Chase Roberts
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View article: Quantum Computing with Differentiable Quantum Transforms
Quantum Computing with Differentiable Quantum Transforms Open
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant ex…
View article: Quantum computing with differentiable quantum transforms
Quantum computing with differentiable quantum transforms Open
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant ex…
View article: Variational quantum algorithm for molecular geometry optimization
Variational quantum algorithm for molecular geometry optimization Open
Classical algorithms for predicting the equilibrium geometry of strongly correlated molecules require expensive wave function methods that become impractical already for few-atom systems. In this work, we introduce a variational quantum al…
View article: Entanglement and Tensor Networks for Supervised Image Classification
Entanglement and Tensor Networks for Supervised Image Classification Open
Tensor networks, originally designed to address computational problems in quantum many-body physics, have recently been applied to machine learning tasks. However, compared to quantum physics, where the reasons for the success of tensor ne…
View article: Anomaly Detection with Tensor Networks
Anomaly Detection with Tensor Networks Open
Originating from condensed matter physics, tensor networks are compact representations of high-dimensional tensors. In this paper, the prowess of tensor networks is demonstrated on the particular task of one-class anomaly detection. We exp…
View article: TensorNetwork: A Library for Physics and Machine Learning
TensorNetwork: A Library for Physics and Machine Learning Open
TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of othe…
View article: Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM Open
We propose a simple mathematical definition and new neural architecture for finding anomalies within discrete sequence datasets. Our model comprises of a modified LSTM autoencoder and an array of One-Class SVMs. The LSTM takes in elements …