Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that carries out computation through one-way measurements on entangled photon qubits. Practical photonic hardware first generates a 2D mesh of resource states with each being a small number of entangled photon qubits and then exploits fusion operations to connect resource states to scale up the computation. Given that the fusion operation is highly error-prone, it is important to reduce the number of fusions for an MBQC circuit.
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that carries out computation through one-way measurements on entangled photon qubits. Practical photonic hardware first generates a 2D mesh of resource states with each being a small number of entangled photon qubits and then exploits fusion operations to connect resource states to scale up the computation. Given that the fusion operation is highly error-prone, it is important to reduce the number of fusions for an MBQC circuit.
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through "one-way" measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) plays an important role in the overall execution time and circuit error. In this paper, we propose FMCC, a compilation framework that employs dynamic programming with heuristics to efficiently minimize the cluster state depth. Experimental results on six quantum applications show that FMCC achieves 51.7%, 57.4%, and 56.8% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilations.
Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that performs computation through ``one-way'' measurements on entangled quantum qubits. It is widely used in photonic quantum computing (PQC), where the computation is carried out on photonic cluster states (i.e., a 2-D mesh of entangled photons). In MBQC-based PQC, the cluster state depth (i.e., the length of one-way measurements) to execute a quantum circuit plays an important role in the overall execution time and error. Thus, it is important to reduce the cluster state depth. In this paper, we propose FMCC, a compilation framework that employs dynamic programming to efficiently minimize the cluster state depth. Experimental results on five representative quantum algorithms show that FMCC achieves 53.6%, 60.6%, and 60.0% average depth reductions in small, medium, and large qubit counts compared to the state-of-the-art MBQC compilation frameworks.
Photonic quantum computing provides a promising route toward quantum computation by naturally supporting the measurement-based quantum computation (MBQC) model. In MBQC, programs are executed through measurements on a pre-generated graph state, whose construction largely depends on probabilistic fusion operations. However, fusion operations in PQC are vulnerable to two major error sources: fusion failure and fusion erasure. As a result, MBQC compilation must account for both error mechanisms to generate reliable and efficient photonic executions. Prior state-of-the-art MBQC compilation, represented by OneAdapt, is designed for all-photonic architectures and mainly focuses on handling fusion failures. Nevertheless, it does not explicitly model fusion erasures induced by photon loss, which can be substantially more damaging than fusion failures.
To mitigate fusion erasure errors, we introduce a new MBQC compilation scheme built upon the spin qubit quantum memory. We propose tree-encoded fusion, an encoding strategy that suppresses erasure errors during graph-state generation. We further incorporate this scheme into a compiler framework with algorithms that reduce the execution overhead of quantum programs. We evaluate the proposed framework using a realistic PQC simulator on six representative quantum algorithm benchmarks across multiple program scales. The results show that tree-encoded fusion achieves better robustness than alternative fusion-encoding strategies, and that our compiler provides exponential improvement over OneAdapt. In addition, we validate the feasibility of our approach through a proof-of-concept demonstration on real PQC hardware.
Abstract The measurement-based quantum computing paradigm relies on entangling the qubits of a register into a graph state and on measuring subsets of such register in order to condition the unmeasured qubits. The computation therefore, instead of being carried on only by one-qubit and two-qubits logic gates, is largely based on entanglement and measurement processes. Compared to the gate model architecture, in MBQC the overhead in terms of computation resources to synthesize logical qubits is higher. While gate model relies on a register of logical qubits whose number is set at the beginning of the computation and remains constant during the computation, in the MBQC the input qubits outnumber the output ones, due to the destructive nature of measurement processes. Still, we analytically prove and experimentally confirm that MBQC can be efficiently emulated on a classical software, reaching equal loads with respect to the gate-based approach in terms of average runtime and storage of memory. The numerical results confirm that despite the potential computational overhead due to the high entanglement of the initial graph state, the MBQC paradigm carries similar computational complexity, both in terms of time and memory, with respect to the gate-based approach.
Measurement-based Quantum Computing (MBQC) achieves universal quantum computing by applying measurements on the photonic architectures. While it has many advantages, such as long qubit decoherence time and strong scalability, the success rate of MBQC execution is constrained by imperfect photon control, measurement, and fusion operations. Both fusion failure and photon loss necessitate the re-execution of the entire quantum circuit, leading to significant overhead in terms of additional execution time and increased consumption of resource state layers. Recent studies mainly focus on mitigating fusion failures and little attention has been paid to photon loss. In this paper, we propose STMC (Small-Tile Multiple-Copy) compilation framework to reduce the re-execution overhead caused by both the fusion failure and photon loss. Specifically, STMC first transforms a quantum circuit into a fusion graph and partitions the fusion graph into subgraphs. Then, STMC generates compact subgraph mappings that are appropriate for the size of a subportion in the resource state layer, referred to as a tile. Finally, STMC employs multiple copies of each subgraph when mapping to tiles, duplicates the execution of tiles in parallel, and finishes the whole circuit execution in order. The experimental results demonstrate that STMC achieves an average execution time speedup of 65.68× for successfully executing the circuit under a 75% fusion success rate, compared to prior work. Additionally, STMC reduces the number of resource state layers by three orders of magnitude and decreases the number of resource states by an average of 36.40×.
Circuit-based quantum computing (CBQC) has far remained the most intuitive and experimentally mature model of universal quantum computation, serving as the basis for today’s noisy intermediate-scale quantum (NISQ) devices and emerging fault-tolerant quantum computation (FTQC) due to its well-developed control and error-correction techniques. Nevertheless, scalability challenges and suitability to photonic platforms have motivated the exploration of alternative computational models such as measurement-based quantum computing (MBQC), which separates entanglement generation from actual computation and is considered more natural for scalable and distributed architectures. More recently, fusion-based quantum computation (FBQC), and in particular its encoded variant (EFBQC), have been proposed to alleviate many of the practical limitations of traditional MBQC by using constant-size resource states and encoded fusion operations with controllable failure behavior, thus improving practicality, flexibility, and fault-tolerance. Motivated by these potential advantages of measurement-based approaches while recognizing the continued prominence and circuit-level versatility of CBQC, in this work, we propose a hybrid approach that combines CBQC with (E)FBQC to improve long-range connectivity and Clifford-dominated subroutine execution under threshold-compatible operating conditions, enabling higher performance in the context of fault tolerance and architectural scalability. In detail, we propose an encoded-fusion- assisted teleportation (EFAT) mechanism, with two configurations for different scenarios: (1) the Minimal Hybrid, in which (E)FBQC acts as a logical entanglement bus that supplies encoded Bell pairs for teleportation-based connectivity between CBQC modules, thus replacing distance-dependent routing with teleportation and reducing routing overhead when routing dominates the logical schedule, while remaining compatible with the derived surface-code threshold condition; (2) the Extended Hybrid, in which (E)FBQC additionally participates in the computation alongside CBQC nodes, so that encoded cluster fragments can be used to execute Clifford subroutines in constant depth. Our performance analysis shows that the Minimal Hybrid can reduce routing-dominated logical depth while remaining compatible with the derived fault-tolerance condition, whereas the Extended Hybrid can further compress Clifford-dominated subroutines and may improve the temporal scheduling of magic-state supply, at the expense of higher photonic throughput and tighter synchronization requirements. Together, these results indicate a practical route toward scalable, modular, and fault-tolerant quantum computing based on encoded-fusion-assisted hybrid architectures.