35,086 research outputs found
Quantum computers can search rapidly by using almost any transformation
A quantum computer has a clear advantage over a classical computer for
exhaustive search. The quantum mechanical algorithm for exhaustive search was
originally derived by using subtle properties of a particular quantum
mechanical operation called the Walsh-Hadamard (W-H) transform. This paper
shows that this algorithm can be implemented by replacing the W-H transform by
almost any quantum mechanical operation. This leads to several new applications
where it improves the number of steps by a square-root. It also broadens the
scope for implementation since it demonstrates quantum mechanical algorithms
that can readily adapt to available technology.Comment: This paper is an adapted version of quant-ph/9711043. It has been
modified to make it more readable for physicists. 9 pages, postscrip
Quantum search on structured problems
This paper shows how a basic property of unitary transformations can be used
for meaningful computations. This approach immediately leads to search-type
applications, where it improves the number of steps by a square-root - a simple
minded search that takes N steps, can be improved to O(sqrt(N)) steps. The
quantum search algorithm is one of several immediate consequences of this
framework. Several novel search-related applications are presented.Comment: To be presented at the 1st NASA QCQC conference in Palm Springs,
California, Feb. 17-20, '98. 12 pages, postscrip
Exploration of Reaction Pathways and Chemical Transformation Networks
For the investigation of chemical reaction networks, the identification of
all relevant intermediates and elementary reactions is mandatory. Many
algorithmic approaches exist that perform explorations efficiently and
automatedly. These approaches differ in their application range, the level of
completeness of the exploration, as well as the amount of heuristics and human
intervention required. Here, we describe and compare the different approaches
based on these criteria. Future directions leveraging the strengths of chemical
heuristics, human interaction, and physical rigor are discussed.Comment: 48 pages, 4 figure
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde
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