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Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification of network architectures, in contrast to traditional convolutional neural networks (CNNs). Leveraging the advantage of FNOs, we propose a novel deep-learning framework for classifying images with varying sizes. Particularly, we simultaneously train the proposed network on multi-sized images. As a practical application, we consider the problem of predicting the label (e.g., permeability) of three-dimensional digital porous media. To construct the framework, an intuitive approach is to connect FNO layers to a classifier using adaptive max pooling. First, we show that this approach is only effective for porous media with fixed sizes, whereas it fails for porous media of varying sizes. To overcome this limitation, we introduce our approach: instead of using adaptive max pooling, we use static max pooling with the size of channel width of FNO layers. Since the channel width of the FNO layers is independent of input image size, the introduced framework can handle multi-sized images during training. We show the effectiveness of the introduced framework and compare its performance with the intuitive approach through the example of the classification of three-dimensional digital porous media of varying sizes.

In this technical report, we extensively investigate the accuracy of outputs from well-known generative artificial intelligence (AI) applications in response to prompts describing common fluid motion phenomena familiar to the fluid mechanics community. We examine a range of applications, including Midjourney, Dall-E, Runway ML, Microsoft Designer, Gemini, Meta AI, and Leonardo AI, introduced by prominent companies such as Google, OpenAI, Meta, and Microsoft. Our text prompts for generating images or videos include examples such as "Von Karman vortex street", "flow past an airfoil", "Kelvin-Helmholtz instability", "shock waves on a sharp-nosed supersonic body", etc. We compare the images generated by these applications with real images from laboratory experiments and numerical software. Our findings indicate that these generative AI models are not adequately trained in fluid dynamics imagery, leading to potentially misleading outputs. Beyond text-to-image/video generation, we further explore the transition from image/video to text generation using these AI tools, aiming to investigate the accuracy of their descriptions of fluid motion phenomena. This report serves as a cautionary note for educators in academic institutions, highlighting the potential for these tools to mislead students. It also aims to inform researchers at these renowned companies, encouraging them to address this issue. We conjecture that a primary reason for this shortcoming is the limited access to copyright-protected fluid motion images from scientific journals.

The relevance of shallow-depth quantum circuits has recently increased, mainly due to their applicability to near-term devices. In this context, one of the main goals of quantum circuit complexity is to find problems that can be solved by quantum shallow circuits but require more computational resources classically. Our first contribution in this work is to prove new separations between classical and quantum constant-depth circuits. Firstly, we show a separation between constant-depth quantum circuits with quantum advice $\mathsf{QNC}^0/\mathsf{qpoly}$, and $\mathsf{AC}^0[p]$, which is the class of classical constant-depth circuits with unbounded-fan in and $\pmod{p}$ gates. In addition, we show a separation between $\mathsf{QAC}^0$, which additionally has Toffoli gates with unbounded control, and $\mathsf{AC}^0[p]$. This establishes the first such separation for a shallow-depth quantum class that does not involve quantum fan-out gates. Secondly, we consider $\mathsf{QNC}^0$ circuits with infinite-size gate sets. We show that these circuits, along with (classical or quantum) prime modular gates, can implement threshold gates, showing that $\mathsf{QNC}^0[p]=\mathsf{QTC}^0$. Finally, we also show that in the infinite-size gateset case, these quantum circuit classes for higher-dimensional Hilbert spaces do not offer any advantage to standard qubit implementations.

Cilj: Williamsov sindrom je neurorazvojni poremećaj uzrokovan delecijom na kromosomu 7. Karakterizira ga niz medicinskih problema uz demonstraciju neprilagodljivih emocionalnih i fizičkih odgovora na podražaje iz okoline. Nadalje, poremećaj senzorne integracije česte su kod djece s Williamsovim sindromom. Stoga je ova studija imala za cilj izvijestiti o poteškoćama senzorne integracije kod djece s Williamsovim sindromom u Turskoj. Metode: Uključeno je dvadeset troje djece s Williamsovim sindromom (prosječne dobi 63,16±13,50 mjeseci; ženskog spola n=13) i dvadeset dvoje djece u tipičnom razvoju (prosječne dobi 67,66±13,23 mjeseci; ženskog spola n=12). Roditelji su ispunjavali upitnik senzornog profila (Sensory Profile Questionnaire). Podaci su analizirani pomoću deskriptivne statistike i Mann-Whitney U testa. Rezultati: Djeca s Williamsovim sindromom pokazala su poremećaj senzorne integracije u područjima senzorne obrade, modulacije, ponašanja i emocionalnog odgovora. Štoviše, imaju poteškoće u slabom registriranju, traženju osjeta, senzornoj osjetljivosti, izbjegavanju osjeta, senzornom traženju, emocionalno reaktivnoj niskoj izdržljivosti/tonusu, oralnoj osjetljivosti, nepažnji/distraktibilnosti, slabom registriranju, sjedilačkom ponašanju i poteškoćama fine motorike/perceptivnih vještina u usporedbi s njihovi vršnjaci u tipičnom razvoju. Zaključci: Ovi nalazi, uzeti u obzir sa sličnim objavljenim studijama, potvrđuju prevalenciju i vrste poremećaja senzorne integracije u Williamsovom sindromu, Aim: Williams syndrome is a neurodevelopmental disorder caused by a deletion on chromosome 7. It is characterized by a range of medical problems in addition to the demonstration of maladaptive emotional and physical responses to environmental stimuli. Furthermore, sensory processing abnormalities are common in children with Williams syndrome. Therefore, this study aimed to report sensory processing difficulties in children with Williams syndrome in Turkey. Methods: Twenty-three children with Williams syndrome (mean age 63.16±13.50 months; females n=13) and twenty-two typically developing children (mean age 67.66±13.23 months; females n=12) were included. Parents completed the Sensory Profile Questionnaire. Data were analysed using descriptive statistics and the Mann-Whitney U test. Results: Children with Williams syndrome demonstrated sensory processing dysfunction in the sensory processing, modulation, behaviour and emotional response areas. Moreover, they have difficulties in low registration, sensation seeking, sensory sensitivity, sensation avoiding, sensory seeking, emotionally reactive low endurance/tone, oral sensitivity, inattention/distractibility, poor registration, sedentary behaviours, and fine motor/perceptual skills factors compared to their typically developing peers. Conclusions: These findings, considered with similar published studies, confirm the prevalence and types of sensory processing abnormalities in Williams syndrome

Quantum networks have the potential to bring many novel applications to communication networks and distributed computing, leading to more secure communication, accelerated communication rates, and more powerful computing methods. With this thesis, we explore such applications. We explore designs for entanglement-assisted communication systems and distributed quantum computers. We then develop simulation software for validating the designs and developing novel applications for quantum networks., Quantennetzwerke haben das Potenzial, viele neuartige Anwendungen für Kommunikationsnetzwerke und verteiltes Rechnen zu eröffnen und zu sichererer Kommunikation, beschleunigten Kommunikationsraten und leistungsfähigeren Rechenmethoden zu führen. Mit dieser Arbeit untersuchen wir solche Anwendungen. Wir erforschen Entwürfe für verschränkungsgestützte Kommunikationssysteme und verteilte Quantencomputer. Anschließend entwickeln wir Simulationssoftware zur Validierung der Designs und zur Entwicklung neuartiger Anwendungen für Quantennetzwerke.

