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Reducing the Number of Qubits from $n^2$ to $n\log_{2} (n)$ to Solve the Traveling Salesman Problem with Quantum Computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era
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abstract
In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from $n^2$ to $n\log_{2} (n)$, our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges.
Forward citations
Cited by 3 Pith papers
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Resource-efficient variational quantum solver for the travelling salesman problem and its silicon photonics implementation
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A quantum dynamic programming circuit prepares the uniform superposition of all Hamiltonian cycles in polynomial gates, reducing Grover-based TSP search complexity to O(sqrt((N-1)!).
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Scalable Quantum Walk-Based Heuristics for the Minimum Vertex Cover Problem
A continuous-time quantum walk transition-probability heuristic is proposed for Minimum Vertex Cover, with iterative vertex freezing.
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