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Quantum Algorithms for Matrix Products over Semirings
In this paper we construct quantum algorithms for matrix products over
several algebraic structures called semirings, including the (max,min)-matrix
product, the distance matrix product and the Boolean matrix product. In
particular, we obtain the following results.
We construct a quantum algorithm computing the product of two n x n matrices
over the (max,min) semiring with time complexity O(n^{2.473}). In comparison,
the best known classical algorithm for the same problem, by Duan and Pettie,
has complexity O(n^{2.687}). As an application, we obtain a O(n^{2.473})-time
quantum algorithm for computing the all-pairs bottleneck paths of a graph with
n vertices, while classically the best upper bound for this task is
O(n^{2.687}), again by Duan and Pettie.
We construct a quantum algorithm computing the L most significant bits of
each entry of the distance product of two n x n matrices in time O(2^{0.64L}
n^{2.46}). In comparison, prior to the present work, the best known classical
algorithm for the same problem, by Vassilevska and Williams and Yuster, had
complexity O(2^{L}n^{2.69}). Our techniques lead to further improvements for
classical algorithms as well, reducing the classical complexity to
O(2^{0.96L}n^{2.69}), which gives a sublinear dependency on 2^L.
The above two algorithms are the first quantum algorithms that perform better
than the -time straightforward quantum algorithm based on
quantum search for matrix multiplication over these semirings. We also consider
the Boolean semiring, and construct a quantum algorithm computing the product
of two n x n Boolean matrices that outperforms the best known classical
algorithms for sparse matrices. For instance, if the input matrices have
O(n^{1.686...}) non-zero entries, then our algorithm has time complexity
O(n^{2.277}), while the best classical algorithm has complexity O(n^{2.373}).Comment: 19 page
Computational Difficulty of Global Variations in the Density Matrix Renormalization Group
The density matrix renormalization group (DMRG) approach is arguably the most
successful method to numerically find ground states of quantum spin chains. It
amounts to iteratively locally optimizing matrix-product states, aiming at
better and better approximating the true ground state. To date, both a proof of
convergence to the globally best approximation and an assessment of its
complexity are lacking. Here we establish a result on the computational
complexity of an approximation with matrix-product states: The surprising
result is that when one globally optimizes over several sites of local
Hamiltonians, avoiding local optima, one encounters in the worst case a
computationally difficult NP-hard problem (hard even in approximation). The
proof exploits a novel way of relating it to binary quadratic programming. We
discuss intriguing ramifications on the difficulty of describing quantum
many-body systems.Comment: 5 pages, 1 figure, RevTeX, final versio
Faster Algorithms for Rectangular Matrix Multiplication
Let {\alpha} be the maximal value such that the product of an n x n^{\alpha}
matrix by an n^{\alpha} x n matrix can be computed with n^{2+o(1)} arithmetic
operations. In this paper we show that \alpha>0.30298, which improves the
previous record \alpha>0.29462 by Coppersmith (Journal of Complexity, 1997).
More generally, we construct a new algorithm for multiplying an n x n^k matrix
by an n^k x n matrix, for any value k\neq 1. The complexity of this algorithm
is better than all known algorithms for rectangular matrix multiplication. In
the case of square matrix multiplication (i.e., for k=1), we recover exactly
the complexity of the algorithm by Coppersmith and Winograd (Journal of
Symbolic Computation, 1990).
These new upper bounds can be used to improve the time complexity of several
known algorithms that rely on rectangular matrix multiplication. For example,
we directly obtain a O(n^{2.5302})-time algorithm for the all-pairs shortest
paths problem over directed graphs with small integer weights, improving over
the O(n^{2.575})-time algorithm by Zwick (JACM 2002), and also improve the time
complexity of sparse square matrix multiplication.Comment: 37 pages; v2: some additions in the acknowledgment
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