Binomial negative regression is able to handle poisson regression problem with underdispersion assumption. When the data has hierarchy and level that need to be calculated, regression is no longer appropriate to solve this problem, therefore binomial negative regression is used. To solve multilevel binomial negative regression modeling, several steps need to be fulfill: poisson assumption test and underdispersion assumption test, parameter estimation with expectation-maximization algorithm, variance components estimation, feasibility test with likelihood ratio test, significance parameter test with wald test and determining the best model. This research modeled the numbers of neonatal death in district as cluster 1 and small public health center as cluster 2, in the correlation with the number of visit on trimester 1 and 3, number of pregnant mother who have Tetanus Diphtheria vaccination, assumed number of neonatal babies with complication disease, numbers of babies who got Hepatitis B vaccination less than 24 hour, numbers of babies who got BCG vaccination and also number of visit neonatal 1 and 3. The result shows that number of neonatal death is only affected by number of babies who had Hepatitis B vaccination less than 24 hour

Secure multi-party computation (SMPC) protocols allow several parties that distrust each other to collectively compute a function on their inputs. In this paper, we introduce a protocol that lifts classical SMPC to quantum SMPC in a composably and statistically secure way, even for a single honest party. Unlike previous quantum SMPC protocols, our proposal only requires very limited quantum resources from all but one party; it suffices that the weak parties, i.e. the clients, are able to prepare single-qubit states in the X-Y plane. The novel quantum SMPC protocol is constructed in a naturally modular way, and relies on a new technique for quantum verification that is of independent interest. This verification technique requires the remote preparation of states only in a single plane of the Bloch sphere. In the course of proving the security of the new verification protocol, we also uncover a fundamental invariance that is inherent to measurement-based quantum computing., Comment: 27+10 pages, 5 figures. This work supersedes arXiv:2102.12949

We show the security of multi-user key establishment on a single line of quantum communication. More precisely, we consider a quantum communication architecture where the qubit generation and measurement happen at the two ends of the line, whilst intermediate parties are limited to single-qubit unitary transforms. This network topology has been previously introduced to implement quantum-assisted secret-sharing protocols for classical data, as well as the key establishment, and secure computing. This architecture has numerous advantages. The intermediate nodes are only using simplified hardware, which makes them easier to implement. Moreover, key establishment between arbitrary pairs of parties in the network does not require key routing through intermediate nodes. This is in contrast with quantum key distribution (QKD) networks for which non-adjacent nodes need intermediate ones to route keys, thereby revealing these keys to intermediate parties and consuming previously established ones to secure the routing process. Our main result is to show the security of key establishment on quantum line networks. We show the security using the framework of abstract cryptography. This immediately makes the security composable, showing that the keys can be used for encryption or other tasks., Comment: 23 pages, 4 figures, comments are welcome

Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand, fully supervised learning models are first trained usually over a few thousand domains with known solutions (i.e., labeled data) and then predict the solution over a few hundred unseen domains. Physics-informed PointNet (PIPN) is primarily designed to fill this gap between PINNs (as weakly supervised learning models) and fully supervised learning models. In this article, we demonstrate that PIPN predicts the solution of desired partial differential equations over a few hundred domains simultaneously, while it only uses sparse labeled data. This framework benefits fast geometric designs in the industry when only sparse labeled data are available. Particularly, we show that PIPN predicts the solution of a plane stress problem over more than 500 domains with different geometries, simultaneously. Moreover, we pioneer implementing the concept of remarkable batch size (i.e., the number of geometries fed into PIPN at each sub-epoch) into PIPN. Specifically, we try batch sizes of 7, 14, 19, 38, 76, and 133. Additionally, the effect of the PIPN size, symmetric function in the PIPN architecture, and static and dynamic weights for the component of the sparse labeled data in the loss function are investigated.

ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numerical algorithms. Specifically, we examine the capability of GhatGPT for generating codes for numerical algorithms in different programming languages, for debugging and improving written codes by users, for completing missed parts of numerical codes, rewriting available codes in other programming languages, and for parallelizing serial codes. Additionally, we assess if ChatGPT can recognize if given codes are written by humans or machines. To reach this goal, we consider a variety of mathematical problems such as the Poisson equation, the diffusion equation, the incompressible Navier-Stokes equations, compressible inviscid flow, eigenvalue problems, solving linear systems of equations, storing sparse matrices, etc. Furthermore, we exemplify scientific machine learning such as physics-informed neural networks and convolutional neural networks with applications to computational physics. Through these examples, we investigate the successes, failures, and challenges of ChatGPT. Examples of failures are producing singular matrices, operations on arrays with incompatible sizes, programming interruption for relatively long codes, etc. Our outcomes suggest that ChatGPT can successfully program numerical algorithms in different programming languages, but certain limitations and challenges exist that require further improvement of this machine learning model.

Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over convolution neural networks in fundamental computer vision tasks. However, the scientific community has not fully grasped the inner workings of vision transformers, nor the basis for their decision-making, which underscores the importance of explainability methods. Understanding how these models arrive at their decisions not only improves their performance but also builds trust in AI systems. This study explores different explainability methods proposed for visual transformers and presents a taxonomy for organizing them according to their motivations, structures, and application scenarios. In addition, it provides a comprehensive review of evaluation criteria that can be used for comparing explanation results, as well as explainability tools and frameworks. Finally, the paper highlights essential but unexplored aspects that can enhance the explainability of visual transformers, and promising research directions are suggested for future investment., Comment: 20 pages,5 figures

Differential privacy is a widely used notion of security that enables the processing of sensitive information. In short, differentially private algorithms map "neighbouring" inputs to close output distributions. Prior work proposed several quantum extensions of differential privacy, each of them built on substantially different notions of neighbouring quantum states. In this paper, we propose a novel and general definition of neighbouring quantum states. We demonstrate that this definition captures the underlying structure of quantum encodings and can be used to provide exponentially tighter privacy guarantees for quantum measurements. Our approach combines the addition of classical and quantum noise and is motivated by the noisy nature of near-term quantum devices. Moreover, we also investigate an alternative setting where we are provided with multiple copies of the input state. In this case, differential privacy can be ensured with little loss in accuracy combining concentration of measure and noise-adding mechanisms. En route, we prove the advanced joint convexity of the quantum hockey-stick divergence and we demonstrate how this result can be applied to quantum differential privacy. Finally, we complement our theoretical findings with an empirical estimation of the certified adversarial robustness ensured by differentially private measurements., Comment: This articles supersedes arXiv:2203.03604

In this work we develop new methods for the characterisation of continuous variable quantum states using heterodyne measurement in both the trusted and untrusted settings. First, building on quantum state tomography with heterodyne detection, we introduce a reliable method for continuous variable quantum state certification, which directly yields the elements of the density matrix of the state considered with analytical confidence intervals. This method neither needs mathematical reconstruction of the data nor discrete binning of the sample space and uses a single Gaussian measurement setting. Second, beyond quantum state tomography and without its identical copies assumption, we promote our reliable tomography method to a general efficient protocol for verifying continuous variable pure quantum states with Gaussian measurements against fully malicious adversaries, i.e., making no assumptions whatsoever on the state generated by the adversary. These results are obtained using a new analytical estimator for the expected value of any operator acting on a continuous variable quantum state with bounded support over the Fock basis, computed with samples from heterodyne detection of the state.

