OPAQUE: Obfuscating Phase in Quantum Circuit Compilation for Efficient IP Protection
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Abstract (arXiv)
Quantum compilers play a crucial role in quantum computing by converting these algorithmic quantum circuits into forms compatible with specific quantum computer hardware. However, untrusted quantum compilers present considerable risks, including the potential theft of quantum circuit intellectual property (IP) and compromise of the functionality (e.g. Trojan insertion). Quantum circuit obfuscation techniques protect quantum IP by transforming a quantum circuit into a key-dependent version before compilation and restoring the compiled circuit's functionality with the correct key. This prevents the untrusted compiler from knowing the circuit's original functionality. Existing quantum circuit obfuscation techniques focus on inserting key qubits to control key gates. One added key gate can represent at most one Boolean key bit. In this paper, we propose OPAQUE, a phase-based quantum circuit obfuscation approach where we use the angle of rotation gates as the secret keys. The rotation angle is a continuous value, which makes it possible to represent multiple key bits. Moreover, phase gates are usually implemented as virtual gates in quantum hardware, diminishing their cost and impact on accuracy.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2025.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustnesscited by 3
- 2025Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defensescited by 1
- 2026Q-Tag: Watermarking Quantum Circuit Generative Modelscited by 0
- 2026Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithmscited by 0
- 2026Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms5 May 2026
- 2026Q-Tag: Watermarking Quantum Circuit Generative Models26 Feb 2026
- 2025SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness19 Nov 2025
- 2025Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses27 Jun 2025