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Register a new userReverse engineering protocols for controlling spin dynamics
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We put forward reverse engineering protocols to shape in time the components of the magnetic field to manipulate a single spin, two independent spins with different gyromagnetic factors, and two interacting spins in short amount of times. We also use these techniques to setup protocols robust against the exact knowledge of the gyromagnetic factors for the one spin problem, or to generate entangled states for two or more spins coupled by dipole-dipole interactions.
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We show that specific quantum noise, acting as an open-system reservoir for non-locally entangled atoms, can serve to preserve rather than degrade joint coherence. This creates a new type of long-time control over hiding and recovery of quantum entanglement.
IoT security and privacy has raised grave concerns. Efforts have been made to design tools to identify and understand vulnerabilities of IoT systems. Most of the existing protocol security analysis techniques rely on a well understanding of the underlying communication protocols. In this paper, we systematically present the first manual reverse engineering framework for discovering communication protocols of embedded Linux based IoT systems. We have successfully applied our framework to reverse engineer a number of IoT systems. As an example, we present a detailed use of the framework reverse-engineering the WeMo smart plug communication protocol by extracting the firmware from the flash, performing static and dynamic analysis of the firmware and analyzing network traffic. The discovered protocol exposes severe design flaws that allow attackers to control or deny the service of victim plugs. Our manual reverse engineering framework is generic and can be applied to both read-only and writable Embedded Linux filesystems.
The uncontrolled interaction of a quantum system with its environment is detrimental for quantum coherence. In the context of solid-state qubits, techniques to mitigate the impact of fluctuating electric and magnetic fields from the environment are well-developed. In contrast, suppression of decoherence from thermal lattice vibrations is typically achieved only by lowering the temperature of operation. Here, we use a nano-electro-mechanical system (NEMS) to mitigate the effect of thermal phonons on a solid-state quantum emitter without changing the system temperature. We study the silicon-vacancy (SiV) colour centre in diamond which has optical and spin transitions that are highly sensitive to phonons. First, we show that its electronic orbitals are highly susceptible to local strain, leading to its high sensitivity to phonons. By controlling the strain environment, we manipulate the electronic levels of the emitter to probe, control, and eventually, suppress its interaction with the thermal phonon bath. Strain control allows for both an impressive range of optical tunability and significantly improved spin coherence. Finally, our findings indicate that it may be possible to achieve strong coupling between the SiV spin and single phonons, which can lead to the realisation of phonon-mediated quantum gates and nonlinear quantum phononics.
Counterdiabatic (CD) driving presents a way of generating adiabatic dynamics at arbitrary pace, where excitations due to non-adiabaticity are exactly compensated by adding an auxiliary driving term to the Hamiltonian. While this CD term is theoretically known and given by the adiabatic gauge potential, obtaining and implementing this potential in many-body systems is a formidable task, requiring knowledge of the spectral properties of the instantaneous Hamiltonians and control of highly nonlocal multibody interactions. We show how an approximate gauge potential can be systematically built up as a series of nested commutators, remaining well-defined in the thermodynamic limit. Furthermore, the resulting CD driving protocols can be realized up to arbitrary order without leaving the available control space using tools from periodically-driven (Floquet) systems. This is illustrated on few- and many-body quantum systems, where the resulting Floquet protocols significantly suppress dissipation and provide a drastic increase in fidelity.
Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we would like to reverse engineer it--to obtain a quantitative, interpretable description of how it solves a particular task. Even for simple tasks, a detailed understanding of how recurrent networks work, or a prescription for how to develop such an understanding, remains elusive. In this work, we use tools from dynamical systems analysis to reverse engineer recurrent networks trained to perform sentiment classification, a foundational natural language processing task. Given a trained network, we find fixed points of the recurrent dynamics and linearize the nonlinear system around these fixed points. Despite their theoretical capacity to implement complex, high-dimensional computations, we find that trained networks converge to highly interpretable, low-dimensional representations. In particular, the topological structure of the fixed points and corresponding linearized dynamics reveal an approximate line attractor within the RNN, which we can use to quantitatively understand how the RNN solves the sentiment analysis task. Finally, we find this mechanism present across RNN architectures (including LSTMs, GRUs, and vanilla RNNs) trained on multiple datasets, suggesting that our findings are not unique to a particular architecture or dataset. Overall, these results demonstrate that surprisingly universal and human interpretable computations can arise across a range of recurrent networks.
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