Reviewerly/exHarmony huggingface dataset:
Quantitative Evaluation of Hardware Binary Stochastic Neurons

Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such Ising Machines is to tailor the physics of underlying hardware to the mathematics of the Ising model to improve some aspect of performance that is measured in speed to solution, energy consumption per solution or area footprint of the adopted hardware. One such approach to build an Ising spin, or a binary stochastic neuron (BSN), is a compact mixed-signal unit based on a low-barrier nanomagnet based design that uses a single magnetic tunnel junction (MTJ) and three transistors (3T-1MTJ) where the MTJ functions as a stochastic resistor (1SR). Such a compact unit can drastically reduce the area footprint of BSNs while promising massive scalability by leveraging the existing Magnetic RAM (MRAM) technology that has integrated 1T-1MTJ cells in ~Gbit densities. The 3T-1SR design however can be realized using different materials or devices that provide naturally fluctuating resistances. Extending previous work, we evaluate hardware BSNs from this general perspective by classifying necessary and sufficient conditions to design a fast and energy-efficient BSN that can be used in scaled Ising Machine implementations. We connect our device analysis to systems-level metrics by emphasizing hardware-independent figures-of-merit such as flips per second and dissipated energy per random bit that can be used to classify any Ising Machine.
TIMERS: Document-level Temporal Relation Extraction

We present TIMERS -a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification.Our proposed method leverages rhetorical discourse features and temporal arguments from semantic role labels, in addition to traditional local syntactic features, trained through a Gated Relational-GCN.Extensive experiments show that the proposed model outperforms previous methods by 5-18% on the TDDiscourse, TimeBank-Dense, and MATRES datasets due to our discourse-level modeling.
Empowering Language Understanding with Counterfactual Reasoning

Present language understanding methods have demonstrated extraordinary ability of recognizing patterns in texts via machine learning.However, existing methods indiscriminately use the recognized patterns in the testing phase that is inherently different from us humans who have counterfactual thinking, e.g., to scrutinize for the hard testing samples.Inspired by this, we propose a Counterfactual Reasoning Model, which mimics the counterfactual thinking by learning from few counterfactual samples.In particular, we devise a generation module to generate representative counterfactual samples for each factual sample, and a retrospective module to retrospect the model prediction by comparing the counterfactual and factual samples.Extensive experiments on sentiment analysis (SA) and natural language inference (NLI) validate the effectiveness of our method.
Natural quantum reservoir computing for temporal information processing

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.
Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations

Optimization of unitary transformations in Variational Quantum Algorithms benefits highly from efficient evaluation of cost function gradients with respect to amplitudes of unitary generators. We propose several extensions of the parametric-shift-rule to formulating these gradients as linear combinations of expectation values for generators with general eigen-spectrum (i.e. with more than two eigenvalues). Our approaches are exact and do not use any auxiliary qubits, instead they rely on a generator eigen-spectrum analysis. Two main directions in the parametric-shift-rule extensions are 1) polynomial expansion of the exponential unitary operator based on a limited number of different eigenvalues in the generator and 2) decomposition of the generator as a linear combination of low-eigenvalue operators (e.g. operators with only 2 or 3 eigenvalues). These techniques have a range of scalings for the number of needed expectation values with the number of generator eigenvalues from quadratic (for polynomial expansion) to linear and even $\log_2$ (for generator decompositions). This allowed us to propose efficient differentiation schemes superior to previous approaches for commonly used 2-qubit transformations (e.g. match-gates, transmon and fSim gates) and $\hat S^2$-conserving fermionic operators for the variational quantum eigensolver.
Variational approaches to constructing the many-body nuclear ground state for quantum computing

We explore the preparation of specific nuclear states on gate-based quantum hardware using variational algorithms. Large-scale classical diagonalizations of the nuclear shell model have reached sizes of ${10}^{9}--{10}^{10}$ basis states but are still severely limited by computational resources. Quantum computing can, in principle, solve such systems exactly with exponentially fewer resources than classical computing. Exact solutions for large systems require many qubits and large gate depth, but variational approaches can effectively limit the required gate depth. We use the unitary coupled cluster approach to construct approximations of the ground-state vectors, later to be used in dynamics calculations. The testing ground is the phenomenological shell model space, which allows us to mimic the complexity of the internucleon interactions. We find that often one needs to minimize over a large number of parameters, using a large number of entanglements that makes the application on existing hardware challenging. Prospects for rapid improvements with more capable hardware are, however, very encouraging.
Quantum Fourier-transform infrared spectroscopy in the fingerprint region

