Survival of the Optimized: An Evolutionary Approach to T-depth Reduction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911052214566912 |
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| author | Ghosh, Archisman Chatterjee, Avimita Ghosh, Swaroop |
| author_facet | Ghosh, Archisman Chatterjee, Avimita Ghosh, Swaroop |
| contents | Quantum Error Correction (QEC) is the cornerstone of practical Fault-Tolerant Quantum Computing (FTQC), but incurs enormous resource overheads. Circuits must decompose into Clifford+T gates, and the non-transversal T gates demand costly magic-state distillation. As circuit complexity grows, sequential T-gate layers ("T-depth") increase, amplifying the spatiotemporal overhead of QEC. Optimizing T-depth is NP-hard, and existing greedy or brute-force strategies are either inefficient or computationally prohibitive. We frame T-depth reduction as a search optimization problem and present a Genetic Algorithm (GA) framework that approximates optimal layer-merge patterns across the non-convex search space. We introduce a mathematical formulation of the circuit expansion for systematic layer reordering and a greedy initial merge-pair selection, accelerating the convergence and enhancing the solution quality. In our benchmark with ~90-100 qubits, our method reduces T-depth by 79.23% and overall T-count by 41.86%. Compared to the reversible circuit benchmarks, we achieve a 2.58x improvement in T-depth over the state-of-the-art methods, demonstrating its viability for near-term FTQC. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_09391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survival of the Optimized: An Evolutionary Approach to T-depth Reduction Ghosh, Archisman Chatterjee, Avimita Ghosh, Swaroop Quantum Physics Hardware Architecture Emerging Technologies Quantum Error Correction (QEC) is the cornerstone of practical Fault-Tolerant Quantum Computing (FTQC), but incurs enormous resource overheads. Circuits must decompose into Clifford+T gates, and the non-transversal T gates demand costly magic-state distillation. As circuit complexity grows, sequential T-gate layers ("T-depth") increase, amplifying the spatiotemporal overhead of QEC. Optimizing T-depth is NP-hard, and existing greedy or brute-force strategies are either inefficient or computationally prohibitive. We frame T-depth reduction as a search optimization problem and present a Genetic Algorithm (GA) framework that approximates optimal layer-merge patterns across the non-convex search space. We introduce a mathematical formulation of the circuit expansion for systematic layer reordering and a greedy initial merge-pair selection, accelerating the convergence and enhancing the solution quality. In our benchmark with ~90-100 qubits, our method reduces T-depth by 79.23% and overall T-count by 41.86%. Compared to the reversible circuit benchmarks, we achieve a 2.58x improvement in T-depth over the state-of-the-art methods, demonstrating its viability for near-term FTQC. |
| title | Survival of the Optimized: An Evolutionary Approach to T-depth Reduction |
| topic | Quantum Physics Hardware Architecture Emerging Technologies |
| url | https://arxiv.org/abs/2504.09391 |