Quantum network protocols offer new functionalities such as enhanced security to communication and computational systems. Despite the rapid progress in quantum hardware, it has not yet reached a level of maturity that enables execution of many quantum protocols in practical settings. To develop quantum protocols in real world, it is necessary to examine their performance considering the imperfections in their practical implementation using simulation platforms. In this paper, we consider several quantum protocols that enable promising functionalities and services in near-future quantum networks. The protocols are chosen from both areas of quantum communication and quantum computation as follows: quantum money, W-state based anonymous transmission, verifiable blind quantum computation, and quantum digital signature. We use NetSquid simulation platform to evaluate the effect of various sources of noise on the performance of these protocols, considering different figures of merit. We find that to enable quantum money protocol, the decoherence time constant of the quantum memory must be at least three times the storage time of qubits. Furthermore, our simulation results for the w-state based anonymous transmission protocol show that to achieve an average fidelity above 0.8 in this protocol, the storage time of sender’s and receiver’s particles in the quantum memory must be less than half of the decoherence time constant of the quantum memory. We have also investigated the effect of gate imperfections on the performance of verifiable blind quantum computation. We find that with our chosen parameters, if the depolarizing probability of quantum gates is equal to or greater than 0.05, the security of the protocol cannot be guaranteed. Lastly, our simulation results for quantum digital signature protocol show that channel loss has a significant effect on the probability of repudiation., QID/Wehner Group, QuTech

In this work, novel proton-exchange membranes (PEMs) based on sulfonated poly(ether ether ketone) (SPEEK) and two-dimensional (2D) sulfonated niobium disulphide (S-NbS2) nanoflakes are synthesized by a solution-casting method and used in vanadium redox flow batteries (VRFBs). The NbS2 nanoflakes are produced by liquid-phase exfoliation of their bulk counterpart and chemically functionalized with terminal sulfonate groups to improve dimensional and chemical stabilities, proton conductivity (sigma) and fuel barrier properties of the as-produced membranes. The addition of S-NbS2 nanoflakes to SPEEK decreases the vanadium ion permeability from 5.42 x 10(-7) to 2.34 x 10(-7) cm(2) min(-1). Meanwhile, it increases the membrane sigma and selectivity up to 94.35 mS cm(-2) and 40.32 x 10(4) S min cm(-3), respectively. The cell assembled with the optimized membrane incorporating 2.5 wt% of S-NbS2 nanoflakes (SPEEK:2.5% S-NbS2) exhibits high efficiency metrics, i.e., coulombic efficiency between 98.7 and 99.0%, voltage efficiency between 90.2 and 73.2% and energy efficiency between 89.3 and 72.8% within the current density range of 100-300 mA cm(-2), delivering a maximum power density of 0.83 W cm(-2) at a current density of 870 mA cm(-2). The SPEEK:2.5% S-NbS2 membrane-based VRFBs show a stable behavior over 200 cycles at 200 mA cm(-2). This study opens up an effective avenue for the production of advanced SPEEK-based membranes for VRFBs.

In this work, novel proton-exchange membranes (PEMs) based on sulfonated poly(ether ether ketone) (SPEEK) and two-dimensional (2D) sulfonated niobium disulphide (S-NbS2) nanoflakes are synthesized by a solution-casting method and used in vanadium redox flow batteries (VRFBs). The NbS2 nanoflakes are produced by liquid-phase exfoliation of their bulk counterpart and chemically functionalized with terminal sulfonate groups to improve dimensional and chemical stabilities, proton conductivity (sigma) and fuel barrier properties of the as-produced membranes. The addition of S-NbS2 nanoflakes to SPEEK decreases the vanadium ion permeability from 5.42 x 10(-7) to 2.34 x 10(-7) cm(2) min(-1). Meanwhile, it increases the membrane sigma and selectivity up to 94.35 mS cm(-2) and 40.32 x 10(4) S min cm(-3), respectively. The cell assembled with the optimized membrane incorporating 2.5 wt% of S-NbS2 nanoflakes (SPEEK:2.5% S-NbS2) exhibits high efficiency metrics, i.e., coulombic efficiency between 98.7 and 99.0%, voltage efficiency between 90.2 and 73.2% and energy efficiency between 89.3 and 72.8% within the current density range of 100-300 mA cm(-2), delivering a maximum power density of 0.83 W cm(-2) at a current density of 870 mA cm(-2). The SPEEK:2.5% S-NbS2 membrane-based VRFBs show a stable behavior over 200 cycles at 200 mA cm(-2). This study opens up an effective avenue for the production of advanced SPEEK-based membranes for VRFBs.

In this work, novel proton-exchange membranes (PEMs) based on sulfonated poly(ether ether ketone) (SPEEK) and two-dimensional (2D) sulfonated niobium disulphide (S-NbS2) nanoflakes are synthesized by a solution-casting method and used in vanadium redox flow batteries (VRFBs). The NbS2 nanoflakes are produced by liquid-phase exfoliation of their bulk counterpart and chemically functionalized with terminal sulfonate groups to improve dimensional and chemical stabilities, proton conductivity (sigma) and fuel barrier properties of the as-produced membranes. The addition of S-NbS2 nanoflakes to SPEEK decreases the vanadium ion permeability from 5.42 x 10(-7) to 2.34 x 10(-7) cm(2) min(-1). Meanwhile, it increases the membrane sigma and selectivity up to 94.35 mS cm(-2) and 40.32 x 10(4) S min cm(-3), respectively. The cell assembled with the optimized membrane incorporating 2.5 wt% of S-NbS2 nanoflakes (SPEEK:2.5% S-NbS2) exhibits high efficiency metrics, i.e., coulombic efficiency between 98.7 and 99.0%, voltage efficiency between 90.2 and 73.2% and energy efficiency between 89.3 and 72.8% within the current density range of 100-300 mA cm(-2), delivering a maximum power density of 0.83 W cm(-2) at a current density of 870 mA cm(-2). The SPEEK:2.5% S-NbS2 membrane-based VRFBs show a stable behavior over 200 cycles at 200 mA cm(-2). This study opens up an effective avenue for the production of advanced SPEEK-based membranes for VRFBs.

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Making more localized prediction (e.g., words or phrases), however, is relatively harder because the role of smaller text units depends on the context in which they are used (e.g., sentences or paragraphs), and training a supervised model to take the context into account, requires labeled training corpora, which is not available in many problem domains. Often, the global prediction at a greater context may be informative for a more localized prediction at a smaller semantic unit. However, directly inferring the most salient local features from the corresponding, easier to learn, global prediction may overlook the semantics of this relationship. This thesis argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes a more robust and accurate inference scheme, and shows how it can be implemented as a transfer function that rewrites a greater context from one class to another. We study the generalizability of the proposed framework to problem domains with varying data availability profiles and different levels of inference granularity. We demonstrate the robustness of the contrapositive inference to the noisy data and how data augmentation can facilitate the generation of weakly-labeled training data for resource-constrained problem domains. We discuss the transferability and adaptability of the contrapositive relationship to the problem domains with limited amount of training data. In addition, we show the robustness of the contrapositive inference scheme to variability in the size of the local and global contexts: from paragraphs to sentences, and from sentences to words and phrases.

