Quantitative Robustness Analysis of Quantum Programs (Extended Version)

Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are currently cost-prohibitive. But while this reality means that quantum programs are almost certain to have errors, there as yet exists no principled means to reason about erroneous behavior. This paper attempts to fill this gap by developing a semantics for erroneous quantum while-programs, as well as a logic for reasoning about them. This logic permits proving a property we have identified, called $\epsilon$-robustness, which characterizes possible "distance" between an ideal program and an erroneous one. We have proved the logic sound, and showed its utility on several case studies, notably: (1) analyzing the robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum walk (QW); (2) demonstrating the (in)effectiveness of different error correction schemes on single-qubit errors; and (3) analyzing the robustness of a fault-tolerant version of QBF.Comment: 34 pages, LaTeX; v2: fixed typo

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Recently, Zhu et al. (PLDI 2020) have formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q#, and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.Comment: Codes are available at https://github.com/njuwfang/DifferentiableQP

Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning

Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinforcement learning (MBRL) but is limited to domain-specific or simulated data. In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of downstream visual control tasks. However, in-the-wild videos are complicated with various contextual factors, such as intricate backgrounds and textured appearance, which precludes a world model from extracting shared world knowledge to generalize better. To tackle this issue, we introduce Contextualized World Models (ContextWM) that explicitly model both the context and dynamics to overcome the complexity and diversity of in-the-wild videos and facilitate knowledge transfer between distinct scenes. Specifically, a contextualized extension of the latent dynamics model is elaborately realized by incorporating a context encoder to retain contextual information and empower the image decoder, which allows the latent dynamics model to concentrate on essential temporal variations. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample-efficiency of MBRL in various domains, including robotic manipulation, locomotion, and autonomous driving

Security Evaluation against Differential Cryptanalysis for Block Cipher Structures

Estimating immunity against differential and linear cryptanalysis is essential in designing secure block ciphers. A practical measure to achieve it is to find the minimal number of active S-boxes, or a lower bound for this minimal number. In this paper, we provide a general algorithm using integer programming, which not only can estimate a good lower bound of the minimal differential active S-boxes for various block cipher structures, but also provides an efficient way to select new structures with good properties against differential cryptanalysis. Experimental results for the Feistel, CAST256, SMS4, CLEFIA and Generalized Feistel structures indicate that bounds obtained by our algorithm are the tightest except for a few rounds of the SMS4 structure. Then, for the first time, bounds of the differential active S-boxes number for the MISTY1, Skipjack, MARS and Four-cell structures are illustrated with the application of our algorithm. Finally, our algorithm is used to find four new structures with good properties against differential cryptanalysis. Security evaluation against liner cryptanalysis can be processed with our algorithm similarly by considering dual structures

Automatic Search of Truncated Impossible Differentials for Word-Oriented Block Ciphers (Full Version)

Impossible differential cryptanalysis is a powerful technique to recover the secret key of block ciphers by exploiting the fact that in block ciphers specific input and output differences are not compatible. This paper introduces a novel tool to search truncated impossible differentials for word-oriented block ciphers with bijective Sboxes. Our tool generalizes the earlier $\mathcal{U}$-method and the UID-method. It allows to reduce the gap between the best impossible differentials found by these methods and the best known differentials found by ad hoc methods that rely on cryptanalytic insights. The time and space complexities of our tool in judging an $r$-round truncated impossible differential are about $O(c\cdot l^4\cdot r^4)$ and $O(c\u27\cdot l^2\cdot r^2)$ respectively, where $l$ is the number of words in the plaintext and $c$, $c\u27$ are constants depending on the machine and the block cipher. In order to demonstrate the strength of our tool, we show that it does not only allow to automatically rediscover the longest truncated impossible differentials of many word-oriented block ciphers, but also finds new results. It independently rediscovers all 72 known truncated impossible differentials on 9-round CLEFIA. In addition, finds new truncated impossible differentials for AES, ARIA, Camellia without FL and FL$^{-1}$ layers, E2, LBlock, MIBS and Piccolo. Although our tool does not improve the lengths of impossible differentials for existing block ciphers, it helps to close the gap between the best known results of previous tools and those of manual cryptanalysis

Solving High-Dimensional PDEs with Latent Spectral Models

Deep models have achieved impressive progress in solving partial differential equations (PDEs). A burgeoning paradigm is learning neural operators to approximate the input-output mappings of PDEs. While previous deep models have explored the multiscale architectures and various operator designs, they are limited to learning the operators as a whole in the coordinate space. In real physical science problems, PDEs are complex coupled equations with numerical solvers relying on discretization into high-dimensional coordinate space, which cannot be precisely approximated by a single operator nor efficiently learned due to the curse of dimensionality. We present Latent Spectral Models (LSM) toward an efficient and precise solver for high-dimensional PDEs. Going beyond the coordinate space, LSM enables an attention-based hierarchical projection network to reduce the high-dimensional data into a compact latent space in linear time. Inspired by classical spectral methods in numerical analysis, we design a neural spectral block to solve PDEs in the latent space that approximates complex input-output mappings via learning multiple basis operators, enjoying nice theoretical guarantees for convergence and approximation. Experimentally, LSM achieves consistent state-of-the-art and yields a relative error reduction of 11.5% averaged on seven benchmarks covering both solid and fluid physics

Identification of genes induced by salt stress from Medicago truncatula L. seedlings

In order to identify genes induced during the salt stress response in barrel medic (Medicago truncatula L) seedlings, a cDNA library by salt stress was constructed  by suppression subtractive hybridization (SSH). Total RNA from 15-day-old seedlings was used as a ‘driver’, and total RNA from seedlings induced by salt was used as a ‘tester’. One hundred and sixty nine clones identified as positive clones by reverse northern dot-blotting resulted in 75 uni-ESTs that comprised of 13 contigs  and 62 singletons. Basic Local Alignment Search Tool (BLAST) analysis of deduced protein sequences revealed that 35 expressed sequence tags (ESTs) had identity similar to proteins with known function, while 27 could not be annotated at all. Most of the known function sequences were homologous to genes involved in abiotic stress in plants. Among these protein, citrate synthase, ribulose- 1,5-bisphosphate carboxylase, chloroplast protein, phosphoenolpyruvate carboxylase and  chloroplast outer envelope protein are related to photosynthesis; DNA binding/transcription factor, putative AP2/EREBP transcription factor, Cab9 gene, photosystem II polypeptide and calcium-dependent protein kinase play a significant role in signal transduction and transcription regulation; and aldolase and sucrose synthase are interrelated to osmolyte synthesis. Moreover, 5 of the ESTs, similar to genes from other plant species and closely involved in salt stress were isolated from M. truncatula L. They are superoxide dimutase (SOD)-1, gene for copper/zinc superoxide dismutase, cysteine protease, Na+/H+ antiporter and salt overly sensitive 2 (SOS2). To further assess the expression level of salt-induced ESTs, real-time polymerase chain reaction (PCR) analysis was employed, and the result showed that these genes have significantly increased expression and probably play an important role in the response of plants to salt stress.Key words: Barrel medic (Medicago truncatula L.), suppression subtraction hybridization (SSH), reverse northern dot-blotting, salt stress, real-time polymerase chain reaction (PCR)