Supervisory Control of Quantum Discrete Event Systems

• URL: http://arxiv.org/abs/2104.09753v3
• Date: Thu, 4 May 2023 02:54:14 GMT
• Title: Supervisory Control of Quantum Discrete Event Systems
• Authors: Daowen Qiu
• Abstract summary: This paper establishes a basic framework of QDES by using it quantum finite automata (QFA) as the modelling formalisms. We present a number of new examples of QFA to illustrate the supervisory control of QDES and to verify the essential advantages of QDES over classical DES in state complexity.
• Score: 2.3097706741644686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discrete event systems (DES) have been deeply developed and applied in practice, but state complexity in DES still is an important problem to be better solved with innovative methods. With the development of quantum computing and quantum control, a natural problem is to simulate DES by means of quantum computing models and to establish {\it quantum DES} (QDES). The motivation is twofold: on the one hand, QDES have potential applications when DES are simulated and processed by quantum computers, where quantum systems are employed to simulate the evolution of states driven by discrete events, and on the other hand, QDES may have essential advantages over DES concerning state complexity for imitating some practical problems. So, the goal of this paper is to establish a basic framework of QDES by using {\it quantum finite automata} (QFA) as the modelling formalisms, and the supervisory control theorems of QDES are established and proved. Then we present a polynomial-time algorithm to decide whether or not the controllability condition holds. In particular, we construct a number of new examples of QFA to illustrate the supervisory control of QDES and to verify the essential advantages of QDES over classical DES in state complexity.

Related papers

• QCircuitNet: A Large-Scale Hierarchical Dataset for Quantum Algorithm Design [17.747641494506087]
  We introduce QCircuitNet, the first benchmark and test dataset designed to evaluate AI's capability in designing and implementing quantum algorithms. Unlike using AI for writing traditional codes, this task is fundamentally different and significantly more complicated due to highly flexible design space and intricate manipulation of qubits.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• 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)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [59.07246314484875]
  TeD-Q is an open-source software framework for quantum machine learning. It seamlessly integrates classical machine learning libraries with quantum simulators. It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement Learning [1.8710230264817362]
  In this dissertation, we investigate the non-convergence issue of Q-learning. We develop a new convergent Q-learning algorithm, which we call the convergent deep Q network (C-DQN) algorithm. We prove the convergence of C-DQN and apply it to the Atari 2600 benchmark.
  arXiv  Detail & Related papers  (2022-12-21T00:52:43Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• Neural Predictor based Quantum Architecture Search [15.045985536395479]
  Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the near term. In this work, we propose to use a neural network based predictor as the evaluation policy for quantum architecture search (QAS)
  arXiv  Detail & Related papers  (2021-03-11T08:26:12Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)
• Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features [0.0]
  We propose the Quingo framework to integrate and manage quantum-classical software and hardware to provide the programmability over HQCC applications. We also propose the Quingo programming language, an external domain-specific language highlighting timer-based timing control and opaque operation definition.
  arXiv  Detail & Related papers  (2020-09-02T06:42:51Z)
• On the Principles of Differentiable Quantum Programming Languages [13.070557640180004]
  Variational Quantum Circuits (VQCs) are predicted to be one of the most important near-term quantum applications. We propose the first formalization of auto-differentiation techniques for quantum circuits.
  arXiv  Detail & Related papers  (2020-04-02T16:46:13Z)

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.