Most quantum computers today are constrained by hardware limitations, particularly the number of available qubits, causing significant challenges for executing large-scale quantum algorithms. Circuit cutting has emerged as a key technique to overcome these limitations by decomposing large quantum circuits into smaller subcircuits that can be executed independently and later reconstructed. In this work, we introduce Qdislib, a distributed and flexible library for quantum circuit cutting, designed to seamlessly integrate with hybrid quantum-classical high-performance computing (HPC) systems. Qdislib employs a graph-based representation of quantum circuits to enable efficient partitioning, manipulation and execution, supporting both wire cutting and gate cutting techniques. The library is compatible with multiple quantum computing libraries, including Qiskit and Qibo, and leverages distributed computing frameworks to execute subcircuits across CPUs, GPUs, and quantum processing units (QPUs) in a fully parallelized manner. We present a proof of concept demonstrating how Qdislib enables the distributed execution of quantum circuits across heterogeneous computing resources, showcasing its potential for scalable quantum-classical workflows.Most quantum computers today are constrained by hardware limitations, particularly the number of available qubits, causing significant challenges for executing large-scale quantum algorithms. Circuit cutting has emerged as a key technique to overcome these limitations by decomposing large quantum circuits into smaller subcircuits that can be executed independently and later reconstructed. In this work, we introduce Qdislib, a distributed and flexible library for quantum circuit cutting, designed to seamlessly integrate with hybrid quantum-classical high-performance computing (HPC) systems. Qdislib employs a graph-based representation of quantum circuits to enable efficient partitioning, manipulation and execution, supporting both wire cutting and gate cutting techniques. The library is compatible with multiple quantum computing libraries, including Qiskit and Qibo, and leverages distributed computing frameworks to execute subcircuits across CPUs, GPUs, and quantum processing units (QPUs) in a fully parallelized manner. We present a proof of concept demonstrating how Qdislib enables the distributed execution of quantum circuits across heterogeneous computing resources, showcasing its potential for scalable quantum-classical workflows.

International Conference for High Performance Computing, Networking, Storage and Analysis (SC)Nov 7, 20251 citation

Accelerating Quantum Subcircuit Reconstruction Utilizing Multi-Node Computation

Chuck Garcia, Ellie Vogel, Wei Tang +1 more

Today's Quantum Computers (QCs) face significant engineering challenges that limit their size and fidelity. To execute realistic applications, researchers must develop ways to move past these constraints. CutQC enables small QCs to run larger quantum circuits by cutting circuits into smaller subcircuits and then utilizing classical resources and Kronecker products to reproduce the desired output. In this work, we enhance the reconstruction process. In particular, we develop a distributed PyTorch implementation of CutQC's classical post-processing step, which can run across multiple nodes on either GPU or CPU devices. Our results show that our PyTorch implementation executes circuits of up to 35 qubits on 10- and 15-qubit QCs with efficient reconstruction. We use single-node computation as a baseline and then compare the results to the runtimes of our PyTorch workflows. By utilizing parallelism and specified devices, the reconstruction step of hybrid quantum-classical circuit execution can be improved. The original CutQC paper demonstrated a 60X to 8600X runtime speedup over classical simulation, and our work increases this speedup. These results show that large quantum circuits can be run on smaller QCs by leveraging circuit cutting with data parallelism techniques to maintain a reasonable runtime. This allows researchers to maintain the high fidelity of smaller machines while executing larger circuits.Today's Quantum Computers (QCs) face significant engineering challenges that limit their size and fidelity. To execute realistic applications, researchers must develop ways to move past these constraints. CutQC enables small QCs to run larger quantum circuits by cutting circuits into smaller subcircuits and then utilizing classical resources and Kronecker products to reproduce the desired output. In this work, we enhance the reconstruction process. In particular, we develop a distributed PyTorch implementation of CutQC's classical post-processing step, which can run across multiple nodes on either GPU or CPU devices. Our results show that our PyTorch implementation executes circuits of up to 35 qubits on 10- and 15-qubit QCs with efficient reconstruction. We use single-node computation as a baseline and then compare the results to the runtimes of our PyTorch workflows. By utilizing parallelism and specified devices, the reconstruction step of hybrid quantum-classical circuit execution can be improved. The original CutQC paper demonstrated a 60X to 8600X runtime speedup over classical simulation, and our work increases this speedup. These results show that large quantum circuits can be run on smaller QCs by leveraging circuit cutting with data parallelism techniques to maintain a reasonable runtime. This allows researchers to maintain the high fidelity of smaller machines while executing larger circuits.

