How Does Quantum Computing Affect Big Data?
How quantum computing could affect big data: faster search and optimization for some tasks, data security, machine learning and storage.
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739 articles
Quantum Semidefinite Programming and Its Speedup
Quantum algorithms for semidefinite programming: how they work, which speedups are claimed and uses in machine learning and quantum information.
Quantum Algorithms for Lattice Problems
Quantum algorithms for lattice problems and quantum lattice models, and why lattice-based cryptography is believed to resist quantum attacks.
Quantum Connectivity Problems on Qubit Graphs
Why qubit connectivity limits quantum computers and networks: routing, swap overhead, entanglement distribution and network topologies.
Quantum Correlation Complexity and Its Measures
Measures of quantum correlations beyond entanglement, such as discord, how they differ from classical correlations and where they matter.
Quantum Coherence as a Resource, Quantified
The resource theory of coherence treats superposition as a resource that can be measured. Coherence measures and their link to entanglement.
Entanglement Complexity: Measuring How Tangled
How entanglement is quantified: entanglement entropy, witnesses and other measures, plus experiments, swapping, teleportation and networks.
Computational Phase Transitions in Quantum Problems
Computational phase transitions are sharp changes in how hard a problem is. How they appear in quantum problems and affect algorithms.
Quantum Threshold Logic and Its Circuits
Threshold logic gates fire when a weighted sum passes a threshold. How this idea is carried over to quantum circuits and where it might apply.
Quantum Error-Correcting Codes: A Catalogue
A catalogue of quantum error-correcting codes: principles, types, detection and correction mechanisms, implementation challenges and uses.
Quantum Sparse Coding and Dictionary Learning
Quantum approaches to sparse coding and dictionary learning: how data might be encoded in quantum states and how they compare with classical ones.
How Hard Are Quantum Neural Networks to Train?
Why quantum neural networks can be hard to train: barren plateaus, circuit depth, qubit connectivity, noise and the complexity trade-offs.