Fermion sampling is to generate probability distribution of a many-body Slater-determinant wavefunction, which is termed determinantal point process in statistical analysis. For its inherently-embedded Pauli exclusion principle, its application reach
es beyond simulating fermionic quantum many-body physics to constructing machine learning models for diversified datasets. Here we propose a fermion sampling algorithm, which has a polynomial time-complexity -- quadratic in the fermion number and linear in the system size. This algorithm is about 100% more efficient in computation time than the best known algorithms. In sampling the corresponding marginal distribution, our algorithm has a more drastic improvement, achieving a scaling advantage. We demonstrate its power on several test applications, including sampling fermions in a many-body system and a machine learning task of text summarization, and confirm its improved computation efficiency over other methods by counting floating-point operations.
Optical lattices are known for their flexibility to emulate condensed matter physics and beyond. Based on an early theoretical proposal [Science Bulletin 65, 2080 (2020)], a recent experiment published by Wang et al. [Science 372, 271 (2021)] accompl
ishes the first experimental realization of topological band structure of the ideal Weyl semimetal in ultracold atomic matter, prompting fundamental interest in the context of gapless topological physics. With a neat design of 3D spin-orbit interaction, the experiment has probed the gapless band topology through spin texture imaging and quantum quench dynamics. This work has far reaching implications to topological effects and quantum anomaly in condensed matter and high energy physics.
For underwater applications, the effects of light absorption and scattering result in image degradation. Moreover, the complex and changeable imaging environment makes it difficult to provide a universal enhancement solution to cope with the diversit
y of water types. In this letter, we present a novel underwater image enhancement (UIE) framework termed SCNet to address the above issues. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Considering the diversity of degradation is mainly rooted in the strong correlation among pixels, we apply whitening to de-correlates activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such latent encodings, the decoder can easily reconstruct the clean signal, and unaffected by the distortion types caused by the water. Experimental results on two real-world UIE datasets show that the proposed approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.
Chiral induced spin selectivity (CISS) describes efficient spin filtering by chiral molecules. This phenomenon has led to nanoscale manipulation of quantum spins with promising applications to spintronics and quantum computing, since its discovery ne
arly two decades ago. However, its underlying mechanism still remains mysterious for the required spin-orbit interaction (SOI) strength is unexpectedly large. Here we report a multi-orbital theory for CISS, where an effective SOI emerges from spontaneous formation of electron-hole pairing caused by many-body correlation. This mechanism produces a strong SOI to the order of tens of milielectronvolts which could support the large spin polarization observed in CISS at room temperature. One central ingredient of our theory is the Wannier functions of the valence and conduction bands correspond respectively to one- and two-dimensional representation of the spatial rotation symmetry around the molecule elongation direction. The induced SOI strength is found to decrease when the band gap increases. Our theory may provide important guidance for searching other molecules with CISS effects.
Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequen
ce of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.
Interacting Fermi gas provides an ideal model system to understand unconventional pairing and intertwined orders relevant to a large class of quantum materials. Rydberg-dressed Fermi gas is a recent experimental system where the sign, strength, and r
ange of the interaction can be controlled. The interaction in momentum space has a negative minimum at $q_c$ inversely proportional to the characteristic length-scale in real space, the soft-core radius $r_c$. We show theoretically that single-component (spinless) Rydberg-dressed Fermi gas in two dimensions has a rich phase diagram with novel superfluid and density wave orders due to the interplay of the Fermi momentum $p_F$, interaction range $r_c$, and interaction strength $u_0$. For repulsive bare interactions $u_0>0$, the dominant instability is $f$-wave superfluid for $p_Fr_clesssim 2$, and density wave for $p_Fr_cgtrsim 4$. The $f$-wave pairing in this repulsive Fermi gas is reminiscent of the conventional Kohn-Luttinger mechanism, but has a much higher $T_c$. For attractive bare interactions $u_0<0$, the leading instability is $p$-wave pairing. The phase diagram is obtained from functional renormalization group that treats all competing many-body instabilities in the particle-particle and particle-hole channels on equal footing.
