Entanglement is an important evidence that a quantum device can potentially solve problems intractable for classical computers. In this paper, we prepare connected graph states involving 8 to 16 qubits on ibmqx5, 16-qubit superconducting processor accessible via IBM cloud,using low-depth circuits. We demonstrate the prepared state fully entangled, i.e. inseparable with respect any fixed partition.

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of features with an adaptive explicit representation, achieve reconstruction times far superior to existing inverse methods. The representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while act as regularizer 3D reconstruction. NeAT framework is designed specifically...

A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient. Recently, Jin et al. \cite{jin2018q} proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP. In this paper, we adapt UCB-exploration bonus to infinite-horizon MDP discounted rewards \emph{without} accessing generative model. We show that the \textit{sample complexity of exploration} our bounded by...

Thanks to its convenience and cost-savings feature, cloud computing ushers a new era. Yet security privacy issues must not be neglected. Private set intersection (PSI) is useful important in many applications, such as document similarity, genetic paternity data mining. The server performs operations on two outsourced encrypted datasets of owners. In the existing protocols, however, owners cannot decide whether use all or part their compute intersection, nor can they specify whom compare...

Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This typically solved by sequential solution that applies demosaicing (DM), denoising (DN), super-resolution (SR) sequentially in fixed predefined pipeline (execution order tasks), DM→DN→SR. The most recent work on image processing focuses developing more sophisticated architectures to achieve higher quality. Little attention has been paid the design pipeline, it still not clear how...

Sensitive data would be encrypted before uploading to the cloud due privacy issue. However, how compare efficiently becomes a problem. Public Key Encryption with Equality Test (PKEET) provides an efficient way check whether two ciphertexts (of possibly different users) contain same message without decryption. As enhanced variant, Attribute-based (ABEET) flexible mechanism of authorization on equality test. Most existing ABEET schemes are only proved secure in random oracle model. Their...

Due to the massive growth of data and security concerns, patients would be encrypted outsourced cloud server for feature matching in various medical scenarios, such as personal health record systems, actuarial judgments diagnostic related groups. Public key encryption with equality test (PKEET) is a useful utility matching. Authorized tester could perform on without decrypting. Unfortunately, due limited terminology medicine, people within institutions may illegally use data, trying obtain...

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for multi-view inverse rendering. Through a combination of features with an adaptive explicit representation, achieve reconstruction times far superior to existing methods. The representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while act as regularizer 3D reconstruction. NeAT framework is designed...

In this paper, we propose a true random number generator (TRNG) exploiting jitter and the chaotic behavior in cross ring oscillators (CROs). We make further study of feedback architecture cross-connect XOR gates inverters to form an oscillator. The CRO utilizes totally digital logic circuits, gains high robust entropy rate, as can accumulate locally between adjacent stages. Two specific working modes which work consistent state free-running respectively are introduced analyzed both...

Multiplayer games, when the number of players exceeds two, present unique challenges that fundamentally distinguish them from extensively studied two-player zero-sum games. These arise non-uniqueness equilibria and risk agents performing highly suboptimally adopting equilibrium strategies. While a line recent works developed learning systems successfully achieving human-level or even superhuman performance in popular multiplayer games such as Mahjong, Poker, Diplomacy, two critical questions...

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where description length game as well complexity many existing learning algorithms scale exponentially with number agents. While recent works successfully address this under model tabular Markov Games, their mechanisms critically rely on states being finite and small, do not extend to practical scenarios enormous state spaces function approximation must be used approximate value functions or...

Reinforcement learning from Human Feedback (RLHF) learns preference signals, while standard Learning (RL) directly reward signals. Preferences arguably contain less information than rewards, which makes preference-based RL seemingly more difficult. This paper theoretically proves that, for a wide range of models, we can solve using existing algorithms and techniques reward-based RL, with small or no extra costs. Specifically, (1) preferences that are drawn probabilistic reduce the problem to...

Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This typically solved by sequential solution that applies demosaicing (DM), denoising (DN), super-resolution (SR) sequentially in fixed predefined pipeline (execution order tasks), DM$\to$DN$\to$SR. The most recent work on image processing focuses developing more sophisticated architectures to achieve higher quality. Little attention has been paid the design pipeline, it still not...

An ideal strategy in zero-sum games should not only grant the player an average reward no less than value of Nash equilibrium, but also exploit (adaptive) opponents when they are suboptimal. While most existing works Markov focus exclusively on former objective, it remains open whether we can achieve both objectives simultaneously. To address this problem, work studies no-regret learning with adversarial competing against best fixed policy hindsight. Along direction, present a new complete...

Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost. While the most recent generation of such methods made progress on (inference) times, very little been improving reconstruction (training) times. In this work, we present Adaptive Scene Tracing (NAScenT), first method based directly training a hybrid explicit-implicit representation. NAScenT uses hierarchical...

Tilt-series cryo-electron tomography is a powerful tool widely used in structural biology to study 3D structures of micro-organisms, macromolecular complexes, etc. Still, the reconstruction process remains an arduous task due several challenges: The missing-wedge acquisition, sample misalignment and motion, need large data, and, especially, low signal-to-noise ratio.Inspired by recently introduced neural representations, we propose adaptive learning-based representation density field...