IEEE International Conference on Quantum Computing and Engineering (QCE)Sep 15, 20240 citations

TensorQC: Towards Scalable Distributed Quantum Computing via Tensor Networks

Wei Tang, Margaret Martonosi

A quantum processing unit (QPU) must contain a large number of high quality qubits to produce accurate results for problems at useful scales. In contrast, most scientific and industry classical computation workloads happen in parallel on distributed systems, which rely on copying data across multiple cores. Unfortunately, copying quantum data is theoretically prohibited due to the quantum non-cloning theory. Instead, quantum circuit cutting techniques cut a large quantum circuit into multiple smaller subcircuits, distribute the subcircuits on parallel QPUs and reconstruct the results with classical computing. Such techniques make distributed hybrid quantum computing (DHQC) a possibility but also introduce an exponential classical co-processing cost in the number of cuts and easily become intractable. This paper presents TensorQC, which leverages classical tensor networks to bring an exponential runtime advantage over state-of-the-art parallelization post-processing techniques. As a result, this paper demonstrates running benchmarks that are otherwise intractable for a standalone QPU and prior circuit cutting techniques. Specifically, this paper runs six realistic benchmarks using QPUs available nowadays and a single GPU, and reduces the QPU size and quality requirements by more than $10\times$ over purely quantum platforms.A quantum processing unit (QPU) must contain a large number of high quality qubits to produce accurate results for problems at useful scales. In contrast, most scientific and industry classical computation workloads happen in parallel on distributed systems, which rely on copying data across multiple cores. Unfortunately, copying quantum data is theoretically prohibited due to the quantum non-cloning theory. Instead, quantum circuit cutting techniques cut a large quantum circuit into multiple smaller subcircuits, distribute the subcircuits on parallel QPUs and reconstruct the results with classical computing. Such techniques make distributed hybrid quantum computing (DHQC) a possibility but also introduce an exponential classical co-processing cost in the number of cuts and easily become intractable. This paper presents TensorQC, which leverages classical tensor networks to bring an exponential runtime advantage over state-of-the-art parallelization post-processing techniques. As a result, this paper demonstrates running benchmarks that are otherwise intractable for a standalone QPU and prior circuit cutting techniques. Specifically, this paper runs six realistic benchmarks using QPUs available nowadays and a single GPU, and reduces the QPU size and quality requirements by more than $10\times$ over purely quantum platforms.

arXiv:2502.03445Feb 5, 20250 citations

A CPU+DCU Heterogeneous Parallel Framework for Post-Processing Reconstruction in Quantum Circuit Cutting

Qingqing Jiang, Weidong Liu, Yufu Liu +4 more

In the NISQ era, limited qubit resources make it difficult to execute large quantum circuits directly on real hardware. Quantum circuit cutting mitigates this limitation by decomposing a large circuit into smaller subcircuits, but it shifts substantial overhead to classical post-processing. As circuit size, complexity, and cut count increase, reconstruction becomes a major computational and storage bottleneck. This paper presents a CPU+DCU heterogeneous parallel framework for circuit-cutting post-processing reconstruction. Instead of constructing a dense $2^n$ -dimensional probability vector or returning only high-probability states, the framework reconstructs the nonzero-probability states in the original output distribution from subcircuit measurement results. It combines heterogeneous CPU+DCU execution with a high/low-word integer representation for global basis-state indices beyond 64 bits and a three-level cooperative storage mechanism spanning device memory, host memory, and out-of-core storage. Experiments on the Songshan supercomputer show that the framework maintains high reconstruction fidelity while achieving up to $259\times$ speedup over an optimized serial baseline on linear-cluster states and up to $4\times$ speedup over a homogeneous CPU-parallel method on random circuits. The framework can also complete reconstruction tasks at the hundred-qubit scale. These results demonstrate that HPC-oriented heterogeneous reconstruction can effectively alleviate the classical post-processing bottleneck and improve reconstruction scalability.In the NISQ era, limited qubit resources make it difficult to execute large quantum circuits directly on real hardware. Quantum circuit cutting mitigates this limitation by decomposing a large circuit into smaller subcircuits, but it shifts substantial overhead to classical post-processing. As circuit size, complexity, and cut count increase, reconstruction becomes a major computational and storage bottleneck. This paper presents a CPU+DCU heterogeneous parallel framework for circuit-cutting post-processing reconstruction. Instead of constructing a dense $2^n$-dimensional probability vector or returning only high-probability states, the framework reconstructs the nonzero-probability states in the original output distribution from subcircuit measurement results. It combines heterogeneous CPU+DCU execution with a high/low-word integer representation for global basis-state indices beyond 64 bits and a three-level cooperative storage mechanism spanning device memory, host memory, and out-of-core storage. Experiments on the Songshan supercomputer show that the framework maintains high reconstruction fidelity while achieving up to $259\times$ speedup over an optimized serial baseline on linear-cluster states and up to $4\times$ speedup over a homogeneous CPU-parallel method on random circuits. The framework can also complete reconstruction tasks at the hundred-qubit scale. These results demonstrate that HPC-oriented heterogeneous reconstruction can effectively alleviate the classical post-processing bottleneck and improve reconstruction scalability.

