Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut, a new quantum circuit cutting and mapping approach for large-sized circuits in DQC. LarQucut has several new designs. (1) LarQucut can have cutting solutions that use fewer cuts, and it does not cut a circuit into independent sub-circuits, therefore reducing the overall cutting and computing overheads. (2) LarQucut finds isomorphic sub-circuits and reuses their execution results. So, LarQucut can reduce the number of sub-circuits that need to be executed to reconstruct the large circuit's output, reducing the time spent on sampling the sub-circuits. (3) We design an adaptive quantum circuit mapping approach, which identifies qubit interaction patterns and accordingly enables the best-fit mapping policy in DQC. The experimental results show that, for large circuits with hundreds to thousands of qubits in DQC, LarQucut can provide a better cutting and mapping solution with lower overall overheads and achieves results closer to the ground truth.
Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut, a new quantum circuit cutting and mapping approach for large-sized circuits in DQC. LarQucut has several new designs. (1) LarQucut can have cutting solutions that use fewer cuts, and it does not cut a circuit into independent sub-circuits, therefore reducing the overall cutting and computing overheads. (2) LarQucut finds isomorphic sub-circuits and reuses their execution results. So, LarQucut can reduce the number of sub-circuits that need to be executed to reconstruct the large circuit's output, reducing the time spent on sampling the sub-circuits. (3) We design an adaptive quantum circuit mapping approach, which identifies qubit interaction patterns and accordingly enables the best-fit mapping policy in DQC. The experimental results show that, for large circuits with hundreds to thousands of qubits in DQC, LarQucut can provide a better cutting and mapping solution with lower overall overheads and achieves results closer to the ground truth.
Abstract In the current noisy intermediate-scale quantum (NISQ) era, the number of qubits and the depth of quantum circuits in a quantum computer are limited because of complex operation among increasing number of qubits, low-fidelity quantum gates under noise, and short coherence time of physical qubits. However, with distributed quantum computation (DQC) in which multiple small-scale quantum computers cooperate, large-scale quantum circuits can be implemented. In DQC, it is a key step to decompose large-scale quantum circuits into several small-scale subcircuits equivalently. In this paper, we propose a quantum circuit cutting scheme for the circuits consisting of only single-qubit gates and two-qubit gates. In the scheme, the number of non-local gates and the rounds of subcircuits operation are minimized by using the multi-objective simulated annealing (MOSA) algorithm to cluster the gates and to choose the cutting positions whilst using non-local gates. A reconstruction process is also proposed to calculate the probability distribution of output states of the original circuit. As an example, the 7-qubit circuit of Shor algorithm factoring 15 is used to verify the algorithm. Five cutting schemes are recommended, which can be selected according to practical requirements. Compared with the results of the mixing integer programming (MIP) algorithm, the number of execution rounds is efficiently reduced by slightly increasing the number of nonlocal gates.
Distributed quantum computing (DQC) architectures offer a scalable solution for the computational demands of large-scale quantum computing. In near-term DQC architectures, the costly remote quantum communication and the execution cost within quantum chips together significantly limit the execution efficiency of quantum circuits. To comprehensively optimize both costs, we propose DMapS, which consists of end-to-end algorithms for qubit mapping and routing. The qubit mapping component, DMapS-M, adopts a two-stage mapping strategy that decomposes a large quantum circuit into smaller ones and parallelizes the qubit mapping on quantum chips. The qubit routing component, DMapS-R, reduces remote quantum communication overhead by prioritizing the insertion of local SWAP gates and further improves transpilation efficiency by exploiting parallelism within chips. Our experimental results show that DMapS-M reduces overall overhead (including both remote quantum communication overhead and local SWAP gate overhead) by an average of 43.44% and 59.72%, respectively, compared to two baseline algorithms, and achieves an average speedup of 87.05x. DMapS-R, compared to the baseline algorithm, reduces overall overhead by an average of 8.85% and achieves an average transpilation speedup of 2.78×. Moreover, compared to the DQC-oriented quantum compiler, DMapS reduces remote communication overhead by an average of 75.16%.
