



@article {2530,
	title = {Quantum Computer Systems for Scientific Discovery},
	year = {2019},
	month = {12/16/2019},
	abstract = {<p>The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by co-designing full stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, and significant challenges for the development of quantum computers for science over the next 2-10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21-22, 2019 in Alexandria, VA.</p>},
	url = {https://arxiv.org/abs/1912.07577},
	author = {Yuri Alexeev and Dave Bacon and Kenneth R. Brown and Robert Calderbank and Lincoln D. Carr and Frederic T. Chong and Brian DeMarco and Dirk Englund and Edward Farhi and Bill Fefferman and Alexey V. Gorshkov and Andrew Houck and Jungsang Kim and Shelby Kimmel and Michael Lange and Seth Lloyd and Mikhail D. Lukin and Dmitri Maslov and Peter Maunz and Christopher Monroe and John Preskill and Martin Roetteler and Martin Savage and Jeff Thompson and Umesh Vazirani}
}
@article {1733,
	title = {A Quantum Version of Sch{\"o}ning{\textquoteright}s Algorithm Applied to Quantum 2-SAT},
	journal = {Quantum Information and Computation},
	volume = {16},
	year = {2016},
	month = {2016/03/22},
	abstract = {<p>We study a quantum algorithm that consists of a simple quantum Markov process, and we analyze its behavior on restricted versions of Quantum 2-SAT. We prove that the algorithm solves this decision problem with high probability for n qubits, L clauses, and promise gap c in time O(n^2 L^2 c^{-2}). If the Hamiltonian is additionally polynomially gapped, our algorithm efficiently produces a state that has high overlap with the satisfying subspace. The Markov process we study is a quantum analogue of Sch\&quot;oning\&$\#$39;s probabilistic algorithm for k-SAT.</p>},
	url = {http://arxiv.org/abs/1603.06985},
	author = {Edward Farhi and Shelby Kimmel and Kristan Temme}
}
@article {1262,
	title = {Quantum search by measurement},
	journal = {Physical Review A},
	volume = {66},
	year = {2002},
	month = {2002/9/23},
	abstract = {  We propose a quantum algorithm for solving combinatorial search problems that
uses only a sequence of measurements. The algorithm is similar in spirit to
quantum computation by adiabatic evolution, in that the goal is to remain in
the ground state of a time-varying Hamiltonian. Indeed, we show that the
running times of the two algorithms are closely related. We also show how to
achieve the quadratic speedup for Grover{\textquoteright}s unstructured search problem with
only two measurements. Finally, we discuss some similarities and differences
between the adiabatic and measurement algorithms.
},
	doi = {10.1103/PhysRevA.66.032314},
	url = {http://arxiv.org/abs/quant-ph/0204013v1},
	author = {Andrew M. Childs and Enrico Deotto and Edward Farhi and Jeffrey Goldstone and Sam Gutmann and Andrew J. Landahl}
}