quant-pharXiv:2607.27947Jul 31, 20260 citations

Scalable Circuit Cutting and Scheduling in a Resource-Constrained and Distributed Quantum System

Shuwen Kan, Zefan Du, Miguel Palma +6 more

Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are progressing towards fault tolerance. However, they face challenges in scalability and control. Recent efforts have concentrated on multi-node quantum systems that connect smaller quantum devices to execute larger circuits. Future demonstrations aim to utilize quantum channels for system coupling, but current methods often resort to classical communication with circuit cutting techniques. This involves dividing large circuits into smaller subcircuits and reconstructing them after execution. Existing cutting methods face challenges such as lengthy search times with increasing numbers of qubits and gates. Moreover, they often struggle to efficiently use resources across various worker configurations in a multi-node system. To address these challenges, we propose FitCut, a novel approach that transforms quantum circuits into weighted graphs. FitCut employs a community-based, bottom-up approach to cut circuits based on resource constraints such as qubit counts on each worker. Additionally, it includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms existing tools such as Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 388% on the worker side, leading to a system-wide improvement of 286% in accumulated circuit depth.Despite rapid developments in quantum computing, current systems remain limited in practical applications due to their constrained qubit counts and quality. Technologies such as superconducting, trapped ions, and neutral atom quantum computing are progressing towards fault tolerance. However, they face challenges in scalability and control. Recent efforts have concentrated on multi-node quantum systems that connect smaller quantum devices to execute larger circuits. Future demonstrations aim to utilize quantum channels for system coupling, but current methods often resort to classical communication with circuit cutting techniques. This involves dividing large circuits into smaller subcircuits and reconstructing them after execution. Existing cutting methods face challenges such as lengthy search times with increasing numbers of qubits and gates. Moreover, they often struggle to efficiently use resources across various worker configurations in a multi-node system. To address these challenges, we propose FitCut, a novel approach that transforms quantum circuits into weighted graphs. FitCut employs a community-based, bottom-up approach to cut circuits based on resource constraints such as qubit counts on each worker. Additionally, it includes a scheduling algorithm that optimizes resource utilization across workers. Implemented with Qiskit and evaluated extensively, FitCut significantly outperforms existing tools such as Qiskit Circuit Knitting Toolbox, reducing time costs by factors ranging from 3 to 2000 and improving resource utilization rates by up to 388% on the worker side, leading to a system-wide improvement of 286% in accumulated circuit depth.

arXiv:2405.04514IEEE International Conference on Quantum Computing and Engineering (QCE)Sep 15, 202416 citations

Quantum Circuit Partitioning For Effective Utilization of Quantum Resources

Connor Howe, Cristina Radian, Justin Woodring +2 more

Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.Near-term hardware is constrained by high error rates, small qubit counts, and relatively low output fidelity, making the execution of large, high performance quantum circuits difficult. Circuit partitioning (or circuit cutting) has emerged as a promising approach to circumvent these limitations by decomposing circuits into smaller subcircuits at two-qubit interaction points. However, it remains unclear which classes of circuits benefit the most from partitioning and under what hardware conditions it is most effective. In this work, we evaluate the suitability of quantum circuits for partitioning from three perspectives: improving fidelity, enabling distributed execution, and scaling to larger circuit sizes. Specifically, we compare uncut circuit execution against two circuit partitioning approaches: Qiskit's automatic cut finding technique and a custom performance optimized circuit cutting method. We also measure these across GHZ, QFT, brickwork, and random quantum circuits ranging from 4 to 14 qubits, using mean absolute error of expectation values and overall output fidelity. Our results show that partitioning benefits larger, highly interconnected circuits, with our custom method reducing error by up to 55\% and improve fidelity for GHZ circuits, but degrading performance for brickwork circuits at larger scales.

quant-pharXiv:2604.22664Apr 27, 20260 citations

10\times

over purely quantum platforms.

2^n

-dimensional probability vector or returning only high-probability states, the framework reconstructs the nonzero-probability states in the original output distribution from subcircuit measurement results. It combines heterogeneous CPU+DCU execution with a high/low-word integer representation for global basis-state indices beyond 64 bits and a three-level cooperative storage mechanism spanning device memory, host memory, and out-of-core storage. Experiments on the Songshan supercomputer show that the framework maintains high reconstruction fidelity while achieving up to

259\times

speedup over an optimized serial baseline on linear-cluster states and up to

4\times

speedup over a homogeneous CPU-parallel method on random circuits. The framework can also complete reconstruction tasks at the hundred-qubit scale. These results demonstrate that HPC-oriented heterogeneous reconstruction can effectively alleviate the classical post-processing bottleneck and improve reconstruction scalability.