  We consider the problem of computing a sparse binary representation of an
image. To be precise, 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
$L_0$ (or, equivalently, an $L_1$) loss on the binary vector enforcing
sparsity. This yields a quadratic binary optimization problem (QUBO), whose
optimal solution(s) in general is NP-hard to find. The method of unsupervised
and unnormalized dictionary feature learning for a desired sparsity level to
best match the data is presented. Next, we solve the sparse representation QUBO
by implementing it both on a D-Wave quantum annealer with Pegasus chip
connectivity via minor embedding, as well as on the Intel Loihi 2 spiking
neuromorphic processor. On the quantum annealer, we sample from the sparse
representation QUBO using parallel quantum annealing combined with quantum
evolution Monte Carlo, also known as iterated reverse annealing. On Loihi 2, we
use a stochastic winner take all network of neurons. The solutions are
benchmarked against simulated annealing, a classical heuristic, and the optimal
solutions are computed using CPLEX. Iterated reverse quantum annealing performs
similarly to simulated annealing, although simulated annealing is always able
to sample the optimal solution whereas quantum annealing was not always able
to. The Loihi 2 solutions that are sampled are on average more sparse than the
solutions from any of the other methods. Loihi 2 outperforms a D-Wave quantum
annealer standard linear-schedule anneal, while iterated reverse quantum
annealing performs much better than both unmodified linear-schedule quantum
annealing and iterated warm starting on Loihi 2.
