Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices

• URL: http://arxiv.org/abs/2412.04844v1
• Date: Fri, 06 Dec 2024 08:29:46 GMT
• Title: Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices
• Authors: Alberto Marchisio, Emman Sychiuco, Muhammad Kashif, Muhammad Shafique,
• Abstract summary: We propose a methodology for quantum circuit cutting of HQNNs, allowing large quantum circuits to be executed on limited-qubit NISQ devices. Our approach preserves the accuracy of the original circuits and supports the training of quantum parameters across all subcircuits. The findings suggest that quantum circuit cutting is a promising technique for advancing Quantum Machine Learning (QML) on current quantum hardware.
• Score: 4.2435928520499635
• License:
• Abstract: The rapid advancement in Quantum Computing (QC), particularly through Noisy-Intermediate Scale Quantum (NISQ) devices, has spurred significant interest in Quantum Machine Learning (QML) applications. Despite their potential, fully-quantum QML algorithms remain impractical due to the limitations of current NISQ devices. Hybrid quantum-classical neural networks (HQNNs) have emerged as a viable alternative, leveraging both quantum and classical computations to enhance machine learning capabilities. However, the constrained resources of NISQ devices, particularly the limited number of qubits, pose significant challenges for executing large-scale quantum circuits. This work addresses these current challenges by proposing a novel and practical methodology for quantum circuit cutting of HQNNs, allowing large quantum circuits to be executed on limited-qubit NISQ devices. Our approach not only preserves the accuracy of the original circuits but also supports the training of quantum parameters across all subcircuits, which is crucial for the learning process in HQNNs. We propose a cutting methodology for HQNNs that employs a greedy algorithm for identifying efficient cutting points, and the implementation of trainable subcircuits, all designed to maximize the utility of NISQ devices in HQNNs. The findings suggest that quantum circuit cutting is a promising technique for advancing QML on current quantum hardware, since the cut circuit achieves comparable accuracy and much lower qubit requirements than the original circuit.

Related papers

• 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)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• 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)
• GSQAS: Graph Self-supervised Quantum Architecture Search [0.18899300124593643]
  Existing Quantum Architecture Search (QAS) algorithms require to evaluate a large number of quantum circuits during the search process. We propose GSQAS, a graph self-supervised QAS, which trains a predictor based on self-supervised learning. GSQAS outperforms the state-of-the-art predictor-based QAS, achieving better performance with fewer labeled circuits.
  arXiv  Detail & Related papers  (2023-03-22T08:35:28Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• 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)
• Variational Quantum Algorithms [1.9486734911696273]
  Quantum computers promise to unlock applications such as large quantum systems or solving large-scale linear algebra problems. Currently available quantum devices have serious constraints, including limited qubit numbers and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which employ a classical simulation to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints.
  arXiv  Detail & Related papers  (2020-12-16T21:00:46Z)
• CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations [18.78105450344374]
  This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. In real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers.
  arXiv  Detail & Related papers  (2020-12-03T23:52:04Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.