Avinatan Hassidim

Quantum Algorithm for Linear Systems of Equations

Solving linear systems of equations is a common problem that arises both on its own and as a subroutine in more complex problems: given a matrix A and a vector b(-->), find a vector x(-->) such that Ax(-->) = b(-->). We consider the case where one does not need to know the solution x(-->) itself, but rather an approximation of the expectation value of some operator associated with x(-->), e.g., x(-->)(dagger) Mx(-->) for some matrix M. In this case, when A is sparse, N x N and has condition number kappa, the fastest known classical algorithms can find x(-->) and estimate x(-->)(dagger) Mx(-->) in time scaling roughly as N square root(kappa). Here, we exhibit a quantum algorithm for estimating x(-->)(dagger) Mx(-->) whose runtime is a polynomial of log(N) and kappa. Indeed, for small values of kappa [i.e., poly log(N)], we prove (using some common complexity-theoretic assumptions) that any classical algorithm for this problem generically requires exponentially more time than our quantum algorithm.

Broadcasting with Side Information

A sender holds a word x consisting of n blocks xi, each of t bits, and wishes to broadcast a codeword to m receivers, R1,...,Rm. Each receiver Ri is interested in one block, and has prior side information consisting of some subset of the other blocks. Let betat be the minimum number of bits that has to be transmitted when each block is of length t, and let beta be the limit beta=limtrarrinfinbetat/t. Informally, beta is the average communication cost per bit in each block (for long blocks). Finding the coding rate beta, for such an informed broadcast setting, generalizes several coding theoretic parameters related to Informed Source Coding on Demand, Index Coding and Network Coding. In this work we show that usage of large data blocks may strictly improve upon the trivial encoding which treats each bit in the block independently. To this end, we provide general bounds on betat, and prove that for any constant C there is an explicit broadcast setting in which beta = 2 but beta1> C. One of these examples answers a question of . In addition, we provide examples with the following counterintuitive direct-sum phenomena. Consider a union of several mutually independent broadcast settings. The optimal code for the combined setting may yield a significant saving in communication over concatenating optimal encodings for the individual settings. This result also provides new non-linear coding schemes which improve upon the largest known gap between linear and non-linear Network Coding, thus improving the results of. The proofs are based on a relation between this problem and results in the study of Witsenhausens rate, OR graph products, colorings of Cayley graphs, and the chromatic numbers of Kneser graphs.

Non-price equilibria in markets of discrete goods

We study markets of indivisible items in which price-based (Walrasian) equilibria often do not exist due to the discrete non-convex setting. Instead we consider Nash equilibria of the market viewed as a game, where players bid for items, and where the highest bidder on an item wins it and pays his bid. We first observe that pure Nash-equilibria of this game excatly correspond to price-based equilibiria (and thus need not exist), but that mixed-Nash equilibria always do exist, and we analyze their structure in several simple cases where no price-based equilibrium exists. We also undertake an analysis of the welfare properties of these equilibria showing that while pure equilibria are always perfectly efficient (“first welfare theorem”), mixed equilibria need not be, and we provide upper and lower bounds on their amount of inefficiency.

Monotonicity and Implementability

Consider an environment with a finite number of alternatives, and agents with private values and quasilinear utility functions. A domain of valuation functions for an agent is a monotonicity domain if every finite-valued monotone randomized allocation rule defined on it is implementable in dominant strategies. We fully characterize the set of all monotonicity domains. Copyright 2010 The Econometric Society.

Local Graph Partitions for Approximation and Testing

We introduce a new tool for approximation and testing algorithms called partitioning oracles. We develop methods for constructing them for any class of bounded-degree graphs with an excluded minor, and in general, for any hyperfinite class of bounded-degree graphs. These oracles utilize only local computation to consistently answer queries about a global partition that breaks the graph into small connected components by removing only a small fraction of the edges. We illustrate the power of this technique by using it to extend and simplify a number of previous approximation and testing results for sparse graphs, as well as to provide new results that were unachievable with existing techniques. For instance:1. We give constant-time approximation algorithms for the size of the minimum vertex cover, the minimum dominating set, and the maximum independent set for any class of graphs with an excluded minor.2. We show a simple proof that any minor-closed graph property is testable in constant time in the bounded degree model.3. We prove that it is possible to approximate the distance to almost any hereditary property in any bounded degree hereditary families of graphs. Hereditary properties of interest include bipartiteness, k-colorability, and perfectness.

