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Found 13 papers, 0 papers with code
Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU
In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers.
Boosting Gradient Ascent for Continuous DR-submodular Maximization
The fundamental idea of our boosting technique is to exploit non-oblivious search to derive a novel auxiliary function $F$, whose stationary points are excellent approximations to the global maximum of the original DR-submodular objective $f$.
Bandit Multi-linear DR-Submodular Maximization and Its Applications on Adversarial Submodular Bandits
Then we reduce submodular bandit with partition matroid constraint and bandit sequential monotone maximization to the online bandit learning of the monotone multi-linear DR-submodular functions, attaining $O(T^{2/3}\log T)$ of $(1-1/e)$-regret in both problems, which improve the existing results.
Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.
Quantum Multi-Armed Bandits and Stochastic Linear Bandits Enjoy Logarithmic Regrets
In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with $O(\mbox{poly}(\log T))$ regrets, exponentially improving the dependence in terms of $T$.
Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback
Our lower bound works even when the loss sequence is oblivious but the delay is non-oblivious.
New Distinguishers for Negation-Limited Weak Pseudorandom Functions
We show how to distinguish circuits with $\log k$ negations (a. k. a $k$-monotone functions) from uniformly random functions in $\exp\left(\tilde{O}\left(n^{1/3}k^{2/3}\right)\right)$ time using random samples.
Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
We study the online influence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds.
Network Inference and Influence Maximization from Samples
Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.
Optimization from Structured Samples for Coverage Functions
We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data.
Quantum Supremacy Circuit Simulation on Sunway TaihuLight
We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight.
Quantum Physics
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
In the guaranteed delivery scenario, ad exposures (which are also called impressions in some works) to users are guaranteed by contracts signed in advance between advertisers and publishers.
Randomized Mechanisms for Selling Reserved Instances in Cloud
We then extend our mechanism to the general case and achieve a competitive ratio $\frac{1}{42\log k\log T}$ for both social welfare and revenue, where $T$ is the ratio of the maximum request length to the minimum request length and $k$ is the ratio of the maximum request value density to the minimum request value density.
