Measurement-based adaptation protocol with quantum reinforcement learning

03/14/2018
by   F. Albarrán-Arriagada, et al.
0

Machine learning employs dynamical algorithms that mimic the human capacity to learn, where the reinforcement learning ones are among the most similar to humans in this respect. On the other hand, adaptability is an essential aspect to perform any task efficiently in a changing environment, and it is fundamental for many purposes, such as natural selection. Here, we propose an algorithm based on successive measurements to adapt one quantum state to a reference unknown state, in the sense of achieving maximum overlap. The protocol naturally provides many identical copies of the reference state, such that in each measurement iteration more information about it is obtained. In our protocol, we consider a system composed of three parts, the "environment" system, which provides the reference state copies; the register, which is an auxiliary subsystem that interacts with the environment to acquire information from it; and the agent, which corresponds to the quantum state that is adapted by digital feedback with input corresponding to the outcome of the measurements on the register. With this proposal we can achieve an average fidelity between the environment and the agent of more than 90% with less than 30 iterations of the protocol. In addition, we extend the formalism to d -dimensional states, reaching an average fidelity of around 80% in less than 400 iterations for d= 11, for a variety of genuinely quantum as well as semiclassical states. This work paves the way for the development of quantum reinforcement learning protocols using quantum data, and the future deployment of semi-autonomous quantum systems.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2018

Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer

We present an experimental realization of a measurement-based adaptation...
research
09/22/2017

Generalized Quantum Reinforcement Learning with Quantum Technologies

We propose a protocol to perform generalized quantum reinforcement learn...
research
10/17/2022

Quantum Event Learning and Gentle Random Measurements

We prove the expected disturbance caused to a quantum system by a sequen...
research
09/25/2017

Enhanced Quantum Synchronization via Quantum Machine Learning

We study the quantum synchronization between a pair of two-level systems...
research
03/03/2022

Quantum Reinforcement Learning via Policy Iteration

Quantum computing has shown the potential to substantially speed up mach...
research
08/22/2019

Improving the dynamics of quantum sensors with reinforcement learning

Recently proposed quantum-chaotic sensors achieve quantum enhancements i...
research
04/19/2022

Adaptive measurement filter: efficient strategy for optimal estimation of quantum Markov chains

Continuous-time measurements are instrumental for a multitude of tasks i...

Please sign up or login with your details

Forgot password? Click here to reset