MLQM: Machine Learning Approach for Accelerating Optimal Qubit Mapping

Abstract

Quantum circuit mapping is a critical process in quantum computing that involves adapting logical quantum circuits to adhere to hardware constraints, thereby generating physically executable quantum circuits. Current quantum circuit mapping techniques, such as solver-based methods, often encounter challenges related to slow solving speeds due to factors like redundant search iterations. Regarding this issue, we propose a machine learning approach for accelerating optimal qubit mapping (MLQM). First, the method proposes a global search space pruning scheme based on prior knowledge and machine learning, which in turn improves the solution efficiency. Second, to address the limited availability of effective samples in the learning task, MLQM introduces a novel data augmentation and refinement scheme, this scheme enhances the size and diversity of the quantum circuit dataset by exploiting gate allocation and qubit rearrangement. Finally, MLQM also further improves the solution efficiency by pruning the local search space, which is achieved through an adaptive dynamic adjustment mechanism of the solver variables. Compared to state-of-the-art qubit mapping approaches, MLQM achieves optimal qubit mapping with an average solving speed-up ratio of 1.79 and demonstrates an average advantage of 22% in terms of space complexity.

References in corpus (13)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Hartree-Fock on a superconducting qubit quantum computer
• Building logical qubits in a superconducting quantum computing system
• Variational ansatz-based quantum simulation of imaginary time evolution
• Quantum computing with neutral atoms
• Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
• Superconducting Quantum Computing: A Review
• Demonstration of Universal Parametric Entangling Gates on a Multi-Qubit Lattice
• Optimality Study of Existing Quantum Computing Layout Synthesis Tools
• Timing and resource-aware mapping of quantum circuits to superconducting processors
• Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search
• Supervised Learning Enhanced Quantum Circuit Transformation
• qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation

Cited by in corpus (2)

• Implementation of Quantum Fourier Transform and Quantum Hashing for a Quantum Device with Arbitrary Qubits Connection Graphs
• Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis