Quantum Bayesian Networks: Inference, Quantised
Quantum Bayesian networks extend Bayesian inference to quantum probabilities. How they are built and compared with classical networks, and their uses.
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739 articlesQuantum Bayesian networks extend Bayesian inference to quantum probabilities. How they are built and compared with classical networks, and their uses.
Quantum state tomography reconstructs an unknown quantum state from many measurements. The methods, maximum likelihood estimation and its costs.
Quantum algorithms for graph problems: quantum walks, search speedups and subgraph questions, with an honest view of where advantages hold.
Quantum clustering algorithms group data using quantum states, walks, annealing and Grover search. How they compare with classical k-means methods.
How quantum circuits are designed and compiled: gate decomposition, qubit mapping, depth reduction, parallelization, synthesis and benchmarking.
Quantum multi-prover interactive proofs let a verifier check provers who share entanglement. Their complexity, security aspects and known results.
What a quantum oracle is, how algorithms query it as a black box, how oracles are built as circuits, and why they matter for speedup claims.
Schumacher’s theorem sets the limit for compressing quantum information, just as Shannon’s does for classical data. How quantum compression works.
The main quantum cryptography algorithms and protocols: QKD, entanglement-based schemes, secure direct communication, and how they compare.
Which quantum chemistry algorithms run on today’s hardware, what they compute, and how they may help drug discovery and materials science.
How quantum computers estimate eigenvalues: phase estimation, variational methods and their uses in chemistry, physics and data analysis.
Quantum amplitude estimation measures a probability with quadratically fewer samples than classical Monte Carlo. The circuit, precision and applications.