Comparing quantum annealing and spiking neuromorphic computing for sampling binary sparse coding QUBO problems
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Harvard University, Boston, MA (United States)
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 L2 loss on the reconstruction error, and an L0 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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- 89233218CNA000001
- OSTI ID:
- 2574168
- Report Number(s):
- LA-UR--23-26361; 10.1038/s44335-025-00028-2; 3004-8672
- Journal Information:
- npj Unconventional Computing, Journal Name: npj Unconventional Computing Journal Issue: 1 Vol. 2; ISSN 3004-8672
- Publisher:
- Springer NatureCopyright Statement
- Country of Publication:
- United States
- Language:
- English