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SUMMARY:Developing Hybrid Quantum Monte Carlo Algorithms for Low Quantum O
 verheads and Improved Noise Resilience - Dr Maria-Andreea Filip\, Universi
 ty of Cambridge
DTSTART:20230524T133000Z
DTEND:20230524T143000Z
UID:TALK194146@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Quantum chemistry problems have recently become particularly i
 nteresting targets for quantum\ncomputing algorithms due to their exponent
 ially scaling Hilbert space which can be efficiently\nmapped onto a linear
  number of qubits\, promising significant memory improvements in quantum\n
 over classical algorithms.\nHowever\, in the era of noisy intermediate-sca
 le quantum (NISQ) devices and hybrid algorithms\,\nthe size of chemical sy
 stems that can be treated is still severely limited\, with hardware noise\
 nand qubit decoherence precluding useful computation even for modest numbe
 rs of qubits.\nAditionally\, most current quantum algorithms aim to use qu
 antum devices to measure physical\nquantities of interest effectively exac
 tly. Lowering noise to acceptable levels therefore requires\nnon-trivial r
 epetitions of the preparation and measurement procedure\, which is both ti
 me- and\nresource-consuming.\nQuantum Monte Carlo (QMC) algorithms[1\,2] h
 ave proven effective at lowering the computa-\ntional overhead of challeng
 ing problems\, both in classical[3] and quantum settings.[4] In this\npres
 entation\, we combine ideas from conventional QMC algorithms with quantum 
 computation\nto devise less cumbersome hybrid quantum algorithms. First\, 
 we show that\, by using a quantum\nprocessor to compute projective Monte C
 arlo (PMC) residuals\, one avoids the issue of having\nto importance sampl
 e the wavefunction contributions to the residuals\, which is one of the\nb
 ottlenecks of many QMC algorithms. Secondly\, the resulting Monte Carlo es
 timate of the\nwavefunction and its properties is resilient to noisy measu
 rement of the residuals so very few\nshots are necessary for the algorithm
  to succeed.\nGoing further\, we use stochastic representations of the wav
 efunction and the Hamiltonian to\nfurther reduce quantum overhead. While t
 runcating the wavefunction parametrisation or the\nHamiltonian would intro
 duce a systematic error in a deterministic approach\, this is not the case
 \nfor QMC\, in which we find that biases average out over the course of th
 e calculation.\nFinally\, we explore the expansion of these methods to exc
 ited electronic states.\n[1] G. H. Booth\, A. J. W. Thom\, A. Alavi\, Jour
 nal of Chemical Physics 131\, 054106 (2009).\n[2] M.-A. Filip\, A. J. W. T
 hom\, Journal of Chemical Physics 153\, 214106 (2020)\n[3] J. E. Deustua\,
  J. Shen\, P. Piecuch\, Journal of Chemical Physics 154\, 124103 (2021)\n[
 4] M.-A. Filip\, N. Fitzpatrick\, D. Muñoz-Ramo\, A. J. W. Thom\, Physica
 l Review Research 4\,\n023243 (2022)
LOCATION:Unilever Lecture Theatre\, Yusuf Hamied Department of Chemistry
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