Understanding the Scalability of Circuit Cutting Techniques for Practical Quantum Applications

• URL: http://arxiv.org/abs/2411.17756v1
• Date: Mon, 25 Nov 2024 23:16:27 GMT
• Title: Understanding the Scalability of Circuit Cutting Techniques for Practical Quantum Applications
• Authors: Songqinghao Yang, Prakash Murali,
• Abstract summary: Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers.
• Score: 1.2553583315791608
• License:
• Abstract: Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by major quantum hardware vendors as part of their scaling roadmaps. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms, such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine the qubit, quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum runtime and the classical post-processing overhead as the qubit count increases renders it impractical for larger quantum systems with current implementation strategies. As we transition from noisy quantum hardware to fault-tolerance, our work provides important guidance for the design of quantum software and runtime systems.

Related papers

• Circuit Folding: Modular and Qubit-Level Workload Management in Quantum-Classical Systems [5.6744988702710835]
  Circuit knitting is a technique that offloads some of the computational burden from quantum circuits. We propose CiFold, a novel graph-based system that identifies and leverages repeated structures within quantum circuits. Our system has been extensively evaluated across various quantum algorithms, achieving up to 799.2% reduction in quantum resource usage.
  arXiv  Detail & Related papers  (2024-12-24T23:34:17Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• Scalable Circuit Cutting and Scheduling in a Resource-constrained and Distributed Quantum System [14.348391106346876]
  Current quantum computing systems are limited in practical applications due to their limited qubit count and quality. Recent efforts have focused on multi-node quantum systems that connect multiple smaller quantum devices to execute larger circuits. We introduce FitCut, a novel approach that transforms quantum circuits into weighted graphs.
  arXiv  Detail & Related papers  (2024-05-07T17:45:53Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation [4.2754140179767415]
  We present it FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. We achieve an increase of fidelity by 14.83% compared to direct execution without cutting the circuit, and 8.45% over the state-of-the-art ILP-based method.
  arXiv  Detail & Related papers  (2023-09-30T17:38:31Z)
• Circuit Cutting with Non-Maximally Entangled States [59.11160990637615]
  Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. circuit cutting techniques enable the distribution of quantum computations through classical communication. Quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. We propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs.
  arXiv  Detail & Related papers  (2023-06-21T08:03:34Z)
• ScaleQC: A Scalable Framework for Hybrid Computation on Quantum and Classical Processors [25.18520278107402]
  Quantum processing unit (QPU) has to satisfy highly demanding quantity and quality requirements on its qubits. Quantum circuit cutting techniques cut and distribute a large quantum circuit into multiple smaller subcircuits feasible for less powerful QPUs. Our tool, called ScaleQC, addresses the bottlenecks by developing novel algorithmic techniques.
  arXiv  Detail & Related papers  (2022-07-03T01:44:31Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
  We propose that quantum circuits can be modeled as queuing networks. Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
  arXiv  Detail & Related papers  (2021-06-26T10:55:52Z)
• Machine Learning Optimization of Quantum Circuit Layouts [63.55764634492974]
  We introduce a quantum circuit mapping, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. We present empiric evidence for the feasibility of learning the layout method using approximation.
  arXiv  Detail & Related papers  (2020-07-29T05:26:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.