310 research outputs found
Speeding-up the decision making of a learning agent using an ion trap quantum processor
We report a proof-of-principle experimental demonstration of the quantum
speed-up for learning agents utilizing a small-scale quantum information
processor based on radiofrequency-driven trapped ions. The decision-making
process of a quantum learning agent within the projective simulation paradigm
for machine learning is implemented in a system of two qubits. The latter are
realized using hyperfine states of two frequency-addressed atomic ions exposed
to a static magnetic field gradient. We show that the deliberation time of this
quantum learning agent is quadratically improved with respect to comparable
classical learning agents. The performance of this quantum-enhanced learning
agent highlights the potential of scalable quantum processors taking advantage
of machine learning.Comment: 21 pages, 7 figures, 2 tables. Author names now spelled correctly;
sections rearranged; changes in the wording of the manuscrip
Quantum Inference on Bayesian Networks
Performing exact inference on Bayesian networks is known to be #P-hard.
Typically approximate inference techniques are used instead to sample from the
distribution on query variables given the values of evidence variables.
Classically, a single unbiased sample is obtained from a Bayesian network on
variables with at most parents per node in time
, depending critically on , the probability the
evidence might occur in the first place. By implementing a quantum version of
rejection sampling, we obtain a square-root speedup, taking
time per sample. We exploit the Bayesian
network's graph structure to efficiently construct a quantum state, a q-sample,
representing the intended classical distribution, and also to efficiently apply
amplitude amplification, the source of our speedup. Thus, our speedup is
notable as it is unrelativized -- we count primitive operations and require no
blackbox oracle queries.Comment: 8 pages, 3 figures. Submitted to PR
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
Projective simulation with generalization
The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
present a dynamic and autonomous machinery that enables projective simulation
agents to meaningfully generalize. Projective simulation, a novel, physical
approach to artificial intelligence, was recently shown to perform well in
standard reinforcement learning problems, with applications in advanced
robotics as well as quantum experiments. Both the basic projective simulation
model and the presented generalization machinery are based on very simple
principles. This allows us to provide a full analytical analysis of the agent's
performance and to illustrate the benefit the agent gains by generalizing.
Specifically, we show that already in basic (but extreme) environments,
learning without generalization may be impossible, and demonstrate how the
presented generalization machinery enables the projective simulation agent to
learn.Comment: 14 pages, 9 figure
Quantum tree-based planning
Reinforcement Learning is at the core of a recent revolution in Arti cial Intelligence.
Simultaneously, we are witnessing the emergence of a new  eld: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the  first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020
Multiqubit and multilevel quantum reinforcement learning with quantum technologies
We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.We acknowledge support from CEDENNA basal grant No. FB0807 and Direccion de Postgrado USACH (FAC-L), FONDECYT under grant No. 1140194 (JCR), Spanish MINECO/FEDER FIS2015-69983-P and Basque Government IT986-16 (LL and ES), and Ramon y Cajal Grant RYC-2012-11391 (LL)
Towards interpretable quantum machine learning via single-photon quantum walks
Variational quantum algorithms represent a promising approach to quantum
machine learning where classical neural networks are replaced by parametrized
quantum circuits. However, both approaches suffer from a clear limitation, that
is a lack of interpretability. Here, we present a variational method to
quantize projective simulation (PS), a reinforcement learning model aimed at
interpretable artificial intelligence. Decision making in PS is modeled as a
random walk on a graph describing the agent's memory. To implement the
quantized model, we consider quantum walks of single photons in a lattice of
tunable Mach-Zehnder interferometers trained via variational algorithms. Using
an example from transfer learning, we show that the quantized PS model can
exploit quantum interference to acquire capabilities beyond those of its
classical counterpart. Finally, we discuss the role of quantum interference for
training and tracing the decision making process, paving the way for
realizations of interpretable quantum learning agents.Comment: 11+8 pages, 6+9 figures, 2 tables. F. Flamini and M. Krumm
contributed equally to this wor
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 optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
Quantum optimal control, a toolbox for devising and implementing the shapes of external fields that accomplish given tasks in the operation of a quantum device in the best way possible, has evolved into one of the cornerstones for enabling quantum technologies. The last few years have seen a rapid evolution and expansion of the field. We review here recent progress in our understanding of the controllability of open quantum systems and in the development and application of quantum control techniques to quantum technologies. We also address key challenges and sketch a roadmap for future developments
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