TetrisLock: Quantum Circuit Split Compilation with Interlocking Patterns
- URL: http://arxiv.org/abs/2503.11982v1
- Date: Sat, 15 Mar 2025 03:41:24 GMT
- Title: TetrisLock: Quantum Circuit Split Compilation with Interlocking Patterns
- Authors: Qian Wang, Jayden John, Ben Dong, Yuntao Liu,
- Abstract summary: In quantum computing, quantum circuits are fundamental representations of quantum algorithms.<n>In this paper, we propose TetrisLock, a split compilation method for quantum circuit obfuscation.
- Score: 7.041881854531399
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In quantum computing, quantum circuits are fundamental representations of quantum algorithms, which are compiled into executable functions for quantum solutions. Quantum compilers transform algorithmic quantum circuits into one compatible with target quantum computers, bridging quantum software and hardware. However, untrusted quantum compilers pose significant risks. They can lead to the theft of quantum circuit designs and compromise sensitive intellectual property (IP). In this paper, we propose TetrisLock, a split compilation method for quantum circuit obfuscation that uses an interlocking splitting pattern to effectively protect IP with minimal resource overhead. Our approach divides the quantum circuit into two interdependent segments, ensuring that reconstructing the original circuit functionality is possible only by combining both segments and eliminating redundancies. This method makes reverse engineering by an untrusted compiler unrealizable, as the original circuit is never fully shared with any single entity. Also, our approach eliminates the need for a trusted compiler to process the inserted random circuit, thereby relaxing the security requirements. Additionally, it defends against colluding attackers with mismatched numbers of qubits, while maintaining low overhead by preserving the original depth of the quantum circuit. We demonstrate our method by using established RevLib benchmarks, showing that it achieves a minimal impact on functional accuracy (less than 1%) while significantly reducing the likelihood of IP inference.
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