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.
How quantum computing could affect big data: faster search and optimization for some tasks, data security, machine learning and storage.
Quantum algorithms for lattice problems and quantum lattice models, and why lattice-based cryptography is believed to resist quantum attacks.
Measures of quantum correlations beyond entanglement, such as discord, how they differ from classical correlations and where they matter.
The resource theory of coherence treats superposition as a resource that can be measured. Coherence measures and their link to entanglement.
How entanglement is quantified: entanglement entropy, witnesses and other measures, plus experiments, swapping, teleportation and networks.
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 approaches to sparse coding and dictionary learning: how data might be encoded in quantum states and how they compare with classical ones.
Why quantum neural networks can be hard to train: barren plateaus, circuit depth, qubit connectivity, noise and the complexity trade-offs.
Quantum Boltzmann machines extend Boltzmann machines with quantum terms. How they are trained, possible uses and current challenges.
Full quantum state tomography needs a number of measurements that grows exponentially with qubits. The cost, the methods and the alternatives.
BB84, proposed by Bennett and Brassard in 1984, was the first QKD protocol. How it encodes bits in photons and detects eavesdropping.
Quantum algorithms for computational geometry problems such as convex hulls and point location, and where speedups are possible.