Quarl: A Learning-Based Quantum Circuit Optimizer

• URL: http://arxiv.org/abs/2307.10120v1
• Date: Mon, 17 Jul 2023 19:21:22 GMT
• Title: Quarl: A Learning-Based Quantum Circuit Optimizer
• Authors: Zikun Li, Jinjun Peng, Yixuan Mei, Sina Lin, Yi Wu, Oded Padon, Zhihao Jia
• Abstract summary: This paper presents Quarl, a learning-based quantum circuit. Applying reinforcement learning to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation.
• Score: 8.994999903946848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily decrease performance to achieve a final performance improvement. This paper presents Quarl, a learning-based quantum circuit optimizer. Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation. Quarl addresses these issues with a novel neural architecture and RL-training procedure. Our neural architecture decomposes the action space into two parts and leverages graph neural networks in its state representation, both of which are guided by the intuition that optimization decisions can be mostly guided by local reasoning while allowing global circuit-wide reasoning. Our evaluation shows that Quarl significantly outperforms existing circuit optimizers on almost all benchmark circuits. Surprisingly, Quarl can learn to perform rotation merging, a complex, non-local circuit optimization implemented as a separate pass in existing optimizers.

Related papers

• Optimizing Quantum Circuits, Fast and Slow [7.543907169342984]
  We present a framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, GUOQ, for optimizing quantum circuits.
  arXiv  Detail & Related papers  (2024-11-06T18:34:35Z)
• 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)
• Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions [55.616364225463066]
  Reinforcement learning for the optimization of quantum circuits uses an agent whose goal is to maximize the value of a reward function. We propose an exponential reward function which is sensitive to structural properties of the circuit.
  arXiv  Detail & Related papers  (2023-11-21T10:33:26Z)
• Cost Explosion for Efficient Reinforcement Learning Optimisation of Quantum Circuits [55.616364225463066]
  Reinforcement Learning (RL) is a recent approach for learning strategies to optimise quantum circuits by increasing the reward of an optimisation agent. Our goal is to improve the agent's optimization strategy, by including hints about how quantum circuits are optimized manually. We show that allowing cost explosions offers significant advantages for RL training, such as reaching optimum circuits.
  arXiv  Detail & Related papers  (2023-11-21T10:16:03Z)
• Quantum Circuit Unoptimization [0.6449786007855248]
  We construct a quantum algorithmic primitive called quantum circuit unoptimization. It makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence. We use quantum circuit unoptimization to generate compiler benchmarks and evaluate circuit optimization performance.
  arXiv  Detail & Related papers  (2023-11-07T08:38:18Z)
• A Circuit Domain Generalization Framework for Efficient Logic Synthesis in Chip Design [92.63517027087933]
  A key task in Logic Synthesis (LS) is to transform circuits into simplified circuits with equivalent functionalities. To tackle this task, many LS operators apply transformations to subgraphs -- rooted at each node on an input DAG -- sequentially. We propose a novel data-driven LS operator paradigm, namely PruneX, to reduce ineffective transformations.
  arXiv  Detail & Related papers  (2023-08-22T16:18:48Z)
• Optimizing Variational Circuits for Higher-Order Binary Optimization [2.578242050187029]
  We propose new approaches to encode their Hamiltonian into a ready-to-implement circuit involving only two-qubit gates. We evaluate our approaches by comparing them with the state of the art, showcasing clear gains in terms of circuit depth.
  arXiv  Detail & Related papers  (2023-07-31T15:27:06Z)
• Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation [69.43216268165402]
  We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.
  arXiv  Detail & Related papers  (2023-03-06T16:51:30Z)
• 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)
• 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)

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.