Publications (6)
Robust in Practice: Adversarial Attacks on Quantum Machine Learning
Haoran Liao, Ian Convy, William J. Huggins +1
State-of-the-art classical neural networks are observed to be vulnerable to small crafted adversarial perturbations. A more severe vulnerability has been noted for quantum machine…
Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits
Ian Convy, Haoran Liao, Song Zhang +5
Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination…
Interaction Decompositions for Tensor Network Regression
Ian Convy, K. Birgitta Whaley
It is well known that tensor network regression models operate on an exponentially large feature space, but questions remain as to how effectively they are able to utilize this spa…
Mutual Information Scaling for Tensor Network Machine Learning
Ian Convy, William Huggins, Haoran Liao +1
Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that th…
Decohering Tensor Network Quantum Machine Learning Models
Haoran Liao, Ian Convy, Zhibo Yang +1
Tensor network quantum machine learning (QML) models are promising applications on near-term quantum hardware. While decoherence of qubits is expected to decrease the performance o…
A Logarithmic Bayesian Approach to Quantum Error Detection
Ian Convy, K. Birgitta Whaley
We consider the problem of continuous quantum error correction from a Bayesian perspective, proposing a pair of digital filters using logarithmic probabilities that are able to ach…