arXiv subjects

Lin Lin

Publications and source records attributed to Lin Lin.

At least 109 records · Page 6Linked to original sources

arXiv2024

Rapid initial state preparation for the quantum simulation of strongly correlated molecules

Studies on quantum algorithms for ground state energy estimation often assume perfect ground state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here we address that problem in two ways: by faster preparation of matrix product state (MPS) approximations, and more efficient filtering of the prepared state to find the ground state energy. We show how to achieve unitary synthesis with a Toffoli complexity about $7 \times$ lower than that in prior work, and use that to derive a more efficient MPS preparation method. For filtering we present two different approaches: sampling and binary search. For both we use the theory of window functions to avoid large phase errors and minimise the complexity. We find that the binary search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about $0.003$. Finally, we estimate the total resources to perform ground state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground-states of the FeMo cofactor using an extrapolation procedure. {With a modest MPS bond dimension of 4000, our procedure produces an estimate of $\sim 0.9$ overlap squared with a candidate ground-state of the FeMo cofactor, producing a total resource estimate of $7.3 \times 10^{10}$ Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that used perfect ground state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.

quant-ph↗

arXiv2024

X-ray hardening preceding the onset of SGR 1935+2154's radio pulsar phase

Magnetars are neutron stars with extremely strong magnetic fields, frequently powering high-energy activity in X-rays. Pulsed radio emission following some X-ray outbursts have been detected (\citealt{Camilo2006,camilo2007a}), albeit its physical origin is unclear. It has long been speculated that the origin of magnetars' radio signals is different from those from canonical pulsars, although convincing evidence is still lacking. Five months after magnetar SGR 1935+2154's X-ray outburst and its associated Fast Radio Burst (FRB) 20200428, a radio pulsar phase was discovered. Here we report the discovery of X-ray spectral hardening associated with the emergence of periodic radio pulsations from SGR 1935+2154 and a detailed analysis of the properties of the radio pulses. The observations suggest that radio emission originates from the outer magnetosphere of the magnetar, and the surface heating due to the bombardment of inward-going particles from the radio emission region is responsible for the observed X-ray spectral hardening.

astro-ph.HE↗

arXiv2024

A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions

Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations.

physics.comp-ph↗

arXiv2024

Costal Cartilage Segmentation with Topology Guided Deformable Mamba: Method and Benchmark

Costal cartilage segmentation is crucial to various medical applications, necessitating precise and reliable techniques due to its complex anatomy and the importance of accurate diagnosis and surgical planning. We propose a novel deep learning-based approach called topology-guided deformable Mamba (TGDM) for costal cartilage segmentation. The TGDM is tailored to capture the intricate long-range costal cartilage relationships. Our method leverages a deformable model that integrates topological priors to enhance the adaptability and accuracy of the segmentation process. Furthermore, we developed a comprehensive benchmark that contains 165 cases for costal cartilage segmentation. This benchmark sets a new standard for evaluating costal cartilage segmentation techniques and provides a valuable resource for future research. Extensive experiments conducted on both in-domain benchmarks and out-of domain test sets demonstrate the superiority of our approach over existing methods, showing significant improvements in segmentation precision and robustness.

eess.IV↗

arXiv2024

Deep Variable-Block Chain with Adaptive Variable Selection

The architectures of deep neural networks (DNN) rely heavily on the underlying grid structure of variables, for instance, the lattice of pixels in an image. For general high dimensional data with variables not associated with a grid, the multi-layer perceptron and deep belief network are often used. However, it is frequently observed that those networks do not perform competitively and they are not helpful for identifying important variables. In this paper, we propose a framework that imposes on blocks of variables a chain structure obtained by step-wise greedy search so that the DNN architecture can leverage the constructed grid. We call this new neural network Deep Variable-Block Chain (DVC). Because the variable blocks are used for classification in a sequential manner, we further develop the capacity of selecting variables adaptively according to a number of regions trained by a decision tree. Our experiments show that DVC outperforms other generic DNNs and other strong classifiers. Moreover, DVC can achieve high accuracy at much reduced dimensionality and sometimes reveals drastically different sets of relevant variables for different regions.

stat.ML↗

arXiv2024

Single-ancilla ground state preparation via Lindbladians

We design a quantum algorithm for ground state preparation in the early fault tolerant regime. As a Monte Carlo-style quantum algorithm, our method features a Lindbladian where the target state is stationary. The construction of this Lindbladian is algorithmic and should not be seen as a specific approximation to some weakly coupled system-bath dynamics in nature. Our algorithm can be implemented using just one ancilla qubit and efficiently simulated on a quantum computer. It can prepare the ground state even when the initial state has zero overlap with the ground state, bypassing the most significant limitation of methods like quantum phase estimation. As a variant, we also propose a discrete-time algorithm, demonstrating even better efficiency and providing a near-optimal simulation cost depending on the desired evolution time and precision. Numerical simulation using Ising and Hubbard models demonstrates the efficacy and applicability of our method.

quant-ph↗

arXiv2024

The Peculiar Precursor of a Gamma-Ray Burst from a Binary Merger Involving a Magnetar

The milestone discovery of GW 170817-GRB 170817A-AT 2017gfo has shown that gravitational wave (GW) could be produced during the merger of neutron star-neutron star/black hole and that in electromagnetic (EM) wave a gamma-ray burst (GRB) and a kilonova (KN) are generated in sequence after the merger. Observationally, however, EM property before the merger phase is still unclear. Here we report a peculiar precursor in a KN-associated long-duration GRB 211211A, providing evidence of the EM before the merger. This precursor lasts $\sim$ 0.2 s, and the waiting time between the precursor and the main burst is $\sim$ 1 s, comparable to that between GW 170817 and GRB 170817A. The spectrum of the precursor could be well fit with a non-thermal cutoff power-law model instead of a blackbody. Especially, a $\sim$22 Hz Quasi-Periodic Oscillation candidate ($\sim 3σ$) is detected in the precursor. These temporal and spectral properties indicate that this precursor is probably produced by a catastrophic flare accompanying with magnetoelastic or crustal oscillations of a magnetar in binary compact merger. The strong magnetic field of the magnetar can also account for the prolonged duration of GRB 211211A. However, it poses a challenge to reconcile the rather short lifetime of a magnetar with the rather long spiraling time of a binary neutron star system only by the GW radiation before merger.

astro-ph.HE↗

arXiv2024

First Scan Search for Dark Photon Dark Matter with a Tunable Superconducting Radio-Frequency Cavity

Dark photons have emerged as promising candidates for dark matter, and their search is a top priority in particle physics, astrophysics, and cosmology. We report the first use of a tunable niobium superconducting radio-frequency cavity for a scan search of dark photon dark matter with innovative data analysis techniques. We mechanically adjusted the resonant frequency of a cavity submerged in liquid helium at a temperature of $2$ K, and scanned the dark photon mass over a frequency range of $1.37$ MHz centered at $1.3$ GHz. Our study leveraged the superconducting radio-frequency cavity's remarkably high quality factors of approximately $10^{10}$, resulting in the most stringent constraints to date on a substantial portion of the exclusion parameter space on the kinetic mixing coefficient $ε$ between dark photons and electromagnetic photons, yielding a value of $ε< 2.2 \times 10^{-16}$.

hep-ex↗

arXiv2024

Convergence of variational Monte Carlo simulation and scale-invariant pre-training

We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the energy minimization phase, the standard algorithm is scale-invariant by design, and we provide a proof of convergence for this algorithm without modifications. The pre-training stage typically does not feature such scale-invariance. We propose using a scale-invariant loss for the pretraining phase and demonstrate empirically that it leads to faster pre-training.

cs.LG↗

arXiv2024

Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-based optimization of such quantum circuits. This paper introduces a random coordinate descent algorithm as a practical and easy-to-implement alternative to the full gradient descent algorithm. This algorithm only requires one partial derivative at each iteration. Motivated by the behavior of measurement noise in the practical optimization of parameterized quantum circuits, this paper presents an optimization problem setting that is amenable to analysis. Under this setting, the random coordinate descent algorithm exhibits the same level of stochastic stability as the full gradient approach, making it as resilient to noise. The complexity of the random coordinate descent method is generally no worse than that of the gradient descent and can be much better for various quantum optimization problems with anisotropic Lipschitz constants. Theoretical analysis and extensive numerical experiments validate our findings.

quant-ph↗

arXiv2024

Dense outputs from quantum simulations

The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics using quantum computers. This problem arises frequently in applications such as quantum control and spectroscopic computation. We present a range of algorithms designed to operate on both early and fully fault-tolerant quantum platforms. These methodologies draw upon techniques like amplitude estimation, Hamiltonian simulation, quantum linear Ordinary Differential Equation (ODE) solvers, and quantum Carleman linearization. We provide a comprehensive complexity analysis with respect to the evolution time $T$ and error tolerance $ε$. Our results demonstrate that the linearization approach can nearly achieve optimal complexity $\mathcal{O}(T/ε)$ for a certain type of low-rank dense outputs. Moreover, we provide a linearization of the dense output problem that yields an exact and finite-dimensional closure which encompasses the original states. This formulation is related to the Koopman Invariant Subspace theory and may be of independent interest in nonlinear control and scientific machine learning.

