Research on Quantum Approximate Optimization Algorithm versus Quantum Annealing

Collaborator: Yongcheng Ding, Yuning Zhang, Bin Cheng

TSP Problems

• Quantum Annealing (D-Wave)
• Quantum Approximate Optimization Algorithm(Huawei HiQ Simulator)
• Classical Algorithm (Dynamic Programming, Ant Colony Algorithm)

Contents

• ./analysis/: jupyter notebooks or scripts to process benchmarking data
• ./data/: data file generated by the execution script
• ./quantum_annealing/ : quantum annealing algorithm for TSP run on DWave quantum annealer
• ./docs/ : shared ducuments, including research papers on recent progress of QAOA, technical documents of DWave annealer and our research notes

Library

the algorithms used in this project is capsulated into a indepedent package https://github.com/Neuromancer43/QAOA_LIB

Addressd Targets

• approximated QAOA time complexity with increased problem scaling (iterations x time per iter, evaluated by experimental data of superconducting circuit)
• QAOA space complexity with increased problem scaling (scale of circuit, depth x qubits)
• optimizer performance (iteration, loacl minimum)
• approximate ratio (optimized objective function/global optimum)

Review of previously numerical work