20,462 research outputs found
Quantum Robot: Structure, Algorithms and Applications
A kind of brand-new robot, quantum robot, is proposed through fusing quantum
theory with robot technology. Quantum robot is essentially a complex quantum
system and it is generally composed of three fundamental parts: MQCU (multi
quantum computing units), quantum controller/actuator, and information
acquisition units. Corresponding to the system structure, several learning
control algorithms including quantum searching algorithm and quantum
reinforcement learning are presented for quantum robot. The theoretic results
show that quantum robot can reduce the complexity of O(N^2) in traditional
robot to O(N^(3/2)) using quantum searching algorithm, and the simulation
results demonstrate that quantum robot is also superior to traditional robot in
efficient learning by novel quantum reinforcement learning algorithm.
Considering the advantages of quantum robot, its some potential important
applications are also analyzed and prospected.Comment: 19 pages, 4 figures, 2 table
Basic protocols in quantum reinforcement learning with superconducting circuits
Superconducting circuit technologies have recently achieved quantum protocols
involving closed feedback loops. Quantum artificial intelligence and quantum
machine learning are emerging fields inside quantum technologies which may
enable quantum devices to acquire information from the outer world and improve
themselves via a learning process. Here we propose the implementation of basic
protocols in quantum reinforcement learning, with superconducting circuits
employing feedback-loop control. We introduce diverse scenarios for
proof-of-principle experiments with state-of-the-art superconducting circuit
technologies and analyze their feasibility in presence of imperfections. The
field of quantum artificial intelligence implemented with superconducting
circuits paves the way for enhanced quantum control and quantum computation
protocols.Comment: Published versio
A Survey on Quantum Reinforcement Learning
Quantum reinforcement learning is an emerging field at the intersection of
quantum computing and machine learning. While we intend to provide a broad
overview of the literature on quantum reinforcement learning (our
interpretation of this term will be clarified below), we put particular
emphasis on recent developments. With a focus on already available noisy
intermediate-scale quantum devices, these include variational quantum circuits
acting as function approximators in an otherwise classical reinforcement
learning setting. In addition, we survey quantum reinforcement learning
algorithms based on future fault-tolerant hardware, some of which come with a
provable quantum advantage. We provide both a birds-eye-view of the field, as
well as summaries and reviews for selected parts of the literature.Comment: 62 pages, 16 figure
Quantum Policy Gradient Algorithms
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments. Yet, the applicability of quantum algorithms in this setting remains very limited, notably in environments with large state and action spaces. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies by exploiting quantum interactions with an environment. However, these algorithms only offer full quadratic speed-ups in sample complexity over their classical analogs when the trained policies satisfy some regularity conditions. Interestingly, we find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved with respect to these conditions, which showcases the benefit of a fully-quantum reinforcement learning framework
Coherent Transport of Quantum States by Deep Reinforcement Learning
Some problems in physics can be handled only after a suitable \textit{ansatz
}solution has been guessed. Such method is therefore resilient to
generalization, resulting of limited scope. The coherent transport by adiabatic
passage of a quantum state through an array of semiconductor quantum dots
provides a par excellence example of such approach, where it is necessary to
introduce its so called counter-intuitive control gate ansatz pulse sequence.
Instead, deep reinforcement learning technique has proven to be able to solve
very complex sequential decision-making problems involving competition between
short-term and long-term rewards, despite a lack of prior knowledge. We show
that in the above problem deep reinforcement learning discovers control
sequences outperforming the \textit{ansatz} counter-intuitive sequence. Even
more interesting, it discovers novel strategies when realistic disturbances
affect the ideal system, with better speed and fidelity when energy detuning
between the ground states of quantum dots or dephasing are added to the master
equation, also mitigating the effects of losses. This method enables online
update of realistic systems as the policy convergence is boosted by exploiting
the prior knowledge when available. Deep reinforcement learning proves
effective to control dynamics of quantum states, and more generally it applies
whenever an ansatz solution is unknown or insufficient to effectively treat the
problem.Comment: 5 figure
- …