The Quantum Perceptron: A Neuron Built From Qubits
Proposals for a quantum perceptron built from qubits and gates, how training would work, and how it compares with the classical perceptron.
BasicsAlgorithmsHardwareError CorrectionCryptographyMachine LearningInformation TheoryPhysics & SimulationApplicationsIndustry & Careers
Tagged: Artificial Intelligence
110 articles
Quantum GANs: Generative Adversarial Networks on Qubits
Quantum generative adversarial networks pit a quantum generator against a discriminator. Architecture, training and possible applications.
Quantum Boltzmann Machines (QBM) for Generative Models
Quantum Boltzmann machines as generative models: how they differ from classical ones, how they are trained and possible uses.
Coherent Information and the Quantum Capacity
Coherent information measures how much quantum information survives a channel, and its regularized maximum gives the quantum capacity.
Quantum Information Splitting Between Parties
Quantum information splitting distributes a quantum state among parties so that they must cooperate to recover it. Protocols and uses.
Quantum Equivalence Problems: Deciding Sameness
How to decide whether two quantum states, circuits or systems are equivalent, and the computational complexity of these problems.
Quantum Algorithmic Probability and Universal Priors
Quantum algorithmic probability extends Solomonoff’s universal prior and Kolmogorov complexity to quantum information. Ideas and uses.
Quantum Sampling Problems and Quantum Advantage
Sampling problems such as boson sampling and random circuit sampling were used to demonstrate quantum advantage. Why they are hard classically.
Quantum Multi-Linear Systems and Tensor Equations
Multilinear systems and tensor equations in quantum computing: the algebra involved, tensor networks, circuits and uses in machine learning.
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 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.
