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Researchers have developed a classical algorithm that mimics quantum mechanics principles to solve MaxCut problems with approximation ratios exceeding 0.95. By utilizing sparse Walsh encoding, the method achieves near-optimal results using less than 0.3% of the parameters required by conventional full-space methods. This advancement demonstrates how quantum-inspired techniques can efficiently tackle complex optimization challenges in network analysis and logistics without requiring dedicated quantum hardware.
Researchers Achieve Near Optimal Solutions for Complex Problems with New Solver
On four complex network problems, G1, G6, G12, and G18, a new algorithm achieved approximation ratios exceeding $0. 95$, consistently surpassing traditional methods like random or tabu search. This advance stems from a technique encoding problem variables as correlations derived from classical calculations inspired by principles of quantum mechanics.
A new quantum-inspired algorithm solves the MaxCut problem by representing variables using expectation values of diagonal Pauli/Walsh observables computed from classical autocorrelations. The approach enables efficient optimisation and offers connections to potential future implementation on dedicated hardware. The method utilises eight hundred and one active parameters, corresponding to less than 0.306 per cent of the full Walsh space over eighteen qubits. A new computational method inspired by quantum mechanics improves solutions for complex…
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