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.
Category (publication): Machine Learning
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.
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.
A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to assist the characterisation of quantum devices and to train shallow circuits for generative tasks.
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.
Adversarial Quantum Circuit Learning for Pure State Approximation
In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers.
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.
An Initialization Strategy for Addressing Barren Plateaus in Parametrized Quantum Circuits
In this technical note, we theoretically motivate and empirically validate an initialisation strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity.
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.
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.