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.
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.
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 provableoptimal 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 117 x. Similarly, QMAP and t∣ ket ⟩ exhibit significantly larger gaps of 250 x and 330 x, 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.
Quantum Layout Synthesis (QLS) plays a crucial role in optimizing quantum circuit execution on physical quantum devices. As we enter the era where quantum computers have hundreds of qubits, optimal OLS tools face scalability issues, while heuristic methods suffer significant optimality gap due to the lack of global optimization. To address these challenges, we introduce a multilevel framework, which is an effective methodology for solving large-scale problems in VLSI design. In this paper, we present ML-QLS, the first multilevel quantum layout tool with a scalable refinement operation integrated with novel cost functions and clustering strategies. Our clustering provides valuable insights into generating a proper problem approximation for quantum circuits and devices. The experimental results demonstrate that ML-QLS can scale up to problems involving hundreds of qubits and achieve a remarkable 69% performance improvement over leading heuristic QLS tools for large circuits, which underscores the effectiveness of multilevel frameworks in quantum applications.
Due to its effect on the success rate of a quantum circuit, quantum layout synthesis is a crucial step for circuit compilation. As such, having a layout synthesis tool that provides high solution quality is important to maximize circuit performance and fidelity for NISQ application. Previous heuristic approaches have been shown to be far from optimal when evaluated on known-optimal benchmarks. Alternatively, exact layout synthesis tools can generate optimal results with the aid of constraint solvers but generally suffer from scalability issues because of inefficient encodings and slow optimization methods. In this paper, we propose a scalable optimal layout synthesis tool that improves upon previous works, through a more succinct problem formulation as well as better encoding techniques. Additionally, we implement a depth and SWAP count optimization feature that performs iterative refinement under a fixed time budget. Experimental results show that for depth optimization, our tool can achieve a 692× speedup over the state-of-the-art optimal layout synthesis, and for SWAP optimization, we can obtain a 6,957× speedup on average. Compared to a leading heuristic-based synthesizer, for depth optimization, we can solve circuits consisting of 54 program qubits and 1726 gates within 11 hours with an 18× depth reduction and by 12× SWAP count reduction on average.
Quantum circuit compilation and, in particular, efficient qubit layout synthesis is a critical challenge in modular, multi-core quantum architectures with constrained interconnects. In this work, we extend the SABRE heuristic algorithm to develop TeleSABRE, a layout synthesis approach tailored for architectures featuring teleportation-based interconnects. Unlike standard SABRE, which only introduces SWAP operations for qubit movement, TeleSABRE integrates both intra-core SWAPs and teleportation-based techniques leveraging qubit teleportation and gate teleportation across cores. This enables more efficient circuit execution by reducing both inter-core communication overhead and the number of intra-core SWAPs required to allow teleportation protocols and local gate executions. Experimental results demonstrate that TeleSABRE achieves 28% reduction across various benchmarks in terms of inter-core operations while also taking into account the logistics of the teleport protocols.
Current superconducting quantum devices impose strict connectivity constraints on quantum circuit execution, necessitating circuit transformation before executing quantum circuits on physical hardware. Numerous quantum circuit transformation (QCT) algorithms have been proposed. To enable faithful evaluation of state-of-the-art QCT algorithms, this paper introduces (Qubit mapping Benchmark with Known Near-Optimality), a novel benchmark construction method for QCT. QKNOB circuits have built-in transformations with near-optimal (close to the theoretical optimum) SWAP count and depth overhead. QKNOB provides general and unbiased evaluation of QCT algorithms. Using QKNOB, we demonstrate that SABRE, the default Qiskit compiler, consistently achieves the best performance on the 53-qubit IBM Q Rochester and Google Sycamore devices for both SWAP count and depth objectives. Our results also reveal significant performance gaps relative to the near-optimal transformation costs of QKNOB. Our construction algorithm and benchmarks are open-source.
To address the urgent need in the NISQ era for high-performance, scalable quantum compilers and to advance the integration of classical and quantum computing, we present QLLVM, an advanced Quantum-Classical co-compilation framework built on LLVM. To our knowledge, QLLVM delivers an end-to-end, LLVM-based compilation workflow that unifies the build of classical high-performance programs, including CUDA, MPI, and C++, together with quantum programs into a single executable. For quantum program compilation, QLLVM adopts a three-stage design: high-level optimizations are implemented in the MLIR Quantum dialect and then lowered to QIR, an LLVM IR-based representation, for low-level optimization and hardware mapping. Its extensible architecture and seamless interoperability with classical high-performance computing provide an efficient, flexible, industrial-grade compilation infrastructure for future quantum software development. Experimental results show that, on the MQTBench benchmark suite, QLLVM reduces circuit depth and gate counts compared with state-of-the-art compilers and demonstrates clear advantages in compiling hybrid classical-quantum programs.