounds via objects called dual polynomials. These represent a reformulation of approximate degree using linear programming duality. We discuss in detail a recent, powerful technique for constructing dual polynomials, called “dual block composition”.
The approximate degree of a Boolean function f(x1,x2,…,xn)f(x_{1},x_{2},\ldots,x_{n})f(x1​,x2​,…,xn​) is the minimum degree of a real polynomial that approximates fff pointwise within 1/31/31/3. Upper bounds on approximate degree have a variety of applications in learning theory, differential privacy, and algorithm design in general. Nearly all known upper bounds on approximate degree arise in an existential manner from bounds on quantum query complexity. We develop a first-principles, classical approach to the polynomial approximation of Boolean functions. We use it to give the first constructive upper bounds on the approximate degree of several fundamental problems: - O(n34−14(2k−1))O\bigl(n^{\frac{3}{4}-\frac{1}{4(2^{k}-1)}}\bigr)O(n43​−4(2k−1)1​) for the kkk-element distinctness problem; - O(n1−1k+1)O(n^{1-\frac{1}{k+1}})O(n1−k+11​) for the kkk-subset sum problem; - O(n1−1k+1)O(n^{1-\frac{1}{k+1}})O(n1−k+11​) for any kkk-DNF or kkk-CNF formula; - O(n3/4)O(n^{3/4})O(n3/4) for the surjectivity problem. In all cases, we obtain explicit, closed-form approximating polynomials that are unrelated to the quantum arguments from previous work. Our first three results match the bounds from quantum query complexity. Our fourth result improves polynomially on the Θ(n)Θ(n)Θ(n) quantum query complexity of the problem and refutes the conjecture by several experts that surjectivity has approximate degree Ω(n)Ω(n)Ω(n). In particular, we exhibit the first natural problem with a polynomial gap between approximate degree and quantum query complexity.
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’.
We analyze the connections between the mathematical theory of knots and quantum physics by addressing a number of algorithmic questions related to both knots and braid groups. Knots can be distinguished by means of “knot invariants,” among which the Jones polynomial plays a prominent role, since it can be associated with observables in topological quantum field theory. Although the problem of computing the Jones polynomial is intractable in the framework of classical complexity theory, it has been recently recognized that a quantum computer is capable of approximating it in an efficient way. The quantum algorithms discussed here represent a breakthrough for quantum computation, since approximating the Jones polynomial is actually a “universal problem,” namely, the hardest problem that a quantum computer can efficiently handle.