141 citations · 232 across the 6 of their papers we have counts for
6 papers
Generative Learning of Continuous Data by Tensor Networks
Alex Meiburg, Jing Chen, Jacob Miller +3
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervise…
Qubit seriation: Improving data-model alignment using spectral ordering
Atithi Acharya, Manuel Rudolph, Jing Chen +2
With the advent of quantum and quantum-inspired machine learning, adapting the structure of learning models to match the structure of target datasets has been shown to be crucial f…
Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization
Javier Lopez-Piqueres, Jing Chen, Alejandro Perdomo-Ortiz
Constrained combinatorial optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence…
Decomposition of Matrix Product States into Shallow Quantum Circuits
Manuel S. Rudolph, Jing Chen, Jacob Miller +2
The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed tensor network (TN) algorithms to scale to even…
Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage
Manuel S. Rudolph, Jacob Miller, Danial Motlagh +3
While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the us…
Tensor renormalization of quantum many-body systems using projected entangled simplex states
Z. Y. Xie, J. Chen, J. F. Yu +3
We propose a new class of tensor-network states, which we name projected entangled simplex states (PESS), for studying the ground-state properties of quantum lattice models. These…