Thermoelectric Coefficients of N-Doped Silicon from First-Principles via the Solution of the Boltzmann Transport Equation

We present a first-principles computational approach to calculate thermoelectric transport coefficients via the exact solution of the linearised Boltzmann transport equation, also including the effect of non-equilibrium phonon populations induced by a temperature gradient. We use density functional theory and density functional perturbation theory for an accurate description of the electronic and vibrational properties of a system.

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Quantum-Assisted Helmholtz Machines: A Quantum-Classical Deep Learning Framework for Industrial Datasets in Near-Term Devices

In this work, we introduce the quantum-assisted Helmholtz machine: a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. We use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. The quantum hardware and deep learning architecture work together to train an unsupervised generative model.

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Readiness of Quantum Optimization Machines for Industrial Applications

We analyse the readiness of quantum annealing machines for real world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimisation techniques. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers.

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Hierarchical Quantum Classifiers

Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method.

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Training of Quantum Circuits on a Hybrid Quantum Computer

Generative modelling is a flavour of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. This study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.

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Structure Optimization for Parameterized Quantum Circuits

We propose an efficient method for simultaneously optimising both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimisation perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers.

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Parameterized Quantum Circuits as Machine Learning Models

Hybrid quantum-classical systems make it possible to utilise existing quantum computers to their fullest extent. Within this framework, parameterised quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning and generative modelling.

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