# Comparing and Integrating Constraint Programming and Temporal Planning for Quantum Circuit Compilation

arXiv: https://arxiv.org/abs/1803.06775 · HTML: https://mlsanity.com/paper/1803.06775/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Generalization in quantum machine learning from few training data](https://mlsanity.com/paper/2111.05292/) (2021)
- [Optimal Layout Synthesis for Quantum Circuits as Classical Planning (full version)](https://mlsanity.com/paper/2304.12014/) (2023)
- [Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning](https://mlsanity.com/paper/2204.06904/) (2022)
- [Q-fid: Quantum Circuit Fidelity Improvement with LSTM Networks](https://mlsanity.com/paper/2303.17523/) (2023)
- [Compilation of product-formula Hamiltonian simulation via reinforcement learning](https://mlsanity.com/paper/2311.04285/) (2023)

## Abstract (arXiv)

Recently, the makespan-minimization problem of compiling a general class of quantum algorithms into near-term quantum processors has been introduced to the AI community. The research demonstrated that temporal planning is a strong approach for a class of quantum circuit compilation (QCC) problems. In this paper, we explore the use of constraint programming (CP) as an alternative and complementary approach to temporal planning. We extend previous work by introducing two new problem variations that incorporate important characteristics identified by the quantum computing community. We apply temporal planning and CP to the baseline and extended QCC problems as both stand-alone and hybrid approaches. Our hybrid methods use solutions found by temporal planning to warm start CP, leveraging the ability of the former to find satisficing solutions to problems with a high degree of task optionality, an area that CP typically struggles with. The CP model, benefiting from inferred bounds on planning horizon length and task counts provided by the warm start, is then used to find higher quality solutions. Our empirical evaluation indicates that while stand-alone CP is only competitive for the smallest problems, CP in our hybridization with temporal planning out-performs stand-alone temporal planning in the majority of problem classes.