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cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Quantum Algorithms in Cybernetics
Petar Nikolov · 2021-03-10 · via cs.DS updates on arXiv.org

A new method for simulation of a binary homogeneous Markov process using a quantum computer was proposed. This new method allows using the distinguished properties of the quantum mechanical systems -- superposition, entanglement and probability calculations. Implementation of an algorithm based on this method requires the creation of a new quantum logic gate, which creates entangled state between two qubits. This is a two-qubit logic gate and it must perform a predefined rotation over the X-axis for the qubit that acts as a target, where the rotation accurately represents the transient probabilities for a given Markov process. This gate fires only when the control qubit is in state |1>. It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets. The creation of a quantum controlled n-th root of X gate using only the existing basic quantum logic gates at the available cloud platforms is possible, although the hardware devices are still too noisy, which results in a significant measurement error increase. The IBM's Yorktown 'bow-tie' back-end performs quite better than the 5-qubit T-shaped and the 14-qubit Melbourne quantum processors in terms of quantum fidelity. The simulation of the binary homogeneous Markov process on a real quantum processor gives best results on the Vigo and Yorktown (both 5-qubit) back-ends with Hellinger fidelity of near 0.82. The choice of the right quantum circuit, based on the available hardware (topology, size, timing properties), would be the approach for maximizing the fidelity.