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Paper 1
Chipmunq: A Fault-Tolerant Compiler for Chiplet Quantum Architectures
Peter Wegmann, Aleksandra Świerkowska, Emmanouil Giortamis, Pramod Bhatotia
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.16389
- arXiv
- 2603.16389
As quantum computing advances toward fault-tolerance through quantum error correction, modular chiplet architectures have emerged to provide the massive qubit counts required while overcoming fabrication limits of monolithic chips. However, this transition introduces a critical compilation gap: existing frameworks cannot handle the scale of fault-tolerant quantum circuits while managing the noisy, sparse interconnects of chiplet backends. We present Chipmunq, the first hardware-aware compiler for mapping and routing fault-tolerant circuits onto modular architectures. Chipmunq employs a quantum-error-correction-aware partitioning strategy that preserves the integrity of logical qubit patches, preventing prohibitive gate overheads common in general-purpose compilers. Our evaluation demonstrates that Chipmunq achieves a 13.5x speedup in compilation time compared to state-of-the-art tools. By incorporating chiplet constraints and defective qubits, it reduces circuit depth by 86.4% and SWAP gate counts by 91.4% across varying code distances. Crucially, Chipmunq overcomes heterogeneous inter-chiplet links, improving logical error rates by up to two orders of magnitude.
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ADaPT: Adaptive-window Decoding for Practical fault-Tolerance
Tina Oberoi, Joshua Viszlai, Frederic T. Chong
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2605.01149
- arXiv
- 2605.01149
Window decoding, first proposed to reduce decoding complexity for real-time decoding, is an essential component to realize scalable, universal-fault tolerant computation. Prior work has focused on improving throughput through parallelization and reducing reaction time via speculation on window boundaries. However, these methods use a fixed window size d, paying a fixed decoding time overhead for each window. In practice, we find this overhead of a fixed window size unnecessary in many cases due to the sparsity of average-case errors in QEC. Leveraging this insight, in this paper we propose an adaptive window decoding technique based on decoder confidence. This technique reduces the overhead in decoding time thus reducing reaction time without compromising on logical error rates. We benchmark adaptive window decoding across different codes and hardware inspired noise models. Our results show that this adaptive technique reaches the target error rate while maintaining a low decoding time overhead across different codes, and under different noise models.
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