quant-ph↗

arXiv2024

Multi-level quantum signal processing with applications to ground state preparation using fast-forwarded Hamiltonian evolution

The preparation of the ground state of a Hamiltonian $H$ with a large spectral radius has applications in many areas such as electronic structure theory and quantum field theory. Given an initial state with a constant overlap with the ground state, and assuming that the Hamiltonian $H$ can be efficiently simulated with an ideal fast-forwarding protocol, we first demonstrate that employing a linear combination of unitaries (LCU) approach can prepare the ground state at a cost of $\mathcal{O}(\log^2(\|H\| Δ^{-1}))$ queries to controlled Hamiltonian evolution. Here $\|H\|$ is the spectral radius of $H$ and $Δ$ the spectral gap. However, traditional Quantum Signal Processing (QSP)-based methods fail to capitalize on this efficient protocol, and its cost scales as $\mathcal{O}(\|H\| Δ^{-1})$. To bridge this gap, we develop a multi-level QSP-based algorithm that exploits the fast-forwarding feature. This novel algorithm not only matches the efficiency of the LCU approach when an ideal fast-forwarding protocol is available, but also exceeds it with a reduced cost that scales as $\mathcal{O}(\log(\|H\| Δ^{-1}))$. Additionally, our multi-level QSP method requires only $\mathcal{O}(\log(\|H\| Δ^{-1}))$ coefficients for implementing single qubit rotations. This eliminates the need for constructing the PREPARE oracle in LCU, which prepares a state encoding $\mathcal{O}(\|H\| Δ^{-1})$ coefficients regardless of whether the Hamiltonian can be fast-forwarded.

quant-ph↗

arXiv2024

Eliminating nearfield coupling in dense high quality factor phase gradient metasurfaces

High Q phase gradient metasurfaces are becoming promising elements for revolutionizing light manipulation but near-field coupling typically forces a trade-off between quality factor and resolution. Here, we show a strategy for not just reducing but eliminating coupling-based nonlocal effects in wave shaping metasurfaces composed of meta-pixels with arbitrarily high Q arranged with sub-diffraction spatial resolution. By working at a zero-coupling regime introduced by the interference between enhanced longitudinal and transverse electric fields, the tradeoff between Q and resolution no longer exists. Exploiting for wave shaping the ability to fully suppress coupling between high Q meta-atoms, we numerically show structurally uniform devices that produce beam-splitting to angles of $\pm53^o$ and beam-steering to an angle of $33^o$ with diffraction efficiencies over 90% via refractive index bias of just $2\times10^{-6}$ and $7\times10^{-6}$, respectively. These are made possible by the meta-structure supporting local dipole resonances with Qs of 2.8 million and 0.87 million respectively, arranged with a dense pixel pitch of $λ/1.6$. Extending the approach to structures with $λ/2.2$ resolution we also unlock full-field beam steering via index biasing of just $1\times10^{-4}$. The signature of a zero-coupling regime is discovered in the form of a sign flip in the angular dispersion with resonant wavelength in experiment which validates our scheme. Aside from triangulating a perfect decoupling configuration, one of our fabricated nanofin-isolated metasurfaces with Q-factor >870 has a resonant wavelength that stays within the half linewidth for incident angles of $-20^o$ to $20^o$. Our platform provides a route for densely arrayed high Q metasurfaces with independently addressable constituent meta-atoms, paving the way for highly efficient nonlinear and dynamic wavefront shaping.

physics.optics↗

arXiv2024

Type-printable photodetector arrays for multichannel meta-infrared imaging

Multichannel meta-imaging, inspired by the parallel-processing capability of neuromorphic computing, offers significant advancements in resolution enhancement and edge discrimination in imaging systems, extending even into the mid- to far-infrared spectrum. Currently typical multichannel infrared imaging systems consist of separating optical gratings or merging multi-cameras, which require complex circuit design and heavy power consumption, hindering the implementation of advanced human-eye-like imagers. Here, we present a novel approach for printable graphene plasmonic photodetector arrays driven by a ferroelectric superdomain for multichannel meta-infrared imaging with enhanced edge discrimination. The fabricated photodetectors exhibited multiple spectral responses with zero-bias operation by directly rescaling the ferroelectric superdomain instead of reconstructing the separated gratings. We also demonstrated enhanced and faster shape classification (98.1%) and edge detection (98.2%) using our multichannel infrared images compared with single-channel detectors. Our proof-of-concept photodetector arrays simplify multichannel infrared imaging systems and hold great potential for applications in efficient edge detection in human-brain-type machine vision.

