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Lecture notes in computer science

Benchmarking the D-Wave Quantum Annealer as a Sparse Boltzmann Machine: Recognition and Timing Performances

Jessica Park, Nicholas Chancellor, David Griffin, Viv M. Kendon, Susan Stepney

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.1007/978-3-031-63742-1_4

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Abstract We consider the problem of computing a sparse binary representation of an image. Given an image and an overcomplete, non-orthonormal basis, we aim to find a sparse binary vector indicating the minimal set of basis vectors that when added together best reconstruct the given input. We formulate this problem with an L 2 loss on the reconstruction error, and an L 0 loss on the binary vector enforcing sparsity. First, we solve the sparse representation QUBOs by solving them both on a D-Wave quantum annealer with Pegasus chip connectivity, as well as on the Intel Loihi 2 spiking neuromorphic processor using a stochastic Non-equilibrium Boltzmann Machine (NEBM). Second, using Quantum Evolution Monte Carlo with Reverse Annealing and iterated warm starting on Loihi 2 to evolve the solution quality from the respective machines. We demonstrate that both quantum annealing and neuromorphic computing are suitable for solving binary sparse coding QUBOs.