Published on Thu Apr 15 2021

Quantum Architecture Search via Deep Reinforcement Learning

En-Jui Kuo, Yao-Lung L. Fang, Samuel Yen-Chi Chen
0
0
0
Abstract

Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible. We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this challenge. In the proposed framework, the DRL agent can only access the Pauli-, , expectation values and a predefined set of quantum operations for learning the target quantum state, and is optimized by the advantage actor-critic (A2C) and proximal policy optimization (PPO) algorithms. We demonstrate a successful generation of quantum gate sequences for multi-qubit GHZ states without encoding any knowledge of quantum physics in the agent. The design of our framework is rather general and can be employed with other DRL architectures or optimization methods to study gate synthesis and compilation for many quantum states.

Thu Nov 21 2019
Machine Learning
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning
0
0
0
Mon Nov 11 2019
Machine Learning
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Quantum computing exploits basic quantum phenomena such as state superposition and entanglement to perform computations. QAOA is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods.
0
0
0
Sat Dec 19 2020
Machine Learning
Quantum reinforcement learning in continuous action space
0
0
0
Wed Sep 01 2021
Artificial Intelligence
Variational Quantum Reinforcement Learning via Evolutionary Optimization
Potential applications in quantum RL are limited by the number of qubits available in the modern quantum devices. The hybrid TN-VQC architecture provides a way to perform efficient compression of the input dimension.
8
12
12
Thu Apr 09 2020
Machine Learning
Topological Quantum Compiling with Reinforcement Learning
Quantum compiling decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into asequence of elementary gates from a finite universal set.
0
0
0
Sat Aug 15 2020
Machine Learning
Reinforcement Learning with Quantum Variational Circuits
Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms. The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning.
0
0
0