Papers
Published work and preprints. Also on Google Scholar.
2026
- Structure-Aware Quantum Circuit Partitioning via Reinforcement Learning for Efficient Re-Synthesis
Great Lakes Symposium on VLSI
abstract
The advancement of quantum computing into the utility scale requires compilation frameworks that can effectively manage the discrepancy between high-level algorithmic intent and the low-level physical constraints of contemporary hardware. Quantum circuit partitioning is a pivotal stage in this compilation pipeline, particularly when leveraging high-performance synthesis tools that are computationally bounded by the number of qubits. Existing partitioning approaches, such as ScanPartitioner and QuickPartitioner, while effective, do not leverage structural patterns in circuits, limiting their ability to make globally informed local partitioning decisions. To address this gap, we propose a novel structural-aware quantum circuit partitioning method using a reinforcement learning (RL) framework that harnesses global circuit knowledge to guide local partitioning decisions, enabling more optimization opportunities at the sub-circuit level. Experimental results on benchmark circuits transpiled to satisfy IBM quantum hardware constraints show that our approach reduces the native two-qubit gate count compared to existing quantum circuit partitioners (ScanPartitioner and QuickPartitioner) with an average two-qubit gate reduction of 18.55% over baselines. This research establishes a scalable and robust methodology for partitioning quantum circuits, bridging the gap between exact and approximate synthesis in the Noisy Intermediate-Scale Quantum (NISQ) era and beyond.bibtex
@inbook{10.1145/3787109.3815306, author = {Charrwi, Mohammad Walid and Rasmussen, Christian and Younis, Ed and de Jong, Wibe Albert and Saeed, Samah Mohamed}, title = {Structure-Aware Quantum Circuit Partitioning via Reinforcement Learning for Efficient Re-Synthesis}, year = {2026}, isbn = {9798400724312}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3787109.3815306}, abstract = {The advancement of quantum computing into the utility scale requires compilation frameworks that can effectively manage the discrepancy between high-level algorithmic intent and the low-level physical constraints of contemporary hardware. Quantum circuit partitioning is a pivotal stage in this compilation pipeline, particularly when leveraging high-performance synthesis tools that are computationally bounded by the number of qubits. Existing partitioning approaches, such as ScanPartitioner [19] and QuickPartitioner [21], while effective, do not leverage structural patterns in circuits, limiting their ability to make globally informed local partitioning decisions. To address this gap, we propose a novel structural-aware quantum circuit partitioning method using a reinforcement learning (RL) framework that harnesses global circuit knowledge to guide local partitioning decisions, enabling more optimization opportunities at the sub-circuit level. Experimental results on benchmark circuits transpiled to satisfy IBM quantum hardware constraints show that our approach reduces the native two-qubit gate count compared to existing quantum circuit partitioners (ScanPartitioner and QuickPartitioner) with an average two-qubit gate reduction of 18.55\% over baselines. This research establishes a scalable and robust methodology for partitioning quantum circuits, bridging the gap between exact and approximate synthesis in the Noisy Intermediate-Scale Quantum (NISQ) era and beyond.}, booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2026}, pages = {570–575}, numpages = {6} } - Stability and Reproducibility in Heuristic Unitary Synthesis for Quantum Circuits
Great Lakes Symposium on VLSI
abstract
Quantum circuit optimization can unlock the full potential of quantum computers for scalable and practical applications. In particular, quantum circuit re-synthesis methods enable significant reductions in gate count by applying approximate unitary synthesis locally at the subcircuit level. Despite these improvements, such optimization techniques rely on random seeds, which can lead to variability in performance across different runs. This inherent randomness raises important questions about the stability and reproducibility of approximate re-synthesis methods. In this paper, we study the stability and reproducibility of approximate unitary synthesis across a broad class of quantum algorithms. Based on our findings, we propose ML-based synthesis workflows that leverage multiple runs or multiple candidates within the same run on strategically selected sub-circuits to improve re-synthesis performance.bibtex
