Though variational quantum algorithms (VQAs), which rely on a hybrid

quantum--classical architecture, represent a promising avenue for

addressing fundamental linear algebra problems in exponentially large

dimensions, the potential computational advantage they may have over

purely classical algorithms has not yet been determined. Moreover, VQAs

yield insight into a new paradigm for solving high-dimensional sparse

linear algebra problems, in particular for solving large linear systems.

Inspired by the existing quantum--classical Variational Quantum Linear

Solver (VQLS), the Variational Neural Linear Solver constitutes a fully

classical neural network linear system solver utilizing techniques from

variational quantum Monte Carlo. We introduce the VNLS and demonstrate

its potential for addressing high-dimensional, sufficiently sparse

linear systems in comparison with the established VQAs. Speaker(s): Oliver Knitter (University of Michigan)

quantum--classical architecture, represent a promising avenue for

addressing fundamental linear algebra problems in exponentially large

dimensions, the potential computational advantage they may have over

purely classical algorithms has not yet been determined. Moreover, VQAs

yield insight into a new paradigm for solving high-dimensional sparse

linear algebra problems, in particular for solving large linear systems.

Inspired by the existing quantum--classical Variational Quantum Linear

Solver (VQLS), the Variational Neural Linear Solver constitutes a fully

classical neural network linear system solver utilizing techniques from

variational quantum Monte Carlo. We introduce the VNLS and demonstrate

its potential for addressing high-dimensional, sufficiently sparse

linear systems in comparison with the established VQAs. Speaker(s): Oliver Knitter (University of Michigan)

Building: | East Hall |
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Event Type: | Workshop / Seminar |

Tags: | Mathematics |

Source: | Happening @ Michigan from Department of Mathematics |