physics.optics↗

arXiv2024

Simulating Open Quantum Systems Using Hamiltonian Simulations

We present a novel method to simulate the Lindblad equation, drawing on the relationship between Lindblad dynamics, stochastic differential equations, and Hamiltonian simulations. We derive a sequence of unitary dynamics in an enlarged Hilbert space that can approximate the Lindblad dynamics up to an arbitrarily high order. This unitary representation can then be simulated using a quantum circuit that involves only Hamiltonian simulation and tracing out the ancilla qubits. There is no need for additional postselection in measurement outcomes, ensuring a success probability of one at each stage. Our method can be directly generalized to the time-dependent setting. We provide numerical examples that simulate both time-independent and time-dependent Lindbladian dynamics with accuracy up to the third order.

quant-ph↗

arXiv2024

Direct evidence of low work function on SrVO$_3$ cathode using thermionic electron emission microscopy and high-field ultraviolet photoemission spectroscopy

Perovskite SrVO$_3$ has recently been proposed as a novel electron emission cathode material. Density functional theory (DFT) calculations suggest multiple low work function surfaces and recent experimental efforts have consistently demonstrated effective work functions of ~2.7 eV for polycrystalline samples, both results suggesting, but not directly confirming, some fraction of even lower work function surface is present. In this work, thermionic electron emission microscopy (ThEEM) and high-field ultraviolet photoemission spectroscopy are used to study the local work function distribution and measure the work function of a partially-oriented-(110)-SrVO$_3$ perovskite oxide cathode surface. Our results show direct evidence of low work function patches of about 2.1 eV on the cathode surface, with corresponding onset of observable thermionic emission at 750 $^o$C. We hypothesize that, in our ThEEM experiments, the high applied electric field suppresses the patch field effect, enabling the direct measurement of local work functions. This measured work function of 2.1 eV is comparable to the previous DFT-calculated work function value of the SrVO-terminated (110) SrVO$_3$ surface (2.3 eV) and SrO terminated (100) surface (1.9 eV). The measured 2.1 eV value is also much lower than the work function for the (001) LaB$_6$ single crystal cathode (~2.7 eV) and comparable to the effective work function of B-type dispenser cathodes (~2.1 eV). If SrVO$_3$ thermionic emitters can be engineered to access domains of this low 2.1 eV work function, they have potential to significantly improve thermionic emitter-based technologies.

cond-mat.mtrl-sci↗

arXiv2024

Performance Analysis and ISI Mitigation with Imperfect Transmitter in Molecular Communication

In molecular communication (MC), molecules are released from the transmitter to convey information. This paper considers a realistic molecule shift keying (MoSK) scenario with two species of molecule in two reservoirs, where the molecules are harvested from the environment and placed into different reservoirs, which are purified by exchanging molecules between the reservoirs. This process consumes energy, and for a reasonable energy cost, the reservoirs cannot be pure; thus, our MoSK transmitter is imperfect, releasing mixtures of both molecules for every symbol, resulting in inter-symbol interference (ISI). To mitigate ISI, the properties of the receiver are analyzed and a detection method based on the ratio of different molecules is proposed. Theoretical and simulation results are provided, showing that with the increase of energy cost, the system achieves better performance. The good performance of the proposed detection scheme is also demonstrated.

cs.IT↗

arXiv2024

CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)

Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to extract them from billions of astronomical objects. The existing mainstream lens-finding tools are based on machine learning algorithms and applied to cut-out-centered galaxies. However, according to the design and survey strategy of optical surveys by CSST, preparing cutouts with multiple bands requires considerable efforts. To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images. Moreover, given that multi-color images of strong lensing systems can provide insights into their physical characteristics, our framework is specifically crafted to identify strong lensing systems in images with any number of channels. As evaluated using CSST mock data based on an Semi-Analytic Model named CosmoDC2, our framework achieves precision and recall rates of 0.98 and 0.90, respectively. To evaluate the effectiveness of our method in real observations, we have applied it to a subset of images from the DESI Legacy Imaging Surveys and media images from Euclid Early Release Observations. 61 new strong lensing system candidates are discovered by our method. However, we also identified false positives arising primarily from the simplified galaxy morphology assumptions within the simulation. This underscores the practical limitations of our approach while simultaneously highlighting potential avenues for future improvements.

astro-ph.IM↗