5,073 research outputs found
New summing algorithm using ensemble computing
We propose an ensemble algorithm, which provides a new approach for
evaluating and summing up a set of function samples. The proposed algorithm is
not a quantum algorithm, insofar it does not involve quantum entanglement. The
query complexity of the algorithm depends only on the scaling of the
measurement sensitivity with the number of distinct spin sub-ensembles. From a
practical point of view, the proposed algorithm may result in an exponential
speedup, compared to known quantum and classical summing algorithms. However in
general, this advantage exists only if the total number of function samples is
below a threshold value which depends on the measurement sensitivity.Comment: 13 pages, 0 figures, VIth International Conference on Quantum
Communication, Measurement and Computing (Boston, 2002
Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer
The D-Wave adiabatic quantum annealer solves hard combinatorial optimization
problems leveraging quantum physics. The newest version features over 1000
qubits and was released in August 2015. We were given access to such a machine,
currently hosted at NASA Ames Research Center in California, to explore the
potential for hard optimization problems that arise in the context of
databases.
In this paper, we tackle the problem of multiple query optimization (MQO). We
show how an MQO problem instance can be transformed into a mathematical formula
that complies with the restrictive input format accepted by the quantum
annealer. This formula is translated into weights on and between qubits such
that the configuration minimizing the input formula can be found via a process
called adiabatic quantum annealing. We analyze the asymptotic growth rate of
the number of required qubits in the MQO problem dimensions as the number of
qubits is currently the main factor restricting applicability. We
experimentally compare the performance of the quantum annealer against other
MQO algorithms executed on a traditional computer. While the problem sizes that
can be treated are currently limited, we already find a class of problem
instances where the quantum annealer is three orders of magnitude faster than
other approaches
Quantum Algorithms for Some Hidden Shift Problems
Almost all of the most successful quantum algorithms discovered to date exploit the ability of the Fourier transform to recover subgroup structures of functions, especially periodicity. The fact that Fourier transforms can also be used to capture shift structure has received far less attention in the context of quantum computation. In this paper, we present three examples of "unknown shift" problems that can be solved efficiently on a quantum computer using the quantum Fourier transform. For one of these problems, the shifted Legendre symbol problem, we give evidence that the problem is hard to solve classically, by showing a reduction from breaking algebraically homomorphic cryptosystems. We also define the hidden coset problem, which generalizes the hidden shift problem and the hidden subgroup problem. This framework provides a unified way of viewing the ability of the Fourier transform to capture subgroup and shift structure
Hamiltonian Simulation Using Linear Combinations of Unitary Operations
We present a new approach to simulating Hamiltonian dynamics based on
implementing linear combinations of unitary operations rather than products of
unitary operations. The resulting algorithm has superior performance to
existing simulation algorithms based on product formulas and, most notably,
scales better with the simulation error than any known Hamiltonian simulation
technique. Our main tool is a general method to nearly deterministically
implement linear combinations of nearby unitary operations, which we show is
optimal among a large class of methods.Comment: 18 pages, 3 figure
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