Infrared quantum absorption spectroscopy is one of the quantum sensing techniques, by which the infrared optical properties of a sample can be estimated through visible or near infrared photon detection without need for infrared optical source or detector, which has been an obstacle for higher sensitivity and spectrometer miniaturization. However, experimental demonstrations have been limited to wavelengths shorter than 5 µm or in the terahertz region, and have not been realized in the so-called fingerprint region of 1500-500 cm-1 (6.6 to 20 µm), which is commonly used to identify chemical compounds or molecules. Here we report the experimental demonstration of quantum Fourier-transform infrared (QFTIR) spectroscopy in the fingerprint region, by which both absorption and phase spectra (complex spectra) can be obtained from Fourier transformed quantum interferograms obtained with a single pixel visible-light detector. As demonstrations, we obtained the transmittance spectrum of a silicon wafer at around 10 µm (1000 cm-1) and complex transmittance spectrum of a synthetic fluoropolymer sheet, polytetrafluoroethylene, in the wavelength range of 8 to 10.5 µm (1250 to 950 cm-1), where absorption due to stretching modes of C-F bonds is clearly observed. These results open the way for new forms of spectroscopic devices based on quantum technologies.
Neural sentence embedding models for semantic similarity estimation in the biomedical domain

Neural network based embedding models are receiving significant attention in the field of natural language processing due to their capability to effectively capture semantic information representing words, sentences or even larger text elements in low-dimensional vector space. While current state-of-the-art models for assessing the semantic similarity of textual statements from biomedical publications depend on the availability of laboriously curated ontologies, unsupervised neural embedding models only require large text corpora as input and do not need manual curation. In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature. We trained different neural embedding models on 1.7 million articles from the PubMed Open Access dataset, and evaluated them based on a biomedical benchmark set containing 100 sentence pairs annotated by human experts and a smaller contradiction subset derived from the original benchmark set. Experimental results showed that, with a Pearson correlation of 0.819, our best unsupervised model based on the Paragraph Vector Distributed Memory algorithm outperforms previous state-of-the-art results achieved on the BIOSSES biomedical benchmark set. Moreover, our proposed supervised model that combines different string-based similarity metrics with a neural embedding model surpasses previous ontology-dependent supervised state-of-the-art approaches in terms of Pearson’s r (r = 0.871) on the biomedical benchmark set. In contrast to the promising results for the original benchmark, we found our best models’ performance on the smaller contradiction subset to be poor. In this study, we have highlighted the value of neural network-based models for semantic similarity estimation in the biomedical domain by showing that they can keep up with and even surpass previous state-of-the-art approaches for semantic similarity estimation that depend on the availability of laboriously curated ontologies, when evaluated on a biomedical benchmark set. Capturing contradictions and negations in biomedical sentences, however, emerged as an essential area for further work.
Long-distance twin-field quantum key distribution with entangled sources

Twin-field quantum key distribution (TFQKD), using single-photon-type interference, offers a way to exceed the rate-distance limit without quantum repeaters. However, it still suffers from photon losses and dark counts, which impose an ultimate limit on its transmission distance. In this Letter, we propose a scheme to implement TFQKD with an entangled coherent state source in the middle to increase its range, as well as comparing its performance under coherent attacks with that of TFQKD variants. Simulations show that our protocol has a theoretical distance advantage of 400 km. Moreover, the scheme has great robustness against the misalignment error and finite-size effects. Our work is a promising step toward long-distance secure communication and is greatly compatible with future global quantum networks.
Word-Level Coreference Resolution

Recent coreference resolution models rely heavily on span representations to find coreference links between word spans.As the number of spans is O(n 2 ) in the length of text and the number of potential links is O(n 4 ), various pruning techniques are necessary to make this approach computationally feasible.We propose instead to consider coreference links between individual words rather than word spans and then reconstruct the word spans.This reduces the complexity of the coreference model to O(n 2 ) and allows it to consider all potential mentions without pruning any of them out.We also demonstrate that, with these changes, SpanBERT for coreference resolution will be significantly outperformed by RoBERTa.While being highly efficient, our model performs competitively with recent coreference resolution systems on the OntoNotes benchmark.
One model for the learning of language

Significance It has long been hypothesized that language acquisition may be impossible without innate knowledge of the structures that occur in natural language. Here, we show that a domain general learning setup, originally developed in cognitive psychology to model rule learning, is able to acquire key pieces of natural language from relatively few examples of sentences. This develops a new approach to formalizing linguistic learning and highlights some features of language and language acquisition that may arise from general cognitive processes.
RecShard: statistical feature-based memory optimization for industry-scale neural recommendation

We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement technique for deep learning recommendation models (DLRMs). RecShard is designed based on two key observations. First, not all EMBs are equal, nor all rows within an EMB are equal in terms of access patterns. EMBs exhibit distinct memory characteristics, providing performance optimization opportunities for intelligent EMB partitioning and placement across a tiered memory hierarchy. Second, in modern DLRMs, EMBs function as hash tables. As a result, EMBs display interesting phenomena, such as the birthday paradox, leaving EMBs severely under-utilized. RecShard determines an optimal EMB sharding strategy for a set of EMBs based on training data distributions and model characteristics, along with the bandwidth characteristics of the underlying tiered memory hierarchy. In doing so, RecShard achieves over 6 times higher EMB training throughput on average for capacity constrained DLRMs. The throughput increase comes from improved EMB load balance by over 12 times and from the reduced access to the slower memory by over 87 times.
Decomposing the generalized Toffoli gate with qutrits

