Achieving Pareto-Optimality in Quantum Circuit Compilation via a Multi-Objective Heuristic Optimization Approach

High Performance Computing-Quantum Computing (HPCQC) integration presents a promising yet challenging opportunity, particularly in the area of quantum circuit compilation and optimization, requiring further advancements in the field of Quantum Computing (QC). To address this, we introduce the Munich Quantum Compiler, a key component of the Munich Quantum Software Stack (MQSS). This compiler employs a heuristic-based approach to select a Pareto-optimal subset of optimizations in the form of LLVM passes for quantum circuits described in an LLVM-compliant Intermediate Representation (IR).

Authors

Institutions

Publication Details

Published
2024-09-15
DOI
https://doi.org/10.1109/qce60285.2024.10297
Citations
4
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
1.45
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Achieving Pareto-Optimality in Quantum Circuit Compilation via a Multi-Objective Heuristic Optimization Approach

Laura Schulz, Martin Schulz, Jorge Echavarria, Aleksandra Świerkowska
4 citations
Advanced Multi-Objective Optimization Algorithms
1.45
article

Achieving Pareto-Optimality in Quantum Circuit Compilation via a Multi-Objective Heuristic Optimization Approach

Laura Schulz, Martin Schulz, Jorge Echavarria, Aleksandra Świerkowska
article en
4 citations

Abstract

High Performance Computing-Quantum Computing (HPCQC) integration presents a promising yet challenging opportunity, particularly in the area of quantum circuit compilation and optimization, requiring further advancements in the field of Quantum Computing (QC). To address this, we introduce the Munich Quantum Compiler, a key component of the Munich Quantum Software Stack (MQSS). This compiler employs a heuristic-based approach to select a Pareto-optimal subset of optimizations in the form of LLVM passes for quantum circuits described in an LLVM-compliant Intermediate Representation (IR).

Bavarian Academy of Sciences and Humanities (DE), Leibniz Supercomputing Centre (DE), Technical University of Munich (DE)
Openalex Percentile: Top 15%
Advanced Multi-Objective Optimization Algorithms
1.45
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Achieving Pareto-Optimality in Quantum Circuit Compilation via a Multi-Objective Heuristic Optimization Approach — Laura Schulz, Martin Schulz, et al. · (2024) | TGRS Research Map | TGRS