Scalable Quantum Computing to Solve Optimization Problems

A team of Stanford researchers has developed an efficient, scalable quantum computing system designed to quickly solve combinatorial optimization problems using off-the-shelf components operating at room temperature. This technology employs optical parametric oscillators (OPOs) and an electronic circuit to simulate an Ising machine that computes nondeterministic polynomial (NP)-complete problems - a class of problems that cannot be solved with standard computers because the possible solutions increases exponentially with the number of variables. This network of quantum systems can be adapted and scaled up by expanding the number of OPOs or scaled down and miniaturized. The machine can be implemented with simple programming to solve a wide range of NP optimization problems in fields such as logistics, drug discovery, finance or circuit design.

Stage of Research
The inventors have built a prototype machine and used it to find solutions for thousands of scenarios with up to 100-variables. They have demonstrated experimental results with more than 99.6% success for one-dimensional problems using a 16-bit coherent Ising machine.