4,172 research outputs found
Quantum SDP-Solvers: Better upper and lower bounds
Brand\~ao and Svore very recently gave quantum algorithms for approximately
solving semidefinite programs, which in some regimes are faster than the
best-possible classical algorithms in terms of the dimension of the problem
and the number of constraints, but worse in terms of various other
parameters. In this paper we improve their algorithms in several ways, getting
better dependence on those other parameters. To this end we develop new
techniques for quantum algorithms, for instance a general way to efficiently
implement smooth functions of sparse Hamiltonians, and a generalized
minimum-finding procedure.
We also show limits on this approach to quantum SDP-solvers, for instance for
combinatorial optimizations problems that have a lot of symmetry. Finally, we
prove some general lower bounds showing that in the worst case, the complexity
of every quantum LP-solver (and hence also SDP-solver) has to scale linearly
with when , which is the same as classical.Comment: v4: 69 pages, small corrections and clarifications. This version will
appear in Quantu
The Maximum Traveling Salesman Problem with Submodular Rewards
In this paper, we look at the problem of finding the tour of maximum reward
on an undirected graph where the reward is a submodular function, that has a
curvature of , of the edges in the tour. This problem is known to be
NP-hard. We analyze two simple algorithms for finding an approximate solution.
Both algorithms require oracle calls to the submodular function. The
approximation factors are shown to be and
, respectively; so the second
method has better bounds for low values of . We also look at how these
algorithms perform for a directed graph and investigate a method to consider
edge costs in addition to rewards. The problem has direct applications in
monitoring an environment using autonomous mobile sensors where the sensing
reward depends on the path taken. We provide simulation results to empirically
evaluate the performance of the algorithms.Comment: Extended version of ACC 2013 submission (including p-system greedy
bound with curvature
Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics
Quantum computing is powerful because unitary operators describing the
time-evolution of a quantum system have exponential size in terms of the number
of qubits present in the system. We develop a new "Singular value
transformation" algorithm capable of harnessing this exponential advantage,
that can apply polynomial transformations to the singular values of a block of
a unitary, generalizing the optimal Hamiltonian simulation results of Low and
Chuang. The proposed quantum circuits have a very simple structure, often give
rise to optimal algorithms and have appealing constant factors, while usually
only use a constant number of ancilla qubits. We show that singular value
transformation leads to novel algorithms. We give an efficient solution to a
certain "non-commutative" measurement problem and propose a new method for
singular value estimation. We also show how to exponentially improve the
complexity of implementing fractional queries to unitaries with a gapped
spectrum. Finally, as a quantum machine learning application we show how to
efficiently implement principal component regression. "Singular value
transformation" is conceptually simple and efficient, and leads to a unified
framework of quantum algorithms incorporating a variety of quantum speed-ups.
We illustrate this by showing how it generalizes a number of prominent quantum
algorithms, including: optimal Hamiltonian simulation, implementing the
Moore-Penrose pseudoinverse with exponential precision, fixed-point amplitude
amplification, robust oblivious amplitude amplification, fast QMA
amplification, fast quantum OR lemma, certain quantum walk results and several
quantum machine learning algorithms. In order to exploit the strengths of the
presented method it is useful to know its limitations too, therefore we also
prove a lower bound on the efficiency of singular value transformation, which
often gives optimal bounds.Comment: 67 pages, 1 figur
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