Learning the expressibility of quantum circuit ansatz using transformer

• URL: http://arxiv.org/abs/2405.18837v2
• Date: Fri, 2 Aug 2024 02:08:54 GMT
• Title: Learning the expressibility of quantum circuit ansatz using transformer
• Authors: Fei Zhang, Jie Li, Zhimin He, Haozhen Situ,
• Abstract summary: We propose using a transformer model to predict the expressibility of quantum circuit ansatze. This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.
• Score: 5.368973814856243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the exponentially faster computation for certain problems, quantum computing has garnered significant attention in recent years. Variational quantum algorithms are crucial methods to implement quantum computing, and an appropriate task-specific quantum circuit ansatz can effectively enhance the quantum advantage of VQAs. However, the vast search space makes it challenging to find the optimal task-specific ansatz. Expressibility, quantifying the diversity of quantum circuit ansatz states to explore the Hilbert space effectively, can be used to evaluate whether one ansatz is superior to another. In this work, we propose using a transformer model to predict the expressibility of quantum circuit ansatze. We construct a dataset containing random PQCs generated by the gatewise pipeline, with varying numbers of qubits and gates. The expressibility of the circuits is calculated using three measures: KL divergence, relative KL divergence, and maximum mean discrepancy. A transformer model is trained on the dataset to capture the intricate relationships between circuit characteristics and expressibility. Four evaluation metrics are employed to assess the performance of the transformer. Numerical results demonstrate that the trained model achieves high performance and robustness across various expressibility measures. This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.

Related papers

• Equivalence Checking of Quantum Circuits via Intermediary Matrix Product Operator [4.306566710489809]
  Equivalence checking plays a vital role in identifying errors that may arise during compilation and optimization of quantum circuits. We introduce a novel method based on Matrix Product Operators (MPOs) for determining the equivalence of quantum circuits.
  arXiv  Detail & Related papers  (2024-10-14T18:00:00Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• 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)
• Maximising Quantum-Computing Expressive Power through Randomised Circuits [4.604271571912073]
  variational quantum algorithms (VQAs) have emerged as a promising avenue to obtain quantum advantage. We numerically demonstrate a novel approach for VQAs, utilizing randomised quantum circuits to generate the variational wavefunction. This random-circuit approach presents a trade-off between the expressive power of the variational wavefunction and time cost.
  arXiv  Detail & Related papers  (2023-12-04T15:04:42Z)
• Quantum gradient evaluation through quantum non-demolition measurements [0.0]
  We discuss a Quantum Non-Demolition Measurement protocol to estimate the derivatives of a cost function with a quantum computer. This is a key step for the implementation of variational quantum circuits.
  arXiv  Detail & Related papers  (2023-01-17T19:00:08Z)
• Fundamental limitations on optimization in variational quantum algorithms [7.165356904023871]
  A leading paradigm to establish such near-term quantum applications is variational quantum algorithms (VQAs) We prove that for a broad class of such random circuits, the variation range of the cost function vanishes exponentially in the number of qubits with a high probability. This result can unify the restrictions on gradient-based and gradient-free optimizations in a natural manner and reveal extra harsh constraints on the training landscapes of VQAs.
  arXiv  Detail & Related papers  (2022-05-10T17:14:57Z)
• 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)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• 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)
• 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)
• QUANTIFY: A framework for resource analysis and design verification of quantum circuits [69.43216268165402]
  QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits. It is based on Google Cirq and is developed with Clifford+T circuits in mind. For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
  arXiv  Detail & Related papers  (2020-07-21T15:36:25Z)

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.