MemComputing mainly present themselves as optimizers: Their machine can solve real optimization problems, in practice, today. This is very good because, through that lens, we can skip the BS and go straight to the facts. The evaluation criteria for any optimization approach/algorithm are:
- the quality of the solutions found (in the best case: optimal solutions)
- the presence or absence of optimality guarantees (for example, some algorithms provide optimal solutions very often, but cannot guarantee it 100%)
- the time (or computational cost) needed to find such solutions
Furthermore, the state-of-the-art (SOTA) is well known for most types of optimization problems. In this article, they present a list of real and practical applications, so let us have a look at them one by one:
1.1 Traffic flow optimization
Simple flow problems can be solved in polynomial time (and quickly in practice), so there is no need for anything fancy. Once you introduce additional constraints or discrete variable, the SOTA is mixed-integer programming for offline problems. For online problems it's more complicated. In both cases, I am not aware of any application in which MemComputing can reach SOTA.
1.2 Vehicle routing & scheduling
Here, depending on your computing time constraints, needs for solution quality and/or optimality guarantees, the SOTA can be constraint programming (gecode, OptaPlanner), local search heuristics (LocalSolver), or mixed-integer programming (CPLEX/XPress/Gurobi) with column generation. MemComputing is nowhere to be seen again.
1.3 Supply chain optimization
This is a very broad field, but in general mixed-integer programming is king here.
2.1 Protein folding
Here there is quite an objective measure: the biennal CASP competition. This is where AlphaFold made a splash in 2022. MemComputing has never participated.
2.2 Genome sequencing
I am not knowledgeable enough to comment here.
2.3 Radiotherapy treatment
I am not very knowledgeable here either, but last I looked mixed-integer programming approaches were favored.
3.1 Portfolio risk optimization
Various types of branch-and-bound solvers. Mixed-integer linear/quadratic/convex programming. No MemComputing.
3.2 Detecting market instabilities
No idea.
3.3 Optimizing trading trajectories
No idea.
4.1 Training neural networks
Many people here know how this is done. Stochastic gradient descent on GPUs or TPUs. No MemComputing involved. How can they even claim to be active in this field?
4.2 Detecting statistical anomalies
Vague.
4.3 Classifying unstructured datasets
No idea.
The problem is that if you invent a new optimization algorithm, it is very easy to find one instance of one problem for which your algorithm works well. They did literally that in a paper [1]: They took a library of mixed-integer programming problem instances containing 270 benchmark problems, and published a whitepaper showing that they beat a SOTA solver on one of them. A single instance out of 270!
The really hard part is the opposite: given a class of problems, find an algorithm that beats the SOTA. MemComputing has never done that. Combined with their propensity for grand claims backed by misleading evidence, MemComputing have accumulated a lot of badwill from the academic community over the years. My suspicion is that, while on the surface this post seems to put their approach in contrast to quantum computing, what they really try to do here is ride on the quantum computing hype wave.
[1] https://arxiv.org/abs/2003.10644