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QUANTUM COMPUTING
CARGO LOAD PLANNING
Cargo load planning is computationally intensive and one of those problems that is expected to be solved efficiently by quantum computers. However, quantum computers are still estimated to be 10 years away.
Our latest paper shows how MemComputing can solve these problems today a good decade before quantum computers.
CASE STUDY
TRANSPORTATION LOGISTICS
Read our case study that shows how MemComputing can solves NPHard port logistics problems in seconds, where today’s best in class solution takes over 70 hours.
FUNDING
LATEST INVESTMENT
Japanese investment firm, ITFarm invests in MemComputing.
What it isn’t
It is not a Turing Machine
It does not follow the Von Neumann Architecture
It isn’t Quantum Computing
What it is
An entirely new paradigm
Computation & Memory Combined in the same circuit
A brand new/patented computer architecture
Uses classical low power, low heat transistor technology
Emulated in software, it runs on classical architecture
Optimization Problems Are Everywhere
These fundamental problems pervade essentially every scientific discipline and industry and are usually difficult to solve
Life Sciences
Drug Design
Finance
Portifolio Optimization
Energy
Demand Forecasting
Machine Learning
Deep Learning
Logistic & Distribution
Resource Planning
Optimization problems involve finding the best solution from all possible solutions; often exploring millions, billions or even more possible combinations
One approach is to exhaustively search and check every possible solution but this “brute force” becomes less and less feasible as problems get bigger
For many optimization problems, the time required to solve grows exponentially as more variables are added often rendering them intractable for modern computers
An Exponential Problem
Exponential problems are often called “intractable” since they can take longer than a lifetime to solve
Currently, many known solutions for optimization problems involve exponential growth

Complex optimization problems are often classified as “NPhard“ or “NPComplete”.
“NP” problems are problems which are unsolvable within a reasonable amount of time for large input sizes.

The “Time Complexity” of a problem defines how the time to solve increases with input size

O(n) – Execution time increases linearly with input size

O(n^2 ) – Quadratic Growth

O(2^n ) – Exponential Growth
Exponential problems are often called “intractable” since they can take longer than a lifetime to solve
What to do?
If most optimization problems are intractable, how do we solve them?
For many years, research has been focused on developing computer systems and techniques that could efficiently solve these NP problems. However many challenges remain.
Select Techniques and Systems for Approaching Intractable Optimization Problems
Branch & Bound
Heuristics
A technology that can efficiently provide accurate solutions to these exponential problems stands to unlock tremendous value across a number of industrial & scientific verticals
MemComputing’s New Approach
A proprietary computing architecture that achieves an exponential speedup and finds better solutions

Inspired by the computational efficiency of the brain

Fundamentally different computational architecture

Introduces “Self Organizing Logic Gates” accepting inputs from any terminal which enable collective behavior

Takes advantage of nonlocality of memory and long range correlations between gates

Completely avoids the von Neumann Bottleneck