Perturbative quantum simulation

Approximations based on perturbation theory are the basis for most of the quantitative predictions of quantum mechanics, whether in quantum field theory, many-body physics, chemistry or other domains. Quantum computing provides an alternative to the perturbation paradigm, but the tens of noisy qubits currently available in state-of-the-art quantum processors are of limited practical utility. In this article, we introduce perturbative quantum simulation, which combines the complementary strengths of the two approaches, enabling the solution of large practical quantum problems using noisy intermediate-scale quantum hardware. The use of a quantum processor eliminates the need to identify a solvable unperturbed Hamiltonian, while the introduction of perturbative coupling permits the quantum processor to simulate systems larger than the available number of physical qubits. After introducing the general perturbative simulation framework, we present an explicit example algorithm that mimics the Dyson series expansion. We then numerically benchmark the method for interacting bosons, fermions, and quantum spins in different topologies, and study different physical phenomena on systems of up to $48$ qubits, such as information propagation, charge-spin separation and magnetism. In addition, we use 5 physical qubits on the IBMQ cloud to experimentally simulate the $8$-qubit Ising model using our algorithm. The result verifies the noise robustness of our method and illustrates its potential for benchmarking large quantum processors with smaller ones.Comment: 35 pages, 12 figure

DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs), a field that has seen substantial growth in recent years. With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. These event markers may not be readily available or could be challenging to acquire during real-time inference, and the sequence of eye fixations may not align with the order of spoken words. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it alleviates the order mismatch between eye fixations and spoken words by introducing text-EEG contrastive alignment training, and 2) it minimizes the interference caused by individual differences in EEG waves through an invariant discrete codex. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. Furthermore, this work is the first to facilitate the translation of entire EEG signal periods without needing word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset, respectively. Codes and the final paper will be public soon

Classification for Single-Trial N170 During Responding to Facial Picture With Emotion

Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive and negative emotions of around 170 ms at the occipitotemporal electrodes (i.e., N170). Then, we further proposed the application of the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERPs were obtained by averaging most of the time while ignoring the trial-to-trial variation. In order to find an optimal classifier for emotional classification with single-trial N170 as a feature, three types of classifiers, namely, linear discriminant analysis (LDA), L1-regularized logistic regression (L1LR), and support vector machine with radial basis function (RBF-SVM), were comparatively investigated. The results showed that the single-trial N170 could be used as a classification feature to successfully distinguish positive emotion from negative emotion. L1-regularized logistic regression classifiers showed a good generalization, whereas LDA showed a relatively poor generalization. Moreover, when compared with L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle while applying it to the online operating system of brain-computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expression and that the single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain

Experimental quantum computational chemistry with optimised unitary coupled cluster ansatz

Simulation of quantum chemistry is one of the most promising applications of quantum computing. While recent experimental works have demonstrated the potential of solving electronic structures with variational quantum eigensolver (VQE), the implementations are either restricted to nonscalable (hardware efficient) or classically simulable (Hartree-Fock) ansatz, or limited to a few qubits with large errors for the more accurate unitary coupled cluster (UCC) ansatz. Here, integrating experimental and theoretical advancements of improved operations and dedicated algorithm optimisations, we demonstrate an implementation of VQE with UCC for H_2, LiH, F_2 from 4 to 12 qubits. Combining error mitigation, we produce high-precision results of the ground-state energy with error suppression by around two orders of magnitude. For the first time, we achieve chemical accuracy for H_2 at all bond distances and LiH at small bond distances in the experiment. Our work demonstrates a feasible path towards a scalable solution to electronic structure calculation, validating the key technological features and identifying future challenges for this goal.Comment: 8 pages, 4 figures in the main text, and 29 pages supplementary materials with 16 figure

Invisibility cloak designed with AI

Invisibility cloaks have long been a subject of fascination for both scientists and the general public. With advancement of artificial intelligent, it is possible to design and create cloaks with precise accuracy. In this project, optimization tool in MATLAB is used to test the effect of optimization with different parameters of the cloak. Additionally, the results of the experiment will be collected to form the base of the library for future artificial intelligent program for study.Bachelor of Engineering (Information Engineering and Media

Intra-leaf heterogeneities of hydrogen isotope compositions in leaf water and leaf wax of monocots and dicots

Several recent studies showed that leaf wax n-alkane delta H-2 values (delta H-2(wax)) within a leaf were heterogeneous in a small number of species. It still remains unclear whether the heterogeneity of intra-leaf delta H-2(wax) values is general for various species, how delta H-2(wax) values vary spatially and temporally, and whether there is a common explanation for the intra-leaf delta H-2(wax) heterogeneity in higher plants. Here we compared the hydrogen isotope compositions of leaf wax and corresponding leaf water (delta H-2(lw)) across leaf sections among a variety of monocot and dicot plant species. There is significant and consistent heterogeneity for both delta H-2(wax) and delta H-2(lw), i.e., base-to-tip 2H-enrichment for monocots (except Hemerocallis citrina, and Dactylis glomerata) whereas base-to-tip and center-to-edge increases in delta H-2(wax) and delta H-2(lw) for dicots. The consistent occurrence of variations of delta H-2(lw) and delta H-2(wax) values within a leaf imply that delta H-2(wax) values probably inherit point-to-pint from in-situ delta H-2(lw) values, and thus the intra-leaf delta H-2(wax) heterogeneity mainly results from the spatial pattern of intra-leaf delta H-2(lw) values associated with veinal structures between dicots and monocots. The general heterogeneity of intra-leaf delta H-2(wax) values further intensifies that it is necessarily needed for in-depth understanding leaf wax biomarker. (C) 2021 Elsevier B.V. All rights reserved

Effects of prepayment policy on equilibrium of the retailer-dominated channel considering manufacturer effort

Although upstream manufacturers with small- and mediumsized are gradually willing to invest green efforts for stimulating market demand, they have been encountering the challenge of securing sufficient working capital to develop the green supply chain. Thus, this paper systematically incorporates two types of prepayment policies including risk-free (RF) and risk-taking (RT) into a retailer’s dominated channel. Via deriving operational and financing equilibrium of the green supply chain, a series of interesting findings can be offered. Specifically, (1) this paper identifies a threshold value regarding the manufacturer’s own capital, and proposes two scenarios for assisting the retailer to decide whether offers the manufacturer prepayment policy. (2) The effectiveness of RF for the capital-constrained manufacturer depends on how well its green effort can be implemented. That is, provided that the quality effect is large enough, the manufacturer can get more upfront capital from the retailer, which may entirely cover its total production and green effort costs. (3) Under RT, if the manufacturer’s capital is relatively lower, RT enables the manufacturer to obtain sufficient capital and the retailer is willing to share partial of the manufacturer’s default risk. Otherwise, RT may not be the best prepayment policy for the retailer