17,331 research outputs found
Why it is important to build robots capable of doing science
Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics are prime candidates for being studied by artificial scientists
Experimental quantum speed-up in reinforcement learning agents
Increasing demand for algorithms that can learn quickly and efficiently has
led to a surge of development within the field of artificial intelligence (AI).
An important paradigm within AI is reinforcement learning (RL), where agents
interact with environments by exchanging signals via a communication channel.
Agents can learn by updating their behaviour based on obtained feedback. The
crucial question for practical applications is how fast agents can learn to
respond correctly. An essential figure of merit is therefore the learning time.
While various works have made use of quantum mechanics to speed up the agent's
decision-making process, a reduction in learning time has not been demonstrated
yet. Here we present a RL experiment where the learning of an agent is boosted
by utilizing a quantum communication channel with the environment. We further
show that the combination with classical communication enables the evaluation
of such an improvement, and additionally allows for optimal control of the
learning progress. This novel scenario is therefore demonstrated by considering
hybrid agents, that alternate between rounds of quantum and classical
communication. We implement this learning protocol on a compact and fully
tunable integrated nanophotonic processor. The device interfaces with
telecom-wavelength photons and features a fast active feedback mechanism,
allowing us to demonstrate the agent's systematic quantum advantage in a setup
that could be readily integrated within future large-scale quantum
communication networks.Comment: 10 pages, 4 figure
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 Machine Learning: A tutorial
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.Ministerio de Ciencia, Innovación y Universidades GC2018-095113-B-I00,PID2019-104002GB-C21, and PID2019-104002GB-C22 (MCIU/AEI/FEDER, UE
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
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