Stability in Large Matching Markets with Complementarities

Labor markets can often be viewed as many-to-one matching markets. It is well known that if complementarities are present in such markets, a stable matching may not exist. We study large random matching markets with couples. We introduce a new matching algorithm and show that if the number of couples grows slower than the size of the market, a stable matching will be found with high probability. If however, the number of couples grows at a linear rate, with constant probability not depending on the market size, no stable matching exists. Our results explain data from the market for psychology interns.

The Bayesian Learner is Optimal for Noisy Binary Search (and Pretty Good for Quantum as Well)

We use a Bayesian approach to optimally solve problems in noisy binary search. We deal with two variants:1. Each comparison is erroneous with independent probability 1-p. 2. At each stage k comparisons can be performed in parallel and a noisy answer is returned. We present a (classical) algorithm which solves both variants optimally (with respect to p and k), up to an additive term of O(loglog n), and prove matching information-theoretic lower bounds. We use the algorithm to improve the results of Farhi et al., presenting an exact quantum search algorithm in an ordered list of expected complexity less than (log2 n)/3.

Fast quantum byzantine agreement

We present a fast quantum Byzantine Agreement protocol that can reach agreement in <i>O</i>(1) expected communication rounds against a strong full information, dynamic adversary, tolerating up to the optimal <i>t</i>‹<i>n</i>3 faulty players in the synchronous setting, and up to <i>t</i>‹<i>n</i>4 faulty players for asynchronous systems. This should be contrasted with the known classical synchronous lower bound of Ω(√ <i>n</i>log <i>n</i>) [3] when <i>t</i>=(<i>n</i>).

How to split a flow

Many practically deployed flow algorithms produce the output as a set of values associated with the network links. However, to actually deploy a flow in a network we often need to represent it as a set of paths between the source and destination nodes. In this paper we consider the problem of decomposing a flow into a small number of paths. We show that there is some fixed constant β >; 1 such that it is NP-hard to find a decomposition in which the number of paths is larger than the optimal by a factor of at most β. Furthermore, this holds even if arcs are associated only with three different flow values. We also show that straightforward greedy algorithms for the problem can produce much larger decompositions than the optimal one, on certain well tailored inputs. On the positive side we present a new approximation algorithm that decomposes all but an c-fraction of the flow into at most O(1/ϵ2) times the smallest possible number of paths. We compare the decompositions produced by these algorithms on real production networks and on synthetically generated data. Our results indicate that the dependency of the decomposition size on the fraction of flow covered is exponential. Hence, covering the last few percent of the flow may be costly, so if the application allows, it may be a good idea to decompose most but not all the flow. The experiments also reveal the fact that while for realistic data the greedy approach works very well, our novel algorithm which has a provable worst case guarantee, typically produces only slightly larger decompositions.

On finding an optimal TCAM encoding scheme for packet classification

Hardware-based packet classification has become an essential component in many networking devices. It often relies on TCAMs (ternary content-addressable memories), which need to compare the packet header against a set of rules. But efficiently encoding these rules is not an easy task. In particular, the most complicated rules are range rules, which usually require multiple TCAM entries to encode them. However, little is known on the optimal encoding of such non-trivial rules. In this work, we take steps towards finding an optimal encoding scheme for every possible range rule. We first present an optimal encoding for all possible generalized extremal rules. Such rules represent 89% of all non-trivial rules in a typical real-life classification database. We also suggest a new method of simply calculating the optimal expansion of an extremal range, and present a closed-form formula of the average optimal expansion over all extremal ranges. Next, we present new bounds on the worst-case expansion of general classification rules, both in one-dimensional and two-dimensional ranges. Last, we introduce a new TCAM architecture that can leverage these results by providing a guaranteed expansion on the tough rules, while dealing with simpler rules using a regular TCAM. We conclude by verifying our theoretical results in experiments with synthetic and real-life classification databases.