Travelling Salesman Problem Quantum Benchmark
Benchmark of quantum approaches to the Travelling Salesman Problem, a canonical NP-hard combinatorial optimization task. Instances are encoded as QUBO/Ising Hamiltonians and solved using QAOA or VQE on gate-based hardware, or via quantum annealing. Current NISQ-era implementations handle instances of up to roughly six cities, making TSP a demanding test of quantum optimizer quality.[1]
- Algorithm: QAOA / Variational Quantum Eigensolver (VQE)
- Category: optimization
- Qubits: 30
- Framework: Qiskit, PennyLane, D-Wave Ocean
- Hardware: Various (IBM Quantum, D-Wave, simulators)
- Reproducible: Yes
- Published:
- TSP
- combinatorial-optimization
- QAOA
- VQE
- QUBO
What algorithm does Travelling Salesman Problem Quantum Benchmark use?
Travelling Salesman Problem Quantum Benchmark uses the QAOA / Variational Quantum Eigensolver (VQE) algorithm, categorized under optimization.
Frequently Asked Questions
What is the Travelling Salesman Problem Quantum Benchmark benchmark?
Benchmark of quantum approaches to the Travelling Salesman Problem, a canonical NP-hard combinatorial optimization task. Instances are encoded as QUBO/Ising Hamiltonians and solved using QAOA or VQE on gate-based hardware, or via quantum annealing. Current NISQ-era implementations handle instances of up to roughly six cities, making TSP a demanding test of quantum optimizer quality.
Is Travelling Salesman Problem Quantum Benchmark reproducible?
Yes, this benchmark is reproducible.