arXiv subjects

Shuohao Ping

Publications and source records attributed to Shuohao Ping.

4 recordsLinked to original sources

arXiv2026

AlphaSyndrome: Tackling the Syndrome Measurement Circuit Scheduling Problem for QEC Codes

Quantum error correction (QEC) is essential for scalable quantum computing, yet repeated syndrome-measurement cycles dominate its spacetime and hardware cost. Although stabilizers commute and admit many valid execution orders, different schedules induce distinct error-propagation paths under realistic noise, leading to large variations in logical error rate. Outside of surface codes, effective syndrome-measurement scheduling remains largely unexplored. We present AlphaSyndrome, an automated synthesis framework for scheduling syndrome-measurement circuits in general commuting-stabilizer codes under minimal assumptions: mutually commuting stabilizers and a heuristic decoder. AlphaSyndrome formulates scheduling as an optimization problem that shapes error propagation to (i) avoid patterns close to logical operators and (ii) remain within the decoder's correctable region. The framework uses Monte Carlo Tree Search (MCTS) to explore ordering and parallelism, guided by code structure and decoder feedback. Across diverse code families, sizes, and decoders, AlphaSyndrome reduces logical error rates by 80.6% on average (up to 96.2%) relative to depth-optimal baselines, matches Google's hand-crafted surface-code schedules, and outperforms IBM's schedule for the Bivariate Bicycle code.

cs.ET↗

arXiv2025

A High-Performance Multilevel Framework for Quantum Layout Synthesis

Quantum Layout Synthesis (QLS) is a critical compilation stage that adapts quantum circuits to hardware constraints with an objective of minimizing the SWAP overhead. While heuristic tools demonstrate good efficiency, they often produce suboptimal solutions, and exact methods suffer from limited scalability. In this work, we propose ML-SABRE, a high-performance multilevel framework for QLS that improves both solution quality and compilation time through a hierarchical optimization approach. We employ the state-of-the-art heuristic method, LightSABRE, at all levels to ensure both efficiency and performance. Our evaluation on real benchmarks and hardware architectures shows that ML-SABRE decreases SWAP count by over 60%, circuit depth by 17%, and delivers a 60% compilation time reduction compared to state-of-the-art solvers. Further optimality studies reveal that ML-SABRE can significantly reduce the optimality gap by up to 82% for SWAP count and 49% for circuit depth, making it well-suited for emerging quantum devices with increasing size and architectural complexity.

quant-ph↗

arXiv2025

Assessing Quantum Layout Synthesis Tools via Known Optimal-SWAP Cost Benchmarks

Quantum layout synthesis (QLS) is a critical step in quantum program compilation for superconducting quantum computers, involving the insertion of SWAP gates to satisfy hardware connectivity constraints. While previous works have introduced SWAP-free benchmarks with known-optimal depths for evaluating QLS tools, these benchmarks overlook SWAP count - a key performance metric. Real-world applications often require SWAP gates, making SWAP-free benchmarks insufficient for fully assessing QLS tool performance. To address this limitation, we introduce QUBIKOS, a benchmark set with provable-optimal SWAP counts and non-trivial circuit structures. For the first time, we are able to quantify the optimality gaps of SWAP gate usages of the leading QLS algorithms, which are surprisingly large: LightSabre from IBM delivers the best performance with an optimality gap of 63x, followed by ML-QLS with an optimality gap of 117x. Similarly, QMAP and t|ket> exhibit significantly larger gaps of 250x and 330x, respectively. This highlights the need for further advancements in QLS methodologies. Beyond evaluation, QUBIKOS offers valuable insights for guiding the development of future QLS tools, as demonstrated through an analysis of a suboptimal case in LightSABRE. This underscores QUBIKOS's utility as both an evaluation framework and a tool for advancing QLS research.

quant-ph↗

arXiv2024

Depth-Optimal Addressing of 2D Qubit Array with 1D Controls Based on Exact Binary Matrix Factorization

Reducing control complexity is essential for achieving large-scale quantum computing. However, reducing control knobs may compromise the ability to independently address each qubit. Recent progress in neutral atom-based platforms suggests that rectangular (row-column) addressing may strike a balance between control granularity and flexibility for 2D qubit arrays. This scheme allows addressing qubits on the intersections of a set of rows and columns each time. While quadratically reducing controls, it may necessitate more depth. We formulate the depth-optimal rectangular addressing problem as exact binary matrix factorization, an NP-hard problem also appearing in communication complexity and combinatorial optimization. We introduce a satisfiability modulo theories-based solver for this problem, and a heuristic, row packing, performing close to the optimal solver on various benchmarks. Furthermore, we discuss rectangular addressing in the context of fault-tolerant quantum computing, leveraging a natural two-level structure.

cs.ET↗