5,928 research outputs found

    Quantum many-body theory for electron spin decoherence in nanoscale nuclear spin baths

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    Decoherence of electron spins in nanoscale systems is important to quantum technologies such as quantum information processing and magnetometry. It is also an ideal model problem for studying the crossover between quantum and classical phenomena. At low temperatures or in light-element materials where the spin-orbit coupling is weak, the phonon scattering in nanostructures is less important and the fluctuations of nuclear spins become the dominant decoherence mechanism for electron spins. Since 1950s, semiclassical noise theories have been developed for understanding electron spin decoherence. In spin-based solid-state quantum technologies, the relevant systems are in the nanometer scale and the nuclear spin baths are quantum objects which require a quantum description. Recently, quantum pictures have been established to understand the decoherence and quantum many-body theories have been developed to quantitatively describe this phenomenon. Anomalous quantum effects have been predicted and some have been experimentally confirmed. A systematically truncated cluster correlation expansion theory has been developed to account for the many-body correlations in nanoscale nuclear spin baths that are built up during the electron spin decoherence. The theory has successfully predicted and explained a number of experimental results in a wide range of physical systems. In this review, we will cover these recent progresses. The limitations of the present quantum many-body theories and possible directions for future development will also be discussed.Comment: 44 pages, 29 figures, corrected many typos and added some reference

    Unified theory of classical and quantum signal sensing with a qubit

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    Quantum sensing protocols typically uses a quantum sensor to sense classical signals with the standard Ramsey inteferometry measurements. The classical signals are often real numbers determining the sensor Hamiltonian. However, for a senor embedded in a quantum environment, the signal to detect may be a quantum operator on a target quantum system. There is still no systematic method to detect such a quantum signal. Here we provide a general framework to sense static quantum signals with a qubit sensor by the Ramsey interferometry measurements, with the static classical signal sensing incorporated as a special case. This framework is based on a novel approach to simultaneously estimating the eigenvalues of the quantum signal operator with sequential projective measurements of the sensor, which can extract useful information about the target quantum system. The scheme can also be extended to sense ac quantum signals with dynamical decoupling control of the sensor. As an example, we show that a qubit sensor can simultaneously detect the individual coupling strengths with multiple target qubits in a spin-star model.Comment: 6 pages, 2 figure

    The classical nature of nuclear spin noise near clock transitions of Bi donors in silicon

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    Whether a quantum bath can be approximated as classical noise is a fundamental issue in central spin decoherence and also of practical importance in designing noise-resilient quantum control. Spin qubits based on bismuth donors in silicon have tunable interactions with nuclear spin baths and are first-order insensitive to magnetic noise at so-called clock-transitions (CTs). This system is therefore ideal for studying the quantum/classical nature of nuclear spin baths since the qubit-bath interaction strength determines the back-action on the baths and hence the adequacy of a classical noise model. We develop a Gaussian noise model with noise correlations determined by quantum calculations and compare the classical noise approximation to the full quantum bath theory. We experimentally test our model through dynamical decoupling sequence of up to 128 pulses, finding good agreement with simulations and measuring electron spin coherence times approaching one second - notably using natural silicon. Our theoretical and experimental study demonstrates that the noise from a nuclear spin bath is analogous to classical Gaussian noise if the back-action of the qubit on the bath is small compared to the internal bath dynamics, as is the case close to CTs. However, far from the CTs, the back-action of the central spin on the bath is such that the quantum model is required to accurately model spin decoherence.Comment: 5 pages, 3 figure

    Uncovering many-body correlations in nanoscale nuclear spin baths by central spin decoherence

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    Many-body correlations can yield key insights into the nature of interacting systems; however, detecting them is often very challenging in many-particle physics, especially in nanoscale systems. Here, taking a phosphorus donor electron spin in a natural-abundance 29Si nuclear spin bath as our model system, we discover both theoretically and experimentally that many-body correlations in nanoscale nuclear spin baths produce identifiable signatures in the decoherence of the central spin under multiple-pulse dynamical decoupling control. We find that when the number of decoupling -pulses is odd, central spin decoherence is primarily driven by second-order nuclear spin correlations (pairwise flip-flop processes). In contrast, when the number of -pulses is even, fourth-order nuclear spin correlations (diagonal interaction renormalized pairwise flip-flop processes) are principally responsible for the central spin decoherence. Many-body correlations of different orders can thus be selectively detected by central spin decoherence under different dynamical decoupling controls, providing a useful approach to probing many-body processes in nanoscale nuclear spin baths

    Real Estate with AI:An agent based on LangChain

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    Recent developments in large language models (LLMs) have opened new avenues for the real estate industry. These models not only understand language but also function as intelligent agents, engaging with investors through open-ended conversations and influencing their decision-making. Utilizing unstructured data from a professional Danish real estate website, we developed a real estate AI agent in both English and Danish using LangChain and Pinecone. Through testing and evaluation, our agent has demonstrated superior professional and concise outputs compared to other LLMs like Doubao and ChatGPT 4 and shown excellent performance and effectiveness. Our work serves as a reference for AI in real estate investment-related research and proposes new solutions to the "unprofessional foundation " and " expensive consulting fee" problems encountered by ordinary investors in their investment decisions.</p
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