Simulating Exotic States of Matter With Qubits
How quantum simulation is used to study exotic states of matter such as unconventional superconductors, topological order and anyons.
How quantum simulation is used to study exotic states of matter such as unconventional superconductors, topological order and anyons.
Optical lattices hold ultracold atoms in grids of laser light and act as quantum simulators for many-body physics. Setup, experiments and uses.
Digital quantum simulation breaks a system’s time evolution into sequences of gates on a quantum computer. Methods, uses and hardware limits.
Quantum Markov chains describe memoryless quantum processes and states with conditional independence. Their structure and applications.
Google’s quantum program: the 2019 Sycamore supremacy result, later processors, error correction milestones and what the claims mean.
IBM’s quantum roadmap compared with what it has delivered: Eagle, Heron, Condor, Quantum System Two, Qiskit and targets to 2033.
How cross-validation and model selection work for quantum machine learning models, and the challenges caused by quantum noise.
The expectation-maximization algorithm fits models with hidden variables. How a quantum version works and what speedup it could offer.
Ensemble methods combine several models to improve predictions. How quantum ensembles and model averaging work and what they could offer.
Quantum convolutional neural networks apply convolution and pooling layers to qubits. Their design, uses in classifying quantum states and challenges.
Quantum algorithms for linear regression promise speedups, but loading the data into quantum states can cancel them. Where they might help.
Architectures for quantum reinforcement learning, how they compare with classical reinforcement learning, and where they are being tested.