In this work, novel proton-exchange membranes (PEMs) based on sulfonated poly(ether ether ketone) (SPEEK) and two-dimensional (2D) sulfonated niobium disulphide (S-NbS2) nanoflakes are synthesized by a solution-casting method and used in vanadium redox flow batteries (VRFBs). The NbS2 nanoflakes are produced by liquid-phase exfoliation of their bulk counterpart and chemically functionalized with terminal sulfonate groups to improve dimensional and chemical stabilities, proton conductivity (sigma) and fuel barrier properties of the as-produced membranes. The addition of S-NbS2 nanoflakes to SPEEK decreases the vanadium ion permeability from 5.42 x 10(-7) to 2.34 x 10(-7) cm(2) min(-1). Meanwhile, it increases the membrane sigma and selectivity up to 94.35 mS cm(-2) and 40.32 x 10(4) S min cm(-3), respectively. The cell assembled with the optimized membrane incorporating 2.5 wt% of S-NbS2 nanoflakes (SPEEK:2.5% S-NbS2) exhibits high efficiency metrics, i.e., coulombic efficiency between 98.7 and 99.0%, voltage efficiency between 90.2 and 73.2% and energy efficiency between 89.3 and 72.8% within the current density range of 100-300 mA cm(-2), delivering a maximum power density of 0.83 W cm(-2) at a current density of 870 mA cm(-2). The SPEEK:2.5% S-NbS2 membrane-based VRFBs show a stable behavior over 200 cycles at 200 mA cm(-2). This study opens up an effective avenue for the production of advanced SPEEK-based membranes for VRFBs.

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Making more localized prediction (e.g., words or phrases), however, is relatively harder because the role of smaller text units depends on the context in which they are used (e.g., sentences or paragraphs), and training a supervised model to take the context into account, requires labeled training corpora, which is not available in many problem domains. Often, the global prediction at a greater context may be informative for a more localized prediction at a smaller semantic unit. However, directly inferring the most salient local features from the corresponding, easier to learn, global prediction may overlook the semantics of this relationship. This thesis argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes a more robust and accurate inference scheme, and shows how it can be implemented as a transfer function that rewrites a greater context from one class to another. We study the generalizability of the proposed framework to problem domains with varying data availability profiles and different levels of inference granularity. We demonstrate the robustness of the contrapositive inference to the noisy data and how data augmentation can facilitate the generation of weakly-labeled training data for resource-constrained problem domains. We discuss the transferability and adaptability of the contrapositive relationship to the problem domains with limited amount of training data. In addition, we show the robustness of the contrapositive inference scheme to variability in the size of the local and global contexts: from paragraphs to sentences, and from sentences to words and phrases.

Differential privacy provides a theoretical framework for processing a dataset about $n$ users, in a way that the output reveals a minimal information about any single user. Such notion of privacy is usually ensured by noise-adding mechanisms and amplified by several processes, including subsampling, shuffling, iteration, mixing and diffusion. In this work, we provide privacy amplification bounds for quantum and quantum-inspired algorithms. In particular, we show for the first time, that algorithms running on quantum encoding of a classical dataset or the outcomes of quantum-inspired classical sampling, amplify differential privacy. Moreover, we prove that a quantum version of differential privacy is amplified by the composition of quantum channels, provided that they satisfy some mixing conditions., Comment: This article is superseded by arXiv:2307.04733

Quantum statistical queries provide a theoretical framework for investigating the computational power of a learner with limited quantum resources. This model is particularly relevant in the current context, where available quantum devices are subject to severe noise and have limited quantum memory. On the other hand, the framework of quantum differential privacy demonstrates that noise can, in some cases, benefit the computation, enhancing robustness and statistical security. In this work, we establish an equivalence between quantum statistical queries and quantum differential privacy in the local model, extending a celebrated classical result to the quantum setting. Furthermore, we derive strong data processing inequalities for the quantum relative entropy under local differential privacy and apply this result to the task of asymmetric hypothesis testing with restricted measurements. Finally, we consider the task of quantum multi-party computation under local differential privacy. As a proof of principle, we demonstrate that the parity function is efficiently learnable in this model, whereas the corresponding classical task requires exponentially many samples., Comment: This version significantly extends the previous one with new entropic inequalities under local privacy, a private version of the quantum Stein's Lemma and an application to private multi-party quantum computation

Analyzing how humans revise their writings is an interesting research question, not only from an educational perspective but also in terms of artificial intelligence. Better understanding of this process could facilitate many NLP applications, from intelligent tutoring systems to supportive and collaborative writing environments. Developing these applications, however, requires revision corpora, which are not widely available. In this work, we present ArgRewrite V.2, a corpus of annotated argumentative revisions, collected from two cycles of revisions to argumentative essays about self-driving cars. Annotations are provided at different levels of purpose granularity (coarse and fine) and scope (sentential and subsentential). In addition, the corpus includes the revision goal given to each writer, essay scores, annotation verification, pre- and post-study surveys collected from participants as meta-data. The variety of revision unit scope and purpose granularity levels in ArgRewrite, along with the inclusion of new types of meta-data, can make it a useful resource for research and applications that involve revision analysis. We demonstrate some potential applications of ArgRewrite V.2 in the development of automatic revision purpose predictors, as a training source and benchmark., Comment: Lang Resources & Evaluation (2022)

Quantum network protocols offer new functionalities such as enhanced security to communication and computational systems. Despite the rapid progress in quantum hardware, it has not yet reached a level of maturity that enables execution of many quantum protocols in practical settings. To develop quantum protocols in real world, it is necessary to examine their performance considering the imperfections in their practical implementation using simulation platforms. In this paper, we consider several quantum protocols that enable promising functionalities and services in near-future quantum networks. The protocols are chosen from both areas of quantum communication and quantum computation as follows: quantum money, W-state based anonymous transmission, verifiable blind quantum computation, and quantum digital signature. We use NetSquid simulation platform to evaluate the effect of various sources of noise on the performance of these protocols, considering different figures of merit. We find that to enable quantum money protocol, the decoherence time constant of the quantum memory must be at least three times the storage time of qubits. Furthermore, our simulation results for the w-state based anonymous transmission protocol show that to achieve an average fidelity above 0.8 in this protocol, the storage time of sender’s and receiver’s particles in the quantum memory must be less than half of the decoherence time constant of the quantum memory. We have also investigated the effect of gate imperfections on the performance of verifiable blind quantum computation. We find that with our chosen parameters, if the depolarizing probability of quantum gates is equal to or greater than 0.05, the security of the protocol cannot be guaranteed. Lastly, our simulation results for quantum digital signature protocol show that channel loss has a significant effect on the probability of repudiation., QID/Wehner Group