@inbook{10.1145/3787109.3816401, author = {Rasmussen, Christian and Perez, Jason and Younis, Ed and de Jong, Wibe Albert and Saeed, Samah}, title = {Stability and Reproducibility in Heuristic Unitary Synthesis for Quantum Circuits}, year = {2026}, isbn = {9798400724312}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3787109.3816401}, abstract = {Quantum circuit optimization can unlock the full potential of quantum computers for scalable and practical applications. In particular, quantum circuit re-synthesis methods enable significant reductions in gate count by applying approximate unitary synthesis locally at the subcircuit level. Despite these improvements, such optimization techniques rely on random seeds, which can lead to variability in performance across different runs. This inherent randomness raises important questions about the stability and reproducibility of approximate re-synthesis methods. In this paper, we study the stability and reproducibility of approximate unitary synthesis across a broad class of quantum algorithms. Based on our findings, we propose ML-based synthesis workflows that leverage multiple runs or multiple candidates within the same run on strategically selected sub-circuits to improve re-synthesis performance.}, booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2026}, pages = {856–861}, numpages = {6} }
2025
- Don’t cares in quantum circuits: A security perspective
Great Lakes Symposium on VLSI
abstract
As quantum technologies continue to advance, the security of quantum circuits and systems has become an increasingly critical concern, requiring further exploration. In this paper, we investigate information hiding in the form of don’t cares in quantum circuits. We identify and classify different classes of don’t cares in quantum circuits based on the flexibility of realizing quantum unitary/operations as gates, knowledge of the intermediate states of the quantum system, and unused qubits at different stages in the quantum circuit. We provide case studies from different quantum algorithms and building blocks. We discuss how information hiding in the context of quantum circuits can serve as a double-edged sword, presenting both opportunities and risks for quantum circuit security. Our paper aims to identify and systematize potential sources of don’t cares in quantum circuits and shed light on their security implications.bibtex
@inproceedings{10.1145/3716368.3735283, author = {Lushi, Donald and Rasmussen, Christian and Saeed, Samah Mohamed Ahmed}, title = {Don’t Cares in Quantum Circuits: A Security Perspective}, year = {2025}, isbn = {9798400714962}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3716368.3735283}, doi = {10.1145/3716368.3735283}, abstract = {As quantum technologies continue to advance, the security of quantum circuits and systems has become an increasingly critical concern, requiring further exploration. In this paper, we investigate information hiding in the form of don’t cares in quantum circuits. We identify and classify different classes of don’t cares in quantum circuits based on the flexibility of realizing quantum unitary/operations as gates, knowledge of the intermediate states of the quantum system, and unused qubits at different stages in the quantum circuit. We provide case studies from different quantum algorithms and building blocks. We discuss how information hiding in the context of quantum circuits can serve as a double-edged sword, presenting both opportunities and risks for quantum circuit security. Our paper aims to identify and systematize potential sources of don’t cares in quantum circuits and shed light on their security implications.}, booktitle = {Proceedings of the Great Lakes Symposium on VLSI 2025}, pages = {258–264}, numpages = {7}, keywords = {Don’t cares, Quantum circuits, Approximate synthesis, Ancillary qubits, Mid-circuit measurement, Security, Attacks, Defenses.}, location = { }, series = {GLSVLSI '25} }
2024
- Time-aware re-synthesis for secure quantum systems
Hardware Oriented Security and Trust
abstract
The demand for reliable quantum computing platforms has led to significant progress in the development of quantum circuit synthesis and compilation methods that map quantum algorithms to the target quantum architecture. In this paper, we show that the synthesis tools can offer opportunities to enhance the security of quantum systems. We propose a time-aware framework for injecting signatures into quantum circuits. We explore different threat models including quantum circuit IP piracy and the unauthorized circuit execution on the quantum hardware. Our proposed framework supports time-aware block/sub-circuit construction and key-based re-synthesis. We validate the effectiveness of our proposed methods using different quantum benchmarks executed on real-world quantum computers. Our results show that our proposed methods are very stealthy for heavily optimized quantum circuits.bibtex
@INPROCEEDINGS{10545389, author={Rasmussen, Clancy and Saeed, Samah Mohamed}, booktitle={2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)}, title={Time-Aware Re-Synthesis for Secure Quantum Systems}, year={2024}, volume={}, number={}, pages={01-06}, keywords={Threat modeling;Computers;Quantum system;Quantum algorithm;Logic gates;Hardware;Security;NISQ;Quantum circuit;Quantum circuit Synthesis;Reliability;Security;Slack}, doi={10.1109/HOST55342.2024.10545389} }