Mapping quantum circuits to modular architectures with QUBO

Abstract

Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of interconnected QPUs or cores consisting of tightly coupled quantum bits that can communicate via quantum-coherent and classical links. In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. Therefore, mapping a quantum circuit onto a modular architecture involves finding an optimal assignment of logical qubits (qubits in the quantum circuit) to different cores with the aim to minimize the number of expensive inter-core operations while adhering to given hardware constraints. In this paper, we propose for the first time a Quadratic Unconstrained Binary Optimization (QUBO) technique to encode the problem and the solution for both qubit allocation and inter-core communication costs in binary decision variables. To this end, the quantum circuit is split into slices, and qubit assignment is formulated as a graph partitioning problem for each circuit slice. The costly inter-core communication is reduced by penalizing inter-core qubit communications. The final solution is obtained by minimizing the overall cost across all circuit slices. To evaluate the effectiveness of our approach, we conduct a detailed analysis using a representative set of benchmarks having a high number of qubits on two different multi-core architectures. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits that require on average 0.78 inter-core communications per two-qubit gate.

Submitted to IEEE QCE 2023

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Cited by in corpus (12)

• Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era
• Networked Quantum Services
• Route-Forcing: Scalable Quantum Circuit Mapping for Scalable Quantum Computing Architectures
• Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
• TeleSABRE: Layout Synthesis in Multi-Core Quantum Systems with Teleport Interconnect
• Lightcone Bounds for Quantum Circuit Mapping via Uncomplexity
• On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
• Time-Aware Qubit Assignment and Circuit Optimization for Distributed Quantum Computing
• Multithreaded parallelism for heterogeneous clusters of QPUs
• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
• Evolutionary-Based Circuit Optimization for Distributed Quantum Computing
• Dependency-Aware Circuit Scheduling for Multi-Core Quantum Systems to Minimize Makespan