A coherent Ising machine for 2000-node optimization problems

Area of Science:

• Quantum computing
• Complex systems analysis
• Combinatorial optimization

Background:

• Combinatorial optimization problems are crucial for analyzing complex systems.
• Mapping these problems to Ising models is a common approach.
• Existing physical Ising machines face scalability limitations due to restricted spin couplings.

Purpose of the Study:

• To address the scalability issues in physical Ising machines.
• To develop a novel approach for solving complex optimization problems.
• To implement a large-scale Ising machine with all-to-all couplings.

Main Methods:

• Constructed a 2000-spin network with all-to-all spin-spin couplings.
• Utilized time-multiplexed degenerate optical parametric oscillators.
• Employed a measurement and feedback scheme for problem implementation.
• Applied the system to maximum cut problems on arbitrary graph topologies.

Main Results:

• Achieved a 2000-spin network, overcoming previous coupling limitations.
• Successfully implemented maximum cut problems on graphs up to 2000 nodes.
• Demonstrated superior accuracy and computation time compared to simulated annealing for a 2000-node complete graph.

Conclusions:

• The developed coherent Ising machine offers a scalable solution for combinatorial optimization.
• This approach significantly advances the potential of physical Ising machines for complex problem-solving.
• The system shows promise for tackling large-scale optimization challenges in various scientific domains.