Reproduce “HyCiM: A Hybrid Computing-in-Memory QUBO Solver for General Combinatorial Optimization Problems with Inequality Constraints” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yu Qian, Zeyu Yang, Kai Ni, Alptekin Vardar, Thomas Kämpfe, Xunzhao Yin
HyCiM solves quadratic knapsack problems by checking item-weight constraints in a FeFET-based filter before sending feasible choices to a compute-in-memory annealer. This avoids auxiliary-variable expansion in the QUBO representation. In simulations on 40 instances with 100 items, it achieved 98.54% average success versus 10.75% for D-QUBO, using a target of 95% of the optimum; these results are not chip benchmarks.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.