In this work, novel proton-exchange membranes (PEMs) based on sulfonated poly(ether ether ketone) (SPEEK) and two-dimensional (2D) sulfonated niobium disulphide (S-NbS2) nanoflakes are synthesized by a solution-casting method and used in vanadium redox flow batteries (VRFBs). The NbS2 nanoflakes are produced by liquid-phase exfoliation of their bulk counterpart and chemically functionalized with terminal sulfonate groups to improve dimensional and chemical stabilities, proton conductivity (sigma) and fuel barrier properties of the as-produced membranes. The addition of S-NbS2 nanoflakes to SPEEK decreases the vanadium ion permeability from 5.42 x 10(-7) to 2.34 x 10(-7) cm(2) min(-1). Meanwhile, it increases the membrane sigma and selectivity up to 94.35 mS cm(-2) and 40.32 x 10(4) S min cm(-3), respectively. The cell assembled with the optimized membrane incorporating 2.5 wt% of S-NbS2 nanoflakes (SPEEK:2.5% S-NbS2) exhibits high efficiency metrics, i.e., coulombic efficiency between 98.7 and 99.0%, voltage efficiency between 90.2 and 73.2% and energy efficiency between 89.3 and 72.8% within the current density range of 100-300 mA cm(-2), delivering a maximum power density of 0.83 W cm(-2) at a current density of 870 mA cm(-2). The SPEEK:2.5% S-NbS2 membrane-based VRFBs show a stable behavior over 200 cycles at 200 mA cm(-2). This study opens up an effective avenue for the production of advanced SPEEK-based membranes for VRFBs.

RÉSUMÉ: Récemment, on a assisté à une montée en puissance des techniques d'apprentissage automatique dans le domaine de l'optimisation. Les méthodes d'apprentissage supervisé peuvent exploiter des structures sous-jacentes dans des multitudes de problèmes et utiliser cette expertise pour aider à trouver plus efficacement la solution de nouvelles instances de ces problèmes. Dans l'apprentissage par renforcement (Reinforcement Learning, RL), cependant, un agent interagit avec l'environnement contrôlé par les contraintes d'un problème d'optimisation. Une fonction de récompense qui s'aligne sur la fonction objectif du problème d'optimisation conduit le processus de recherche itératif à prendre de meilleures décisions pour atteindre la politique optimale. Comme les problèmes d'optimisation combinatoire peuvent généralement être formulés comme des problèmes de prise de décision séquentielle, ils peuvent être modélisés comme des processus de décision de Markov (Markov Decision Process, MDP). L'utilisation de méthodes de recherche exhaustive peut devenir prohibitive pour ces problèmes, c'est pourquoi des méthodes d'apprentissage par renforcement peuvent être utilisées pour les résoudre. En raison de la nature combinatoire de ces problèmes, dans de nombreux cas, les algorithmes de programmation en nombres entiers standard des logiciels de programmation mathématique commerciaux ne peuvent pas résoudre efficacement les modèles correspondants. D'un autre point de vue, de nombreux problèmes issus de cas pratiques réels sont de nature online, ce qui signifie que certaines informations ne sont pas disponibles au départ et qu'elles seront révélées au fil du temps. Dans ce cas, l'information des paramètres stochastiques n'est pas connue à l'avance. L'utilisation de la programmation linéaire mixte en nombres entiers pour ce type de problèmes serait coûteuse en termes de calculs. Néanmoins, les méthodes d'apprentissage par renforcement fonctionnent également très bien pour ces problème

Blind delegation protocols allow a client to delegate a computation to a server so that the server learns nothing about the input to the computation apart from its size. For the specific case of quantum computation we know, from work over the past decade, that blind delegation protocols can achieve information-theoretic security (provided the client and the server exchange some amount of quantum information). In this paper we prove, provided certain complexity-theoretic conjectures are true, that the power of information-theoretically secure blind delegation protocols for quantum computation (ITS-BQC protocols) is in a number of ways constrained. In the first part of our paper we provide some indication that ITS-BQC protocols for delegating polynomial-time quantum computations in which the client and the server interact only classically are unlikely to exist. We first show that having such a protocol in which the client and the server exchange O(n^d) bits of communication, implies that BQP subset MA/O(n^d). We conjecture that this containment is unlikely by proving that there exists an oracle relative to which BQP not subset MA/O(n^d). We then show that if an ITS-BQC protocol exists in which the client and the server interact only classically and which allows the client to delegate quantum sampling problems to the server (such as BosonSampling) then there exist non-uniform circuits of size 2^{n - Omega(n/log(n))}, making polynomially-sized queries to an NP^{NP} oracle, for computing the permanent of an n x n matrix. The second part of our paper concerns ITS-BQC protocols in which the client and the server engage in one round of quantum communication and then exchange polynomially many classical messages. First, we provide a complexity-theoretic upper bound on the types of functions that could be delegated in such a protocol by showing that they must be contained in QCMA/qpoly cap coQCMA/qpoly. Then, we show that having such a protocol for delegating NP-hard function

Tree of heaven (Ailanthus altissima) is a fast-growing deciduous tree native to China, considered a serious invasive species worldwide, with several socio-economic and ecological impacts attributed to it. Chemical and mechanical methods have limited efficacy in its management, and biological controls may offer a suitable and sustainable option. Aculus mosoniensis (Ripka) is an eriophyid mite that has been recorded to attack tree of heaven in 13 European countries. This study aims to explore the host range of this mite by exposing 13 plant species, selected either for their phylogenetic and ecological similarity to the target weed or their economic importance. Shortly after inoculation with the mite, we recorded a quick decrease in mite number on all nontarget species and no sign of mite reproduction. Whereas, after just one month, the population of mites on tree of heaven numbered in the thousands, irrespective of the starting population, and included both adults and juveniles. Significantly, we observed evidence of damage due to the mite only on target plants. Due to the specificity, strong impact on the target, and the ability to increase its population to high levels in a relatively short amount of time, we find A. mosoniensis to be a very promising candidate for the biological control of tree of heaven.

Due to the highly dynamic nature of Wireless Sensor Networks (WSN), Software-Defined Network (SDN) is a promising technology to ease network management by providing a logically centralized control plane. It is a common approach to employ multiple SDN controllers to form a physically distributed SDN to achieve better fault tolerance, boost scalability, and enhance performance. Despite physical distribution, since the notion behind SDN is to logically centralize network management, it is essential to provide a consistent view of the network's state for all controllers. Deploying multiple controllers result in higher synchronization and deployment cost. Since network performance and inter-controller synchronization cost seem to be contradicting objectives, it is a research challenge to choose the best placement of SDN controllers to optimize both the performance and synchronization cost of an SDN-enabled WSN simultaneously. In this paper, we first formulate the controller placement problem as a multi-objective optimization problem. In this regard, multiple constraints are considered, including reliability, fault tolerance, performance in terms of latency, synchronization overhead, and deployment cost. Moreover, we leverage the Cuckoo optimization algorithm, a nature-inspired population-based meta-heuristic algorithm to solve the optimization problem. This algorithm seeks to find the global optimum by imitating brood parasitism of some cuckoo species. Finally, to evaluate our proposed method, we compare it against several existing methods in the literature. The experiments reveal that our proposed method considerably outperforms existing methods, namely Simulated Annealing (SA) and Quantum Annealing (QA), in terms of both performance and synchronization cost. Additionally, our proposed algorithm, in contrast to Integer Linear Programming (ILP), is considerably more scalable, which makes it applicable for large-scale WSNs., QC 20210329

