Alibaba Cloud Quantum Development Platform: Applications to Quantum Algorithm Design

Abstract

We report our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). The capability of AC-QDP's computational engine was already reported in \cite{CZH+18, ZHN+19}.In this follow-up article, we demonstrate with figures how AC-QDP helps in testing large-scale quantum algorithms (currently within the QAOA framework). We give new benchmark results on regular graphs. AC-QDP's QAOA framework can simulate thousands of qubits for up to $4$ layers. Then we discuss two interesting use cases we have implemented on the platform: 1. Optimal QAOA sequences for small-cycle free graphs; 2. Graph structure discovery.

References in corpus (8)

• A Quantum Approximate Optimization Algorithm
• Simulating quantum computation by contracting tensor networks
• Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
• Simulation of low-depth quantum circuits as complex undirected graphical models
• Classical Simulation of Intermediate-Size Quantum Circuits
• Graph isomorphism and Gaussian boson sampling
• qTorch: The Quantum Tensor Contraction Handler
• Classical and Quantum Bounded Depth Approximation Algorithms

Cited by in corpus (8)

• Recent advances for quantum classifiers
• Classical Simulation of Quantum Supremacy Circuits
• Quantum-inspired algorithms for multivariate analysis: from interpolation to partial differential equations
• Classical symmetries and the Quantum Approximate Optimization Algorithm
• Exploiting Symmetry Reduces the Cost of Training QAOA
• Alibaba Cloud Quantum Development Platform: Surface Code Simulations with Crosstalk
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Improving the Quantum Approximate Optimization Algorithm with postselection