With the emerging of touch-less human-computer interaction techniques and gadgets, mid-air hand gestures have been widely used for authentication. Much literature examined either the usability or security of a handful of gestures. This paper aims at
quantifying usability and security of gestures as well as understanding their relationship across multiple gestures. To study gesture-based authentication, we design an authentication method that combines Dynamic Time Warping (DTW) and Support Vector Machine (SVM), and conducted a user study with 42 participants over a period of 6 weeks. We objectively quantify the usability of a gesture by the number of corners and the frame length of all gesture samples, quantify the security using the equal error rate (EER), and the consistency by EER over a period of time. Meanwhile, we obtain subjective evaluation of usability and security by conducting a survey. By examining the responses, we found that the subjective evaluation confirms with the objective ones, and usability is in inverse relationship with security. We studied the consistency of gestures and found that most participants forgot gestures to some degree and reinforcing the memorization of gestures is necessary to improve the authentication performance. Finally, we performed a study with another 17 participants on shoulder surfing attacks, where attackers can observe the victims multiple times. The results show that shoulder surfing does not help to boost the attacks.
The contextual information (i.e., the time and location) in which a photo is taken can be easily tampered with or falsely claimed by forgers to achieve malicious purposes, e.g., creating fear among the general public. A rich body of work has focused
on detecting photo tampering and manipulation by verifying the integrity of image content. Instead, we aim to detect photo misuse by verifying the capture time and location of photos. This paper is motivated by the law of nature that sun position varies with the time and location, which can be used to determine whether the claimed contextual information corresponds with the sun position that the image content actually indicates. Prior approaches to inferring sun position from images mainly rely on vanishing points associated with at least two shadows, while we propose novel algorithms which utilize only one shadow in the image to infer the sun position. Meanwhile, we compute the sun position by applying astronomical algorithms which take as input the claimed capture time and location. Only when the two estimated sun positions are consistent can the claimed contextual information be genuine. We have developed a prototype called IMAGEGUARD. The experimental results show that our method can successfully estimate sun position and detect the time-location inconsistency with high accuracy. By setting the thresholds to be 9.4 degrees and 5 degrees for the sun position distance and the altitude angle distance, respectively, our system can correctly identify 91.5% of falsified photos with fake contextual information.
Quantum information scrambling has attracted much attention amid the effort to reconcile the conflict between quantum-mechanical unitarity and the thermalizaiton-irreversibility in many-body systems. Here we propose an unconventional mechanism to gen
erate quantum information scrambling through a high-complexity mapping from logical to physical degrees-of-freedom that hides the logical information into non-separable many-body-correlations. Corresponding to this mapping, we develop an algorithm to efficiently sample a Slater-determinant wavefunction and compute all physical observables in dynamics with a polynomial cost in system-size. The system shows information scrambling in the quantum many-body Hilbert space characterized by the spreading of Hamming-distance. At late time, we find emergence of classical diffusion dynamics in this quantum many-body system. We establish that the operator-mapping enabled growth in out-of-time-order-correlator exhibits exponential-scrambling behavior. The quantum information-hiding mapping approach may shed light on the understanding of fundamental connections among computational complexity, information scrambling and quantum thermalization.
We review the physics of many-body localization in models with incommensurate potentials. In particular, we consider one-dimensional quasiperiodic models with single-particle mobility edges. Although a conventional perspective suggests that delocaliz
ed states act as a thermalizing bath for the localized states in the presence of of interactions, there is evidence that such systems can display non-ergodicity. This is in part due to the fact that the delocalized states do not have any kind of protection due to symmetry or topology and are thus susceptible to localization. A study of non-interacting incommensurate models shows that they admit extended, partially extended, and fully localized many-body states. These models cannot thermalize dynamically and remain localized upon the introduction of interactions. In particular, for a certain range of energy, the system can host a non-ergodic extended (i.e. metallic) phase in which the energy eigenstates violate the eigenstate thermalization hypothesis (ETH) but the entanglement entropy obeys volume-law scaling. The level statistics and entanglement growth also indicate the lack of ergodicity in these models. The phenomenon of localization and non-ergodicity in a system with interactions despite the presence of single-particle delocalized states is closely related to the so-called many-body proximity effect and can also be observed in models with disorder coupled to systems with delocalized degrees of freedom. Many-body localization in systems with incommensurate potentials (without single-particle mobility edges) have been realized experimentally, and we show how this can be modified to study the the effects of such mobility edges. Demonstrating the failure of thermalization in the presence of a single-particle mobility edge in the thermodynamic limit would indicate a more robust violation of the ETH.