In this paper, we are investigating the idea of cutting the original quantum circuits into multiple sub-circuits and evaluating the fidelity (correctness) amount of the sub-circuits results. We introduce an analysis method, Distributed Quantum Circuits (DQC), to encounter all aspects of Quantum properties from qubit's error data to the topology of Quantum processors on cutting the quantum circuits. Finally, we share our observations and experiments to introduce a new design pattern for managing distributed Quantum circuits on smaller Quantum computers.
Abstract As quantum computing rapidly evolves, scaling quantum algorithms to utilize multiple quantum processing units (QPUs) becomes crucial for overcoming the limitations of current noisy intermediate-scale quantum (NISQ) devices. This paper focuses on distributed quantum computing (DQC), specifically targeting the challenges associated with circuit cutting and circuit distribution in multi-QPU environments. By leveraging techniques such as hypergraph partitioning and threading models, this paper presents alternative strategies for dividing and managing both static and adaptive quantum circuits across multiple QPUs. A central research question is how the partitioning and distribution of quantum circuits can be optimized to minimize communication overhead and maximize computational performance in multi-QPU systems, for static and adaptive quantum circuits. To answer this question, a hypergraph partitioning method is proposed to effectively segment large static quantum circuits into manageable subcircuits, ensuring minimal inter-QPU gate operations and reduced time for the partitioning process. Additionally, a co-design threading model is presented, tailored for adaptive quantum circuits. This category of circuits can dynamically adjust their sequence of gates in runtime, based on intermediate measurements and classical control flows, creating unique challenges for circuit partition. Finally, to support the coordination between circuit partitions with static and adaptive circuits, we propose a quantum resource manager (QRM) architecture, bridging the gap between partitioning techniques and practical coordination for a scalable quantum computing system with multi-QPU architecture.
Distributed quantum computing (DQC) is a promising near-term approach for executing circuits that exceed the capacity of a single quantum processing unit (QPU). Prior work has made significant progress on circuit partitioning and reconstruction, but practical DQC requires more than cutting a circuit into smaller pieces. A runtime system must determine whether to execute the circuit intact, cut it for sequential execution on one QPU, or distribute it across multiple QPUs, while accounting for queueing, communication, reconstruction, and hardware constraints. We present a resource-aware scheduler for DQC that compares these execution alternatives at runtime using explicit models of QPU execution, communication, and host-side reconstruction, together with hardware-informed timing derived from simulated IBM QPUs. By treating communication and reconstruction as first-class scheduling costs, the scheduler selects execution plans using a more realistic view of end-to-end overhead than cutting-only approaches. Across our experiments, reconstruction accounts for over 83% of cutting overhead, distributed execution reduces P90 latency by up to 39% relative to uncut single-QPU execution, and scheduling overhead itself remains negligible, never exceeding 0.038% of end-to-end job latency. Together, these results show that DQC must be treated as a runtime scheduling problem over both quantum and classical resources, not just circuit partitioning.
Abstract In the current noisy intermediate-scale quantum era, the limited number of high-fidelity qubits and restricted circuit depth pose significant challenges for large-scale quantum computation. Fortunately, distributed quantum computing (DQC) provides a feasible solution by dividing large quantum circuits into smaller subcircuits that can be executed on existing quantum processors. In this work, we propose a generalized model of circuit reconstruction (GMCR), which is capable of handling complex cutting patterns such as U-type structures to recover the output of the original circuit from the subcircuit results. In addition to the number of nonlocal gates and execution rounds, we introduce a new objective function in multi-objective simulated annealing (MOSA)-based cutting algorithm, the number of required SWAP operations in the subsequent mapping from logical qubits to physical qubits, which is used to satisfy the hardware connectivity constraints and to further decrease the complexity of quantum circuit compiling. We verified the GMCR model by cutting five circuits: encoding circuit for the Steane 7-qubit code, circuit of Shor’s algorithm, quantum supremacy circuit, quantum circuit of Bernstein–Vazirani algorithm, and circuit of approximate quantum Fourier transform. In the case of the Steane 7-qubit code, the number of reconstruction rounds was reduced from 337 to 156 under a fixed nonlocal gate count of two, while the number of SWAP operations was also reduced from 10 to 7 compared with the earlier MOSA-based algorithm. For the U-type subcircuits, using the GMCR model, the original results can be obtained, but cannot be obtained by the dynamic definition, approximate reconstruction algorithm, and fast reconstruction algorithm. This work plays an important role in implementing large-scale DQC, a typical application of future quantum Internet.