Optimal Compilation Strategies for QFT Circuits in Neutral-Atom Quantum Computing

• URL: http://arxiv.org/abs/2506.15116v1
• Date: Wed, 18 Jun 2025 03:34:03 GMT
• Title: Optimal Compilation Strategies for QFT Circuits in Neutral-Atom Quantum Computing
• Authors: Dingchao Gao, Yongming Li, Shenggang Ying, Sanjiang Li,
• Abstract summary: Neutral-atom quantum computing (NAQC) offers distinct advantages such as dynamic qubit reconfigurability, long coherence times, and high gate fidelities.<n>This paper introduces optimal compilation strategies tailored to QFT circuits and NAQC systems, addressing these challenges for both linear and grid-like architectures.
• Score: 10.15254069964668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neutral-atom quantum computing (NAQC) offers distinct advantages such as dynamic qubit reconfigurability, long coherence times, and high gate fidelities, making it a promising platform for scalable quantum computing. Despite these strengths, efficiently implementing quantum circuits like the Quantum Fourier Transform (QFT) remains a significant challenge due to atom movement overheads and connectivity constraints. This paper introduces optimal compilation strategies tailored to QFT circuits and NAQC systems, addressing these challenges for both linear and grid-like architectures. By minimizing atom movements, the proposed methods achieve theoretical lower bounds in atom movements while preserving high circuit fidelity. Comparative evaluations against state-of-the-art compilers demonstrate the superior performance of the proposed methods. These methods could serve as benchmarks for evaluating the performance of NAQC compilers.

Related papers

• TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing [60.996803677584424]
  TensoMeta-VQC is a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly.<n>Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• CutQAS: Topology-aware quantum circuit cutting via reinforcement learning [0.0]
  We propose CutQAS, a framework that integrates quantum circuit cutting with quantum architecture search (QAS) to enhance quantum chemistry simulations.<n>First, an RL agent explores all possible topologies to identify an optimal circuit structure. Subsequently, a second RL agent refines the selected topology by determining optimal circuit cuts, ensuring efficient execution on constrained hardware.
  arXiv  Detail & Related papers  (2025-04-05T13:13:50Z)
• Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures [10.264543064788711]
  We propose, Qompose, a neutral atom quantum computing framework for efficiently composing quantum circuits on 2-D topologies of neutral atoms. Our evaluation demonstrates Qompose is effective for a large collection of randomly-generated quantum circuits and a range of real-world benchmarks.
  arXiv  Detail & Related papers  (2024-09-29T23:03:08Z)
• 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)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• A joint optimization approach of parameterized quantum circuits with a tensor network [0.0]
  Current intermediate-scale quantum (NISQ) devices remain limited in their capabilities. We propose the use of parameterized Networks (TNs) to attempt an improved performance of the Variational Quantum Eigensolver (VQE) algorithm.
  arXiv  Detail & Related papers  (2024-02-19T12:53:52Z)
• Benchmarking Quantum Circuit Transformation with QKNOB Circuits [4.518076543914809]
  superconducting quantum devices impose strict connectivity constraints on quantum circuit execution.<n>This paper introduces QKNOB, a novel benchmark construction method for quantum circuit transformation.<n>We show that SABRE, the default Qiskit compiler, consistently achieves the best performance on the 53-qubit IBM Q Rochester and Google Sycamore devices.
  arXiv  Detail & Related papers  (2023-01-21T10:05:51Z)
• 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)
• Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way [0.0]
  We introduce a new benchmark, dubbed Atos Q-score (TM) The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut optimization problem. We provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware.
  arXiv  Detail & Related papers  (2021-02-25T16:26:23Z)
• 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)

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.