We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning, where we efficiently decompose the tasks of learning classical and quantum correlations in a way which maximizes the utility of both classical and quantum processors. In addition, we introduce the Variational Quantum Thermalizer (VQT) for generating the thermal state of a given Hamiltonian and target temperature, a task for which QHBMs are naturally well-suited.
Full paper can be found here.
We introduce and analyze a new type of decoding algorithm called General Color Clustering (GCC), based on renormalization group methods, to be used in qudit color codes. The performance of this decoder is analyzed under depolarizing and generalized bit flip noise models, and is used to obtain the first fault-tolerant threshold estimates for qudit 6-6-6 color codes. The proposed decoder is compared with similar decoding schemes for qudit surface codes as well as the current leading qubit decoders for both sets of codes…
This Quantum Computing package, started during Wolfram Summer School 2017, enables the simulation of quantum circuits for qudit (generalized d-level quantum system). Using sparse arrays and lookup tables, this package efficiently stores quantum states, maintaining a unified structure for pure, mixed, and entangled quantum states… Continue reading “Wolfram Quantum Computing Framework”
QTop is an open-source python module for simulation and visualization of topological quantum codes. QTop is object-oriented and easy to read, facilitating the addition and assessment of new topological computing architectures, as well as the development of novel decoders.