The Variational Quantum Cloning algorithm uses machine learning to obtain through optimization a circuit capable of generating approximate clones from unknown input quantum states. We adapt this algorithm to a boson sampling photonic platform formed exclusively by phase-shifters and beam-splitters, which is not universal but it still allows us to achieve quantum advantage. We have been able to obtain results through simulations reaching optimal fidelities for 1 to 2 universal cloning and also 1 to 2 and 1 to 3 phase-covariant cloning. This project demonstrates the capabilities of this type of photonic platforms and it can be used as a reference to adapt other variational algorithms to them., El algoritmo de clonación cuántica variacional hace uso de aprendizaje automático para obtener a través de optimización un circuito capaz de generar clones aproximados desde estados cuánticos de entrada desconocidos. Adaptamos este algoritmo a una plataforma fotónica de muestreo de bosones formada por divisores de haz y cambiadores de fase, la cual no es universal pero permite alcanzar ventaja cuántica. Se han obtenido resultados a través de simulaciones alcanzando fidelidades óptimas para clonación universal 1 a 2 y también para clonación de fase covariante 1 a 2 y 1 a 3. Este proyecto demuestra las capacidades de este tipo de plataformas fotónicas y puede ser utilizado como referencia para adaptar otros algoritmos variacionales en ellas., L'algorisme de clonació quàntica variacional fa servir aprenentatge automàtic per obtenir a través d'optimizació un circuit capaç de generar clons aproximats des d'estats quàntics d'entrada desconeguts. Adaptem aquest algorisme a una plataforma fotònica de mostreig de bosons formada per divisors de feix i canviadors de fase, la qual no és universal però ens permet assolir avantatge quàntic. S'han obtingut resultats a través de simulacions assolint fidelitats òptimes per a clonació universal 1 a 2 i també per a clonació de fase covariant 1 a 2 i 1 a 3. Aquest projecte demostra les capacitats d'aquest tipus de plataformes fotòniques i pot ser utilitzat com a referència per adaptar-hi altres algorismes variacionals.

Information-theoretic lower bounds are often encountered in several branches of computer science, including learning theory and cryptography. In the quantum setting, Holevo's and Nayak's bounds give an estimate of the amount of classical information that can be stored in a quantum state. Previous works have shown how to combine information-theoretic tools with a counting argument to lower bound the sample complexity of distribution-free quantum probably approximately correct (PAC) learning. In our work, we establish the notion of Probably Approximately Correct Source Coding and we show two novel applications in quantum learning theory and delegated quantum computation with a purely classical client. In particular, we provide a lower bound of the sample complexity of a quantum learner for arbitrary functions under the Zipf distribution, and we improve the security guarantees of a classically-driven delegation protocol for measurement-based quantum computation (MBQC)., Comment: 13 pages, 1 figure

Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term. In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving the MaxCut problem. Specifically, we address two problems in the QAOA, how to initialise the algorithm, and how to subsequently train the parameters to find an optimal solution. For the former, we propose graph neural networks (GNNs) as a warm-starting technique for QAOA. We demonstrate that merging GNNs with QAOA can outperform both approaches individually. Furthermore, we demonstrate how graph neural networks enables warm-start generalisation across not only graph instances, but also to increasing graph sizes, a feature not straightforwardly available to other warm-starting methods. For training the QAOA, we test several optimisers for the MaxCut problem up to 16 qubits and benchmark against vanilla gradient descent. These include quantum aware/agnostic and machine learning based/neural optimisers. Examples of the latter include reinforcement and meta-learning. With the incorporation of these initialisation and optimisation toolkits, we demonstrate how the optimisation problems can be solved using QAOA in an end-to-end differentiable pipeline., Comment: 12 pages, 8 Figures - publised version

This paper, for the first time, addresses the questions related to the connections between the quantum pseudorandomness and quantum hardware assumptions, specifically quantum physical unclonable functions (qPUFs). Our results show that the efficient pseudorandom quantum states (PRS) are sufficient to construct the challenge set for the universally unforgeable qPUF, improving the previous existing constructions that are based on the Haar-random states. We also show that both the qPUFs and the quantum pseudorandom unitaries (PRUs) can be constructed from each other, providing new ways to obtain PRS from the hardware assumptions. Moreover, we provide a sufficient condition (in terms of the diamond norm) that a set of unitaries should have to be a PRU in order to construct a universally unforgeable qPUF, giving yet another novel insight into the properties of the PRUs. Later, as an application of our results, we show that the efficiency of an existing qPUF-based client-server identification protocol can be improved without losing the security requirements of the protocol., Comment: 33 pages, 4 figures

Physical unclonable functions(PUFs) provide a unique fingerprint to a physical entity by exploiting the inherent physical randomness. Gao et al. discussed the vulnerability of most current-day PUFs to sophisticated machine learning-based attacks. We address this problem by integrating classical PUFs and existing quantum communication technology. Specifically, this paper proposes a generic design of provably secure PUFs, called hybrid locked PUFs(HLPUFs), providing a practical solution for securing classical PUFs. An HLPUF uses a classical PUF(CPUF), and encodes the output into non-orthogonal quantum states to hide the outcomes of the underlying CPUF from any adversary. Here we introduce a quantum lock to protect the HLPUFs from any general adversaries. The indistinguishability property of the non-orthogonal quantum states, together with the quantum lockdown technique prevents the adversary from accessing the outcome of the CPUFs. Moreover, we show that by exploiting non-classical properties of quantum states, the HLPUF allows the server to reuse the challenge-response pairs for further client authentication. This result provides an efficient solution for running PUF-based client authentication for an extended period while maintaining a small-sized challenge-response pairs database on the server side. Later, we support our theoretical contributions by instantiating the HLPUFs design using accessible real-world CPUFs. We use the optimal classical machine-learning attacks to forge both the CPUFs and HLPUFs, and we certify the security gap in our numerical simulation for construction which is ready for implementation., Comment: 47 pages, 13 figures

Tree of heaven (Ailanthus altissima) is a fast-growing deciduous tree native to China, considered a serious invasive species worldwide, with several socio-economic and ecological impacts attributed to it. Chemical and mechanical methods have limited efficacy in its management, and biological controls may offer a suitable and sustainable option. Aculus mosoniensis (Ripka) is an eriophyid mite that has been recorded to attack tree of heaven in 13 European countries. This study aims to explore the host range of this mite by exposing 13 plant species, selected either for their phylogenetic and ecological similarity to the target weed or their economic importance. Shortly after inoculation with the mite, we recorded a quick decrease in mite number on all nontarget species and no sign of mite reproduction. Whereas, after just one month, the population of mites on tree of heaven numbered in the thousands, irrespective of the starting population, and included both adults and juveniles. Significantly, we observed evidence of damage due to the mite only on target plants. Due to the specificity, strong impact on the target, and the ability to increase its population to high levels in a relatively short amount of time, we find A. mosoniensis to be a very promising candidate for the biological control of tree of heaven.

