63 citations · 184 across the 11 of their papers we have counts for
11 papers
Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer
Gavin S. Hartnett, Haoran Liao, Enrico Rinaldi
Matrix models are an important class of systems in string theory and theoretical physics, with applications to random matrix theory, quantum chaos, and black holes. Hamiltonian Mon…
Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor
Gavin S. Hartnett, Khadijeh Sona Najafi, Aleksei Khindanov +5
We report experimental digital quantum simulation of the one-dimensional Fermi-Hubbard model on a superconducting quantum processor at a scale beyond the reach of exact statevector…
Achieving computational gains with quantum error-correction primitives: Generation of long-range entanglement enhanced by error detection
Haoran Liao, Gavin S. Hartnett, Ashish Kakkar +5
The resource overhead required to achieve net computational benefits from quantum error correction (QEC) limits its utility while current systems remain constrained in size, despit…
Entanglement-enhanced learning of quantum processes at scale
Alireza Seif, Senrui Chen, Swarnadeep Majumder +6
Learning unknown processes affecting a quantum system reveals underlying physical mechanisms and enables suppression, mitigation, and correction of unwanted effects. Describing a g…
ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements
Neel R. Vora, Yilun Xu, Akel Hashim +12
Accurate and timely quantum state discrimination is critical for quantum computing, particularly in protocols requiring mid-circuit measurement (MCM) and conditional feed-forward.…
Suppressing Correlated Noise in Quantum Computers via Context-Aware Compiling
Alireza Seif, Haoran Liao, Vinay Tripathi +5
Coherent errors, and especially those that occur in correlation among a set of qubits, are detrimental for large-scale quantum computing. Correlations in noise can occur as a resul…
Machine Learning for Practical Quantum Error Mitigation
Haoran Liao, Derek S. Wang, Iskandar Sitdikov +3
Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle. The key to overcoming errors on near-term devices has em…
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…
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…
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…
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…