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Adversarial quantum circuit learning for pure state approximation
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data.
Universal discriminative quantum neural networks
This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs.
Approximating Hamiltonian dynamics with the Nyström method
Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers.
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
This paper demonstrates a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA).
Learning DNFs under product distributions via μ-biased quantum Fourier sampling
We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle.
Quantum machine learning: a classical perspective
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Experimental learning of quantum states
The number of parameters describing a quantum state is well known to grow exponentially with the number of particles.
Learning hard quantum distributions with variational autoencoders
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
Graph Cut Segmentation Methods Revisited with a Quantum Algorithm
The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented.
Neural Network Cost Landscapes as Quantum States
We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state.
Quantum Physics
The direct and indirect effect of CAP support on farm income enhancement:a farm-based econometric analysis
We assess the correlation between CAP support provided to farmers and their income and use of capital and labour in the first year of the new CAP regime.
The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool
Secondly, it analyses the role of a specific and relatively new CAP measure (i. e., the Income Stabilisation Tool - IST) that is specifically aimed at stabilising farm income.
The impact of CAP subsidies on the productivity of cereal farms in six European countries
The outcomes confirm how CAP negatively impacts farm TFP, but the extent differs according to the type of subsidies, the six countries and, within these, among farms with different productivity groups.
Can Machine Learning discover the determining factors in participation in insurance schemes? A comparative analysis
This study's challenges involve using many factors that could affect insurance participation to make a better forecast. Huge numbers of factors affect participation, making evaluation difficult.
