List of references on QML, mantained by Roger Melko: Link.
Breuckmann et al. \cite{Breuckmann_2017} show how CNN can be proficiently used to tackle quantum fault-tolerance problems. From the paper: "In this work the existence of local decoders for higher dimensional codes leads us to use a low-depth convolutional neural network to locally assign a likelihood of error on each qubit."
Liu and Robentrost \cite{Liu_2017}: Quantum machine learning for quantum anomaly detection.
Romero et al. \cite{Romero_2017}: Quantum autoencoders for efficient compression of quantum data. "The quantum autoencoder is trained to compress a particular dataset of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.". Their idea is to implement an autoencoder in the form of a quantum circuit that takes an input \(\rho\), evolves it through a map \(\mathcal E(\rho)\), and then measures a number of the outputs. The state is thus "reduced" to the fewer degrees of freedom that are left after the measurements. The decoding operation is just the opposite of this.
Rocchetto et al. \cite{Rocchetto_2017}: Experimental learning of quantum states. They present a probabilistic setting in which quantum states can be learned using only a linear number of measurements, and present an experimental demonstration of this protocol with up to six qubits. They work in the context of the Probably Approximately Correct (PAC) model, introduced by Valiant in 1984.
Huembeli et al. \cite{Huembeli_2017}: Adversarial Domain Adaptation for Identifying Phase Transitions.
Zhao-Yu Han et al. \cite{Han_2017}: Efficient Quantum Tomography with Fidelity Estimation.
Benedetti et al. \cite{Benedetti_2017}: Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models.
Yudong Cao et al. \cite{Cao_2017}: Quantum Neuron: an elementary building block for machine learning on quantum computers.
Lumino et al. \cite{Lumino_2017}: Experimental Phase Estimation Enhanced By Machine Learning.
Agresti et al. \cite{Agresti_2017}: Pattern recognition techniques for Boson Sampling validation.
Melnikov et al. \cite{Melnikov_2017}: Active learning machine learns to create new quantum experiments. They develop an algorithm that, using the projective simulation model, is able to automatically discover schemes to create a variety of entangled states.
Otterbach et al. \cite{Otterbach_2017}: Unsupervised Machine Learning on a Hybrid Quantum Computer. They solve a clustering problem, translating it into a combinatorial optimization problem, that can be solved via the Quantum Approximate Optimization algorithm (Farhi 2014, Hadfield 2017). They implement this algorithm on the Rigetti 19-qubit architecture. More specifically, the clustering problem is rephrased as a MAXCUT problem.