We introduce a secure hardware device named a QEnclave that can secure the remote execution of quantum operations while only using classical controls. This device extends to quantum computing the classical concept of a secure enclave which isolates a computation from its environment to provide privacy and tamper-resistance. Remarkably, our QEnclave only performs single-qubit rotations, but can nevertheless be used to secure an arbitrary quantum computation even if the qubit source is controlled by an adversary. More precisely, attaching a QEnclave to a quantum computer, a remote client controlling the QEnclave can securely delegate its computation to the server solely using classical communication. We investigate the security of our QEnclave by modeling it as an ideal functionality named Remote State Rotation. We show that this resource, similar to previously introduced functionality of remote state preparation, allows blind delegated quantum computing with perfect security. Our proof relies on standard tools from delegated quantum computing. Working in the Abstract Cryptography framework, we show a construction of remote state preparation from remote state rotation preserving the security. An immediate consequence is the weakening of the requirements for blind delegated computation. While previous delegated protocols were relying on a client that can either generate or measure quantum states, we show that this same functionality can be achieved with a client that only transforms quantum states without generating or measuring them., Comment: 25 pages, 5 figures

We propose a novel deep learning framework for predicting permeability of porous media from their digital images. Unlike convolutional neural networks, instead of feeding the whole image volume as inputs to the network, we model the boundary between solid matrix and pore spaces as point clouds and feed them as inputs to a neural network based on the PointNet architecture. This approach overcomes the challenge of memory restriction of graphics processing units and its consequences on the choice of batch size, and convergence. Compared to convolutional neural networks, the proposed deep learning methodology provides freedom to select larger batch sizes, due to reducing significantly the size of network inputs. Specifically, we use the classification branch of PointNet and adjust it for a regression task. As a test case, two and three dimensional synthetic digital rock images are considered. We investigate the effect of different components of our neural network on its performance. We compare our deep learning strategy with a convolutional neural network from various perspectives, specifically for maximum possible batch size. We inspect the generalizability of our network by predicting the permeability of real-world rock samples as well as synthetic digital rocks that are statistically different from the samples used during training. The network predicts the permeability of digital rocks a few thousand times faster than a Lattice Boltzmann solver with a high level of prediction accuracy.

We present the design and evaluation of a web-based intelligent writing assistant that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span (sentence versus sub-sentence) of revision analysis and the level of feedback provided (none, binary, or detailed revision purpose categorization). We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions through a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes., Comment: 13 pages, The 2021 ACM CHI Virtual Conference on Human Factors in Computing Systems

We propose the first generalization of the famous Non-Interactive Zero-Knowledge (NIZK) proofs to quantum languages (NIZKoQS) and we provide a protocol to prove advanced properties on a received quantum state non-destructively and non-interactively (a single message being sent from the prover to the verifier). In our second orthogonal contribution, we improve the costly Remote State Preparation protocols [CCKW18,CCKW19,GV19] that can classically fake a quantum channel (this is at the heart of our NIZKoQS protocol) by showing how to create a multi-qubits state from a single superposition. Finally, we generalize these results to a multi-party setting and prove that multiple parties can anonymously distribute a GHZ state in such a way that only participants knowing a secret credential can share this state, which could have applications to quantum anonymous transmission, quantum secret sharing, quantum onion routing and more., Comment: 59 pages, added a precise definition & proof of NIZKoQS

In this paper, we continue the line of work initiated by Boneh and Zhandry at CRYPTO 2013 and EUROCRYPT 2013 in which they formally define the notion of unforgeability against quantum adversaries specifically, for classical message authentication codes and classical digital signatures schemes. We develop a general and parameterised quantum game-based security model unifying unforgeability for both classical and quantum constructions allowing us for the first time to present a complete quantum cryptanalysis framework for unforgeability. In particular, we prove how our definitions subsume previous ones while considering more fine-grained adversarial models, capturing the full spectrum of superposition attacks. The subtlety here resides in the characterisation of a forgery. We show that the strongest level of unforgeability, namely existential unforgeability, can only be achieved if only orthogonal to previously queried messages are considered to be forgeries. In particular, we present a non-trivial attack if any overlap between the forged message and previously queried ones is allowed. We further show that deterministic constructions can only achieve the weaker notion of unforgeability, that is selective unforgeability, against such restricted adversaries, but that selective unforgeability breaks if general quantum adversaries (capable of general superposition attacks) are considered. On the other hand, we show that PRF is sufficient for constructing a selective unforgeable classical primitive against full quantum adversaries. Moreover, we show similar positive results relying on Pseudorandom Unitaries (PRU) for quantum primitives. These results demonstrate the generality of our framework that could be applicable to other primitives beyond the cases analysed in this paper., Comment: 50 pages, 5 figures

This study focuses on the experiences of women wearing hijab in the Swedish labour market. Semi-structured interviews have been conducted with six veiled women who have managed to find their ways to the labour market in Sweden. The study shows that, although the hijab is an important part of a woman´s identity in terms of her religious belief and practice, it is still hard for a woman wearing hijab to be accepted in the Swedish labour market and the Swedish society as a whole. This type of prejudice leads to discrimination and prejudice and deteriorating psychosocial work environments for veiled women. Postcolonial and intersectional perspectives which have been used at the macro level constitute the main theoretical approaches in the study. The analysis has produced results which show that discrimination against veiled women is very extensive in the Swedish labour market and in the Swedish working life. It has also become apparent in the study that veiled women have minimal trust in the labor market due to their experiences of discrimination. A side outcome of the study is the veiled women’s feelings of not being welcomed by feminist groups in Swedish society. Based on the analysis, it has also emerged that women who wear headscarves in our interviews do not feel welcome in feminist groups because of the hijab.

In this work we develop new methods for the characterisation of continuous variable quantum states using heterodyne measurement in both the trusted and untrusted settings. First, building on quantum state tomography with heterodyne detection, we introduce a reliable method for continuous variable quantum state certification, which directly yields the elements of the density matrix of the state considered with analytical confidence intervals. This method neither needs mathematical reconstruction of the data nor discrete binning of the sample space and uses a single Gaussian measurement setting. Second, beyond quantum state tomography and without its identical copies assumption, we promote our reliable tomography method to a general efficient protocol for verifying continuous variable pure quantum states with Gaussian measurements against fully malicious adversaries, i.e., making no assumptions whatsoever on the state generated by the adversary. These results are obtained using a new analytical estimator for the expected value of any operator acting on a continuous variable quantum state with bounded support over the Fock basis, computed with samples from heterodyne detection of the state.

Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies. In this work, we investigate and compare the capabilities of quantum versus classical models for the task of generative modelling in machine learning. We use a real world financial dataset consisting of correlated currency pairs and compare two models in their ability to learn the resulting distribution - a restricted Boltzmann machine, and a quantum circuit Born machine. We provide extensive numerical results indicating that the simulated Born machine always at least matches the performance of the Boltzmann machine in this task, and demonstrates superior performance as the model scales. We perform experiments on both simulated and physical quantum chips using the Rigetti forest platform, and also are able to partially train the largest instance to date of a quantum circuit Born machine on quantum hardware. Finally, by studying the entanglement capacity of the training Born machines, we find that entanglement typically plays a role in the problem instances which demonstrate an advantage over the Boltzmann machine., Comment: 17 Pages, 19 Figures

The generation of certifiable randomness is one of the most promising applications of quantum technologies. Furthermore, the intrinsic non-locality of quantum correlations allow us to certify randomness in a device-independent way, i.e. one need not make assumptions about the devices used. Due to the work of Curchod et. al., a single entangled two-qubit pure state can be used to produce arbitrary amounts of certified randomness. However, the obtaining of this randomness is experimentally challenging as it requires a large number of measurements, both projective and general. Motivated by these difficulties in the device-independent setting, we instead consider the scenario of one-sided device independence where certain devices are trusted, and others not; a scenario motivated by asymmetric experimental set-ups such as ion-photon networks. We show how certain aspects of previous work can be adapted to this scenario and provide theoretical bounds on the amount of randomness which can be certified. Furthermore, we give a protocol for unbounded randomness certification in this scenario, and provide numerical results demonstrating the protocol in the ideal case. Finally, we numerically test the possibility of implementing this scheme on near-term quantum technologies, by considering the performance of the protocol on several physical platforms., Comment: 35 pages, 9 Figures. This is a pre-published extended version of a workshop edition which appeared in the proceedings of PC 2018 (EPTCS 273, 2018, pp. 14-26). The published version of this work is available below

Secure delegated quantum computing allows a computationally weak client to outsource an arbitrary quantum computation to an untrusted quantum server in a privacy-preserving manner. One of the promising candidates to achieve classical delegation of quantum computation is classical-client remote state preparation ($RSP_{CC}$), where a client remotely prepares a quantum state using a classical channel. However, the privacy loss incurred by employing $RSP_{CC}$ as a sub-module is unclear. In this work, we investigate this question using the Constructive Cryptography framework by Maurer and Renner (ICS'11). We first identify the goal of $RSP_{CC}$ as the construction of ideal RSP resources from classical channels and then reveal the security limitations of using $RSP_{CC}$. First, we uncover a fundamental relationship between constructing ideal RSP resources (from classical channels) and the task of cloning quantum states. Any classically constructed ideal RSP resource must leak to the server the full classical description (possibly in an encoded form) of the generated quantum state, even if we target computational security only. As a consequence, we find that the realization of common RSP resources, without weakening their guarantees drastically, is impossible due to the no-cloning theorem. Second, the above result does not rule out that a specific $RSP_{CC}$ protocol can replace the quantum channel at least in some contexts, such as the Universal Blind Quantum Computing (UBQC) protocol of Broadbent et al. (FOCS '09). However, we show that the resulting UBQC protocol cannot maintain its proven composable security as soon as $RSP_{CC}$ is used as a subroutine. Third, we show that replacing the quantum channel of the above UBQC protocol by the $RSP_{CC}$ protocol QFactory of Cojocaru et al. (Asiacrypt '19), preserves the weaker, game-based, security of UBQC., Comment: 40 pages, 12 figures

It is of folkloric belief that the security of classical cryptographic protocols is automatically broken if the Adversary is allowed to perform superposition queries and the honest players forced to perform actions coherently on quantum states. Another widely held intuition is that enforcing measurements on the exchanged messages is enough to protect protocols from these attacks. However, the reality is much more complex. Security models dealing with superposition attacks only consider unconditional security. Conversely, security models considering computational security assume that all supposedly classical messages are measured, which forbids by construction the analysis of superposition attacks. Boneh and Zhandry have started to study the quantum computational security for classical primitives in their seminal work at Crypto'13, but only in the single-party setting. To the best of our knowledge, an equivalent model in the multiparty setting is still missing. In this work, we propose the first computational security model considering superposition attacks for multiparty protocols. We show that our new security model is satisfiable by proving the security of the well-known One-Time-Pad protocol and give an attack on a variant of the equally reputable Yao Protocol for Secure Two-Party Computations. The post-mortem of this attack reveals the precise points of failure, yielding highly counter-intuitive results: Adding extra classical communication, which is harmless for classical security, can make the protocol become subject to superposition attacks. We use this newly imparted knowledge to construct the first concrete protocol for Secure Two-Party Computation that is resistant to superposition attacks. Our results show that there is no straightforward answer to provide for either the vulnerabilities of classical protocols to superposition attacks or the adapted countermeasures., Comment: 46 pages

Recently, major progress has been made towards the realisation of quantum internet to enable a broad range of classically intractable applications. These applications such as delegated quantum computation require running a secure identification protocol between a low-resource and a high-resource party to provide secure communication. In this work, we propose two identification protocols based on the emerging hardware secure solutions, the quantum Physical Unclonable Functions (qPUFs). The first protocol allows a low-resource party to prove its identity to a high-resource party and in the second protocol, it is vice-versa. Unlike existing identification protocols based on Quantum Read-out PUFs which rely on the security against a specific family of attacks, our protocols provide provable exponential security against any Quantum Polynomial-Time adversary with resource-efficient parties. We provide a comprehensive comparison between the two proposed protocols in terms of resources such as quantum memory and computing ability required in both parties as well as the communication overhead between them., Comment: 40 pages, 11 figures

Cryptanalysis on standard quantum cryptographic systems generally involves finding optimal adversarial attack strategies on the underlying protocols. The core principle of modelling quantum attacks in many cases reduces to the adversary's ability to clone unknown quantum states which facilitates the extraction of some meaningful secret information. Explicit optimal attack strategies typically require high computational resources due to large circuit depths or, in many cases, are unknown. In this work, we propose variational quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm which allows an adversary to obtain optimal (approximate) cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques. The algorithm contains operationally meaningful cost functions with theoretical guarantees, quantum circuit structure learning and gradient descent based optimisation. Our approach enables the end-to-end discovery of hardware efficient quantum circuits to clone specific families of quantum states, which in turn leads to an improvement in cloning fidelites when implemented on quantum hardware: the Rigetti Aspen chip. Finally, we connect these results to quantum cryptographic primitives, in particular quantum coin flipping. We derive attacks on two protocols as examples, based on quantum cloning and facilitated by VQC. As a result, our algorithm can improve near term attacks on these protocols, using approximate quantum cloning as a resource., Comment: 16 pages main text, 25 pages supplementary material, 20 figures. Comments welcome

We present a novel deep learning framework for flow field predictions in irregular domains when the solution is a function of the geometry of either the domain or objects inside the domain. Grid vertices in a computational fluid dynamics (CFD) domain are viewed as point clouds and used as inputs to a neural network based on the PointNet architecture, which learns an end-to-end mapping between spatial positions and CFD quantities. Using our approach, (i) the network inherits desirable features of unstructured meshes (e.g., fine and coarse point spacing near the object surface and in the far field, respectively), which minimizes network training cost; (ii) object geometry is accurately represented through vertices located on object boundaries, which maintains boundary smoothness and allows the network to detect small changes between geometries; and (iii) no data interpolation is utilized for creating training data; thus accuracy of the CFD data is preserved. None of these features are achievable by extant methods based on projecting scattered CFD data into Cartesian grids and then using regular convolutional neural networks. Incompressible laminar steady flow past a cylinder with various shapes for its cross section is considered. The mass and momentum of predicted fields are conserved. We test the generalizability of our network by predicting the flow around multiple objects as well as an airfoil, even though only single objects and no airfoils are observed during training. The network predicts the flow fields hundreds of times faster than our conventional CFD solver, while maintaining excellent to reasonable accuracy.