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In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning (RL). In RL, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average ⟨ J ⟩ rewards and ⟨ T ⟩ cl epochs to learn how to solve the task, the hybrid agent will take ⟨ T ⟩ q ⩽ α s α o ⟨ T ⟩ c l ⟨ J ⟩ epochs on average. Here, α s and α o denote constants depending on details of the quantum search and are independent of the problem size. Additionally, we prove that if the environment allows for maximally α o k max sequential coherent interactions, e.g. due to noise effects, an improvement given by ⟨ T ⟩ q ≈ α o ⟨ T ⟩ cl /(4 k max ) is still possible.

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.

In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning. In reinforcement learning, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, such as deterministic strictly epochal environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average \\(\\langle J \\rangle\\) rewards and \\(\\langle T \\rangle_c\\) epochs to learn how to solve the task, the hybrid agent will take \\(\\langle T \\rangle_q \\leq \\alpha \\sqrt{\\langle T \\rangle_c \\langle J \\rangle}\\) epochs on average. Here, \\(\\alpha\\) denotes a constant which is independent of the problem size. Additionally, we prove that if the environment allows for maximally \\(\\alpha_o k_\\text{max}\\) sequential coherent interactions, e.g. due to noise effects, an improvement given by \\(\\langle T \\rangle_q \\approx \\alpha_o\\langle T \\rangle_c/4 k_\\text{max}\\) is still possible.

The robustness of an architecture to changes is a major concern in the design of efficient and reliable state-of-the-art embedded real-time systems. Robustness is important during design process to identify if and in how far a system can accommodate later changes or updates, or whether it can be reused in a next generation product. In the product life-cycle, robustness helps the designer to perform changes as a result of product updates, integration of new components and subsystems, or modifications of the environment. In this paper we determine robustness as a performance reserve , the slack in performance before a system fails to meet timing requirements. This is measured as design sensitivity . Due to complex component interactions, resource sharing and functional dependencies, one-dimensional sensitivity analysis might not cover all effects that modifications of one system property may have on system performance. One reason is that the variation of one property can also affect the values of other system properties requiring new approaches to keep track of simultaneous parameter changes. In this paper we present a framework for one-dimensional and multi-dimensional sensitivity analysis of real-time systems. The framework is based on compositional analysis that is scalable to large systems. The one-dimensional sensitivity analysis combines a binary search technique with a set of formal equations derived from the real-time scheduling theory. The multi-dimensional sensitivity analysis engine consists of an exact algorithm that extends the one-dimensional approach, and a stochastic algorithm based on evolutionary search techniques.

The increasing complexity of heterogeneous systems-on-chip, SoC, and distributed embedded systems makes system optimization and exploration a challenging task. Ideally, a designer would try all possible system configurations and choose the best one regarding specific system requirements. Unfortunately, such an approach is not possible because of the tremendous number of design parameters with sophisticated effects on system properties. Consequently, good search techniques are needed to find design alternatives that best meet constraints and cost criteria. In this paper, we present a compositional design space exploration framework for system optimization and exploration using SymTA/S, a software tool for formal performance analysis. In contrast to many previous approaches pursuing closed automated exploration strategies over large sets of system parameters, our approach allows the designer to effectively control the exploration process to quickly find good design alternatives. An important aspect and key novelty of our approach is system optimization with traffic shaping.

We consider the task of multiple parameter estimation in the presence of strong correlated noise with a network of distributed sensors. We study how to find and improve noise-insensitive strategies. We show that sequentially probing GHZ states is optimal up to a factor of at most 4. This allows us to connect the problem to single parameter estimation, and to use techniques such as protection against correlated noise in a decoherence-free subspace, or read-out by local measurements.

We consider the sensing of scalar valued fields with specific spatial dependence using a network of sensors, e.g. multiple atoms located at different positions within a trap. We show how to harness the spatial correlations to sense only a specific signal, and be insensitive to others at different positions or with unequal spatial dependence by constructing a decoherence-free subspace for noise sources at fixed, known positions. This can be extended to noise sources lying on certain surfaces, where we encounter a connection to mirror charges and equipotential surfaces in classical electrostatics. For general situations, we introduce the notion of an approximate decoherence-free subspace, where noise for all sources within some volume is significantly suppressed, at the cost of reducing the signal strength in a controlled way. We show that one can use this approach to maintain Heisenberg-scaling over long times and for a large number of sensors, despite the presence of multiple noise sources in large volumes. We introduce an efficient formalism to construct internal states and sensor configurations, and apply it to several examples to demonstrate the usefulness and wide applicability of our approach.

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in portable systems appears to be within the reach of near-term technology.

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.

Service robots are complex, heterogeneous, software intensive systems built from components. Recent robotics research trends mainly address isolated capabilities on functional level. Non-functional properties, such as responsiveness or deterministic behavior, are addressed only in isolation (if at all). We argue that handling such non-functional properties on system level is a crucial next step. We claim that precise control over application-specific, dynamic execution and interaction behavior of functional components -- i.e. clear computation and communication semantics on model level without hidden code-defined parts -- is a key ingredient thereto. In this paper, we propose modeling concepts for these semantics, and present a meta-model which (i) enables component developers to implement component functionalities without presuming application-specific, system-level attributes, and (ii) enables system integrators to reason about causal dependencies between components as well as system-level data-flow characteristics. This allows to control data-propagation semantics and system properties such as end-to-end latencies during system integration without breaking component encapsulation.