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Register a new userExploiting structured environments for efficient energy transfer: The phonon antenna mechanism
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A non-trivial interplay between quantum coherence and dissipative environment-driven dynamics is becoming increasingly recognised as key for efficient energy transport in photosynthetic pigment-protein complexes, and converting these biologically-inspired insights into a set of design principles that can be implemented in artificial light-harvesting systems has become an active research field. Here we identify a specific design principle - the phonon antenna - that demonstrates how inter-pigment coherence is able to modify and optimize the way that excitations spectrally sample their local environmental fluctuations. We place this principle into a broader context and furthermore we provide evidence that the Fenna-Matthews-Olson complex of green sulphur bacteria has an excitonic structure that is close to such an optimal operating point, and suggest that this general design principle might well be exploited in other biomolecular systems.
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Exploiting sparsity is a key technique in accelerating quantized convolutional neural network (CNN) inference on mobile devices. Prior sparse CNN accelerators largely exploit un-structured sparsity and achieve significant speedups. Due to the unbounded, largely unpredictable sparsity patterns, however, exploiting unstructured sparsity requires complicated hardware design with significant energy and area overhead, which is particularly detrimental to mobile/IoT inference scenarios where energy and area efficiency are crucial. We propose to exploit structured sparsity, more specifically, Density Bound Block (DBB) sparsity for both weights and activations. DBB block tensors bound the maximum number of non-zeros per block. DBB thus exposes statically predictable sparsity patterns that enable lean sparsity-exploiting hardware. We propose new hardware primitives to implement DBB sparsity for (static) weights and (dynamic) activations, respectively, with very low overheads. Building on top of the primitives, we describe S2TA, a systolic array-based CNN accelerator that exploits joint weight and activation DBB sparsity and new dimensions of data reuse unavailable on the traditional systolic array. S2TA in 16nm achieves more than 2x speedup and energy reduction compared to a strong baseline of a systolic array with zero-value clock gating, over five popular CNN benchmarks. Compared to two recent non-systolic sparse accelerators, Eyeriss v2 (65nm) and SparTen (45nm), S2TA in 65nm uses about 2.2x and 3.1x less energy per inference, respectively.
In a network of interacting quantum systems achieving fast coherent energy transfer is a challenging task. While quantum systems are susceptible to a wide range of environmental factors, in many physical settings their interactions with quantized vibrations, or phonons, of a supporting structure are the most prevalent. This leads to noise and decoherence in the network, ultimately impacting the energy-transfer process. In this work, we introduce a novel type of coherent energy-transfer mechanism for quantum systems, where phonon interactions are able to actually enhance the energy transfer. Here, a shared phonon interacts with the systems and dynamically adjusts their resonances, providing remarkable directionality combined with quantum speed- up. We call this mechanism phonon-induced dynamic resonance energy transfer and show that it enables long-range coherent energy transport even in highly disordered systems.
Elucidating quantum coherence effects and geometrical factors for efficient energy transfer in photosynthesis has the potential to uncover non-classical design principles for advanced organic materials. We study energy transfer in a linear light-harvesting model to reveal that dimerized geometries with strong electronic coherences within donor and acceptor pairs exhibit significantly improved efficiency, which is in marked contrast to predictions of the classical Forster theory. We reveal that energy tuning due to coherent delocalization of photoexcitations is mainly responsible for the efficiency optimization. This coherence-assisted energy-tuning mechanism also explains the energetics and chlorophyll arrangements in the widely-studied Fenna-Matthews-Olson complex. We argue that a clustered network with rapid energy relaxation among donors and resonant energy transfer from donor to acceptor states provides a basic formula for constructing efficient light-harvesting systems, and the general principles revealed here can be generalized to larger systems and benefit future innovation of efficient molecular light-harvesting materials.
We address parameter estimation for complex/structured systems and suggest an effective estimation scheme based on continuous-variables quantum probes. In particular, we investigate the use of a single bosonic mode as a probe for Ohmic reservoirs, and obtain the ultimate quantum limits to the precise estimation of their cutoff frequency. We assume the probe prepared in a Gaussian state and determine the optimal working regime, i.e. the conditions for the maximization of the quantum Fisher information in terms of the initial preparation, the reservoir temperature and the interaction time. Upon investigating the Fisher information of feasible measurements we arrive at a remarkable simple result: homodyne detection of canonical variables allows one to achieve the ultimate quantum limit to precision under suitable, mild, conditions. Finally, upon exploiting a perturbative approach, we find the invariant sweet spots of the (tunable) characteristic frequency of the probe, able to drive the probe towards the optimal working regime.
Compliant robotics have seen successful applications in energy efficient locomotion and cyclic manipulation. However, exploitation of variable physical impedance for energy efficient sequential movements has not been extensively addressed. This work employs a hierarchical approach to encapsulate low-level optimal control for sub-movement generation into an outer loop of iterative policy improvement, thereby leveraging the benefits of both optimal control and reinforcement learning. The framework enables optimizing efficiency trade-off for minimal energy expenses in a model-free manner, by taking account of cost function weighting, variable impedance exploitation, and transition timing -- which are associated with the skill of compliance. The effectiveness of the proposed method is evaluated using two consecutive reaching tasks on a variable impedance actuator. The results demonstrate significant energy saving by improving the skill of compliance, with an electrical consumption reduction of about 30% measured in a physical robot experiment.
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