GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits

• URL: http://arxiv.org/abs/2502.13755v1
• Date: Wed, 19 Feb 2025 14:20:07 GMT
• Title: GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits
• Authors: Ahmad Alomari, Sathish A. P. Kumar,
• Abstract summary: This study proposes a GPA for designing optimal Quantum Sensor Circuits (QSCs) The GPA generates QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) Evaluation of the GPA on a QSC that consists of two qubits and a sequence of R_x, R_y, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1.
• Score: 0.0
• License:
• Abstract: This study proposes a GPA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The GPA consists of two parts: the Quantum Policy Evaluation (QPE) and the Quantum Policy Improvement (QPI). The QPE performs phase estimation to generate the search space, while the QPI utilizes Grover search and amplitude amplification techniques to efficiently identify an optimal policy that generates optimal QSCs. The GPA generates QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) while minimizing the number of gates. The QSCs generated by the GPA are capable of producing entangled quantum states, specifically the squeezed states. High QFI indicates increased sensitivity to parameter changes, making the circuit useful for quantum state estimation and control tasks. Evaluation of the GPA on a QSC that consists of two qubits and a sequence of R_x, R_y, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. Compared to existing quantum agents, the GPA achieves higher QFI with fewer gates, demonstrating a more efficient and scalable approach to the design of QSCs. This work illustrates the potential computational power of quantum agents for solving quantum physics problems

Related papers

• Topology-Driven Quantum Architecture Search Framework [2.9862856321580895]
  We propose a Topology-Driven Quantum Architecture Search (TD-QAS) framework to identify high-performance quantum circuits. By decoupling the extensive search space into topology and gate-type components, TD-QAS avoids exploring gate configurations within low-performance topologies.
  arXiv  Detail & Related papers  (2025-02-20T05:05:53Z)
• Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance [2.502222151305252]
  quantum circuit compilation (QCC) is a crucial component of any quantum algorithm execution. We present a VQCC-objective function called the quantum Wasserstein compilation (QWC) cost function based on the quantum Wasserstein distance of order 1. An estimation method based on measurements of local Pauli-observable is utilized in a generative adversarial network to learn a given quantum circuit.
  arXiv  Detail & Related papers  (2024-09-09T17:46:40Z)
• YAQQ: Yet Another Quantum Quantizer -- Design Space Exploration of Quantum Gate Sets using Novelty Search [0.9932551365711049]
  We present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The developed software, YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates.
  arXiv  Detail & Related papers  (2024-06-25T14:55:35Z)
• 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)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
  This thesis focuses on identifying functional quantum circuits in noisy quantum hardware. We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently. In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
  arXiv  Detail & Related papers  (2024-02-21T12:30:39Z)
• 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)
• Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding [44.89303833148191]
  We develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs) We propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL.
  arXiv  Detail & Related papers  (2022-12-04T03:18:03Z)
• Evolutionary-based quantum architecture search [0.0]
  We propose an evolutionary-based quantum architecture search (EQAS) scheme for the optimal layout to balance the higher expressive power and the trainable ability. The results show that the proposed EQAS can search for the optimal QCA with less parameterized gates, and the higher accuracies are obtained by adopting EQAS for the classification tasks over three dataset.
  arXiv  Detail & Related papers  (2022-12-01T10:51:58Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• 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.