The problem of finding efficient decompositions of multiqubit gates is of importance for quantum computing, especially, in application to existing noisy intermediate-scale quantum devices, whose resources are substantially limited. Here we propose a decomposition scheme for a generalized $N$-qubit Toffoli gate with the use of $2N\ensuremath{-}3$ two-qutrit gates for arbitrary connectivity. The fixed number of the required additional levels (the choice of qutrits is optimal) and the use of the iSWAP gate as a native operation make our approach directly applicable for ongoing experiments with superconducting quantum processors. Specifically, we present a blueprint of the realization of the proposed scheme for the Aspen-9 processor supporting quantum operations with qutrits.
Cryptocurrency adoption: a systematic literature review and bibliometric analysis

Purpose Cryptocurrencies put consumers at the heart of a potential revolution by shifting central authority to a distributed peer-to-peer monetary system. This study aims to perform a systematic literature review and bibliometric analysis within the topic of cryptocurrencies and consumer trust. Design/methodology/approach The Web of Science database has been selected, and the analyses performed allowed us to identify five research trends obtained from the bibliographic coupling analysis: (1) Understanding consumer's (non)acceptance of cryptocurrencies, (2) Ethical aspects and trust in cryptocurrencies, (3) Blockchain technology as a trust-free technology, (4) The blockchain/trust economy, and (5) Blockchain technology: challenging trust. Findings Findings uncover the intellectual structure in the field of cryptocurrencies and consumers' trust and offer insights on the pros and cons of consumers' willingness to trust the digital currency. Originality/value The study proved a great gap in the current literature in linking cryptocurrencies and trust theories in a consumer context. The authors also outline several gaps that allowed us to propose future research guidelines.
Progress in mathematical programming solvers from 2001 to 2020

This study investigates the progress made in lp and milp solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes available today. On average, we found out that for solving lp/milp, computer hardware got about 20 times faster, and the algorithms improved by a factor of about nine for lp and around 50 for milp, which gives a total speed-up of about 180 and 1,000 times, respectively. However, these numbers have a very high variance and they considerably underestimate the progress made on the algorithmic side: many problem instances can nowadays be solved within seconds, which the old codes are not able to solve within any reasonable time.
Exploring the implementation of artificial intelligence applications among academic libraries in Taiwan

Purpose This study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation. Design/methodology/approach The author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians. Findings The author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI. Research limitations/implications The sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference. Practical implications The results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns. Originality/value This study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.
Realizing coherently convertible dual-type qubits with the same ion species

Trapped ions constitute one of the most promising systems for implementing quantum computing and networking1,2. For large-scale ion-trap-based quantum computers and networks, it is critical to have two types of qubit: one for computation and storage, and another for auxiliary operations such as qubit detection3, sympathetic cooling4–7 and entanglement generation through photon links8,9. Although the two qubit types can be implemented using two different ion species3,10–13, this approach introduces substantial complexity into creating and controlling each qubit type14,15. Here we resolve these challenges by implementing two coherently convertible qubit types using one ion species. We encode the qubits into two pairs of clock states of the 171Yb+ ions, and achieve microsecond-level conversion rates between the two types with one-way fidelities of 99.5%. We further demonstrate that operations on one qubit type, including sympathetic laser cooling, single-qubit gates and qubit detection, have crosstalk errors less than 0.06% on the other type, which is below the best-known error threshold of ~1% for fault-tolerant quantum computing using the surface code1,16. Our work establishes the feasibility and advantages of using coherently convertible dual-type qubits with the same ion species for large-scale quantum computing and networking. Quantum computing with trapped ions requires qubits that can store and manipulate quantum information, and others that can be used for destructive incoherent operations. Different states of ytterbium-171 ions can be used to realize both qubit types
Quantum error correction using squeezed Schrödinger cat states

Bosonic quantum codes redundantly encode quantum information in the states of a quantum harmonic oscillator, making it possible to detect and correct errors. Schr\"odinger cat codes -- based on the superposition of two coherent states with opposite displacements -- can correct phase-flip errors induced by dephasing, but they are vulnerable to bit-flip errors induced by particle loss. Here, we develop a bosonic quantum code relying on squeezed cat states, i.e. cat states made of a linear superposition of displaced-squeezed states. Squeezed cat states allow to partially correct errors caused by particle loss, while at the same time improving the protection against dephasing. We present a comprehensive analysis of the squeezed cat code, including protocols for code generation and elementary quantum gates. We characterize the effect of both particle loss and dephasing and develop an optimal recovery protocol that is suitable to be implemented on currently available quantum hardware. We show that with moderate squeezing, and using typical parameters of state-of-the-art quantum hardware platforms, the squeezed cat code has a resilience to particle loss errors that significantly outperforms that of the conventional cat code.