



02574nas a2200313   4500008004100000245006200041210006200103260001500165520165200180100002401832700002201856700002001878700002201898700002001920700002701940700002201967700002401989700001802013700002102031700002102052700001702073700003102090700001902121700002002140700001902160700002302179700002102202856003702223       2019                        eng d00aQuantum Computing at the Frontiers of Biological Sciences0 aQuantum Computing at the Frontiers of Biological Sciences  c2019/11/163 a<p><span style="color: rgb(51, 51, 51); font-family: &quot;Open Sans&quot;, &quot;Lucida Grande&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif; font-size: 14px;">The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences.&nbsp;</span></p>1 aEmani, Prashant, S.1 aWarrell, Jonathan1 aAnticevic, Alan1 aBekiranov, Stefan1 aGandal, Michael1 aMcConnell, Michael, J.1 aSapiro, Guillermo1 aAspuru-Guzik, Alán1 aBaker, Justin1 aBastiani, Matteo1 aMcClure, Patrick1 aMurray, John1 aSotiropoulos, Stamatios, N1 aTaylor, J., M.1 aSenthil, Geetha1 aLehner, Thomas1 aGerstein, Mark, B.1 aHarrow, Aram, W.  uhttps://arxiv.org/abs/1911.0712701766nas a2200181   4500008004100000245008100041210006900122260001500191300001800206490000800224520121000232100001701442700002401459700002001483700002001503700002401523856003701547       2008                        eng d00aPolynomial-time quantum algorithm for the simulation of chemical  dynamics
0 aPolynomialtime quantum algorithm for the simulation of chemical   c2008/11/24  a18681 - 186860 v1053 a  The computational cost of exact methods for quantum simulation using
classical computers grows exponentially with system size. As a consequence,
these techniques can only be applied to small systems. By contrast, we
demonstrate that quantum computers could exactly simulate chemical reactions in
polynomial time. Our algorithm uses the split-operator approach and explicitly
simulates all electron-nuclear and inter-electronic interactions in quadratic
time. Surprisingly, this treatment is not only more accurate than the
Born-Oppenheimer approximation, but faster and more efficient as well, for all
reactions with more than about four atoms. This is the case even though the
entire electronic wavefunction is propagated on a grid with appropriately short
timesteps. Although the preparation and measurement of arbitrary states on a
quantum computer is inefficient, here we demonstrate how to prepare states of
chemical interest efficiently. We also show how to efficiently obtain
chemically relevant observables, such as state-to-state transition
probabilities and thermal reaction rates. Quantum computers using these
techniques could outperform current classical computers with one hundred
qubits.
1 aKassal, Ivan1 aJordan, Stephen, P.1 aLove, Peter, J.1 aMohseni, Masoud1 aAspuru-Guzik, Alán  uhttp://arxiv.org/abs/0801.2986v3