Fast optimal structures generator for parameterized quantum circuits

• URL: http://arxiv.org/abs/2201.03309v2
• Date: Mon, 11 Apr 2022 02:57:20 GMT
• Title: Fast optimal structures generator for parameterized quantum circuits
• Authors: Chuangtao Chen, Zhimin He, Shenggen Zheng, Yan Zhou, Haozhen Situ
• Abstract summary: Current structure optimization algorithms optimize the structure of quantum circuit from scratch for each new task of variational quantum algorithms (VQAs) We propose a rapid structure optimization algorithm for VQAs which automatically determines the number of quantum gates and directly generates the optimal structures for new tasks.
• Score: 4.655660925754175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current structure optimization algorithms optimize the structure of quantum circuit from scratch for each new task of variational quantum algorithms (VQAs) without using any prior experience, which is inefficient and time-consuming. Besides, the number of quantum gates is a hyper-parameter of these algorithms, which is difficult and time-consuming to determine. In this paper, we propose a rapid structure optimization algorithm for VQAs which automatically determines the number of quantum gates and directly generates the optimal structures for new tasks with the meta-trained graph variational autoencoder (VAE) on a number of training tasks. We also develop a meta-trained predictor to filter out circuits with poor performances to further accelerate the algorithm. Simulation results show that our method output structures with lower loss and it is 70 times faster in running time compared to a state-of-the-art algorithm, namely DQAS.

Related papers

• 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)
• Curriculum reinforcement learning for quantum architecture search under hardware errors [1.583327010995414]
  This work introduces a curriculum-based reinforcement learning QAS (CRLQAS) designed to tackle challenges in VQA deployment. The algorithm incorporates (i) a 3D architecture encoding and restrictions on environment dynamics to explore the search space of possible circuits efficiently. To facilitate studies, we developed an optimized simulator for our algorithm, significantly improving computational efficiency in noisy quantum circuits.
  arXiv  Detail & Related papers  (2024-02-05T20:33:00Z)
• Quantum Architecture Search with Unsupervised Representation Learning [24.698519892763283]
  Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs)
  arXiv  Detail & Related papers  (2024-01-21T19:53:17Z)
• Sequential minimum optimization algorithm with small sample size estimators [0.06445605125467573]
  Sequential minimum optimization is a machine-learning global search training algorithm. We apply it to photonics circuits where the additional challenge appears: low frequency of coincidence events lowers the speed of the algorithm.
  arXiv  Detail & Related papers  (2023-03-02T06:02:46Z)
• TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ans\"atze [0.0]
  We introduce an algorithm called TETRIS-ADAPT-VQE, which iteratively builds up variational ans"atze a few operators at a time. It results in denser but significantly shallower circuits, without increasing the number of CNOT gates or variational parameters. These improvements bring us closer to the goal of demonstrating a practical quantum advantage on quantum hardware.
  arXiv  Detail & Related papers  (2022-09-21T18:00:02Z)
• Recommender System Expedited Quantum Control Optimization [0.0]
  Quantum control optimization algorithms are routinely used to generate optimal quantum gates or efficient quantum state transfers. There are two main challenges in designing efficient optimization algorithms, namely overcoming the sensitivity to local optima and improving the computational speed. Here, we propose and demonstrate the use of a machine learning method, specifically the recommender system (RS), to deal with the latter challenge.
  arXiv  Detail & Related papers  (2022-01-29T10:25:41Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

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.