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.
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.
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.
In a distributed quantum computation, a large quantum circuit gets sliced into sub-circuits that must be executed at the same time on a quantum computing cluster. The interactions between the sub-circuits are usually defined in terms of non-local gates that require shared entangled pairs and classical communication between different nodes. Assuming that multiple end users submit distributed quantum computing (DQC) jobs to the cluster, an execution management problem arises. This is actually a parallel job scheduling problem, in which a set of jobs of varying processing times need to be scheduled on multiple machines while trying to minimize the length of the schedule. In a previous work, we started investigating the problem considering random circuits and approximating the length of each DQC job with the number of layers of the circuit. In this work, we put forward the study by considering a more realistic model for estimating DQC job lengths and by performing evaluations with circuits of practical interest.
Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.
Quantum computing offers unparalleled computational capabilities but faces significant challenges, including limited qubit counts, diverse hardware topologies, and dynamic noise/error rates, which hinder scalability and reliability. Distributed quantum computing, particularly chip-to-chip connections, has emerged as a solution by interconnecting multiple processors to collaboratively execute large circuits. While hardware advancements, such as IBM's Quantum Flamingo, focus on improving inter-chip fidelity, limited research addresses efficient circuit cutting and qubit mapping in distributed systems. This project introduces DisMap, a self-adaptive, hardware-aware framework for chip-to-chip distributed quantum systems. DisMap analyzes qubit noise and error rates to construct a virtual system topology, guiding circuit partitioning, and distributed qubit mapping to minimize SWAP overhead and enhance fidelity. Implemented with IBM Qiskit and compared with the state-of-the-art, DisMap achieves up to a 20.8\% improvement in fidelity and reduces SWAP overhead by as much as 80.2\%, demonstrating scalability and effectiveness in extensive evaluations on real quantum hardware topologies.
Currently, quantum researchers are facing the limitations of NISQ (Noisy Intermediate-Scale Quantum) computers, such as limited qubit counts and high sensitivity to external interference. In response to these challenges, two techniques have recently been proposed, among others, to enhance the execution of quantum circuits: circuit cutting and shot-wise distribution. Circuit cutting partitions large quantum circuits into smaller fragments that can be executed on today's NISQ devices. Shot-wise distribution leverages multiple heterogeneous quantum computers by distributing the shots of a single quantum circuit execution across various NISQ devices, improving resiliency and reducing overall execution time, in a flexible and customisable way. In this work, we investigate the synergic effect of a seamless composition of these two techniques into a unified pipeline of our own design, termed Cut&Shoot. This pipeline fragments a single quantum circuit into multiple independent sub-circuits, distributes the shots of each fragment across various quantum computers, and finally merges the results to reconstruct the output for the original circuit. Noisy simulation experiments seem to indicate that circuit cutting effectively reduces error rates, without the need to increase the number of shots performed. Shot-wise distribution, instead, while offering several qualitative advantages, does not significantly affect error rates, with negligible time overhead.
Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum algorithms. As a result, performing classical simulations is usually the preferred method for researching and validating large-scale quantum algorithms. However, these simulations require a huge amount of resources, as each additional qubit exponentially increases the computational space required. Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required for simulating large quantum algorithms at the cost of increased runtime. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory, each having a different type of overhead. The first method, using CutQC, cuts the circuit into smaller subcircuits and allows us to simulate a large quantum circuit on smaller machines. The second method, using Qiskit-Aer-GPU, distributes the computational space across a distributed memory system to simulate the entire quantum circuit. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node. However, circuit-splitting simulations show promising results in specific scenarios as the number of qubits is scaled.