Quantum Coin Method for Numerical Integration

dc.contributor.authorShimada, N. H.en_US
dc.contributor.authorHachisuka, T.en_US
dc.contributor.editorBenes, Bedrich and Hauser, Helwigen_US
dc.date.accessioned2020-10-06T16:54:01Z
dc.date.available2020-10-06T16:54:01Z
dc.date.issued2020
dc.description.abstractLight transport simulation in rendering is formulated as a numerical integration problem in each pixel, which is commonly estimated by Monte Carlo integration. Monte Carlo integration approximates an integral of a black‐box function by taking the average of many evaluations (i.e. samples) of the function (integrand). For queries of the integrand, Monte Carlo integration achieves the estimation error of . Recently, Johnston [Joh16] introduced quantum super‐sampling (QSS) into rendering as a numerical integration method that can run on quantum computers. QSS breaks the fundamental limitation of the convergence rate of Monte Carlo integration and achieves the faster convergence rate of approximately which is the best possible bound of any quantum algorithms we know today [NW99]. We introduce yet another quantum numerical integration algorithm, quantum coin (QCoin) [AW99], and provide numerical experiments that are unprecedented in the fields of both quantum computing and rendering. We show that QCoin's convergence rate is equivalent to QSS's. We additionally show that QCoin is fundamentally more robust under the presence of noise in actual quantum computers due to its simpler quantum circuit and the use of fewer qubits. Considering various aspects of quantum computers, we discuss how QCoin can be a more practical alternative to QSS if we were to run light transport simulation in quantum computers in the future.en_US
dc.description.number6
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14015
dc.identifier.issn1467-8659
dc.identifier.pages243-257
dc.identifier.urihttps://doi.org/10.1111/cgf.14015
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14015
dc.publisher© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltden_US
dc.subjectquantum computing
dc.subjectMonte Carlo
dc.titleQuantum Coin Method for Numerical Integrationen_US
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