Broadening the set of algorithms and use-cases for analog combinatorial optimization accelerators

Recent experimental results show how classical accelerators based on analog computing can outperform quantum annealing alternatives in benchmark tasks that require dense connection matrices. In Hewlett Packard Labs, we have been studying two alternatives: integrated coherent Ising machines and mem-HNNs (based on memristive crossbar arrays). An important challenge for commercial viability is that different industrial workloads typically benefit from the availability of a variety of optimization algorithms and require a broad range of template combinatorial optimization problems. In this talk, we will discuss our recent progress in going beyond Max-Cut, and we will propose a broader range of algorithms. This flexibility in algorithm choices and template problems is an important step forward to address the wide variety of enterprise-level use-cases such as airline scheduling, supply chain optimization, real-time bandwidth management, gene sequencing, etc.