Meta is building a new supercomputer to train enormous machine learning algorithms. Though only partially complete, the AI Research Supercluster (RSC) already ranks among the most powerful machines on the planet. When it’s finished, the […]
Category: singularityhub
Quantum 6, 633 (2022). https://doi.org/10.22331/q-2022-01-26-633 Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis […]
Quantum 6, 632 (2022). https://doi.org/10.22331/q-2022-01-24-632 Quantum error correction procedures have the potential to enable faithful operation of large-scale quantum computers. They protect information from environmental decoherence by storing it in logical qubits, built from ensembles […]
Quantum 6, 631 (2022). https://doi.org/10.22331/q-2022-01-24-631 Quantum circuits that are classically simulatable tell us when quantum computation becomes less powerful than or equivalent to classical computation. Such classically simulatable circuits are of importance because they illustrate […]
Quantum 6, 630 (2022). https://doi.org/10.22331/q-2022-01-24-630 The study of the impact of noise on quantum circuits is especially relevant to guide the progress of Noisy Intermediate-Scale Quantum (NISQ) computing. In this paper, we address the pulse-level […]
Quantum 6, 629 (2022). https://doi.org/10.22331/q-2022-01-24-629 Programmable arrays of hundreds of Rydberg atoms have recently enabled the exploration of remarkable phenomena in many-body quantum physics. In addition, the development of high-fidelity quantum gates are making them […]
Quantum 6, 628 (2022). https://doi.org/10.22331/q-2022-01-24-628 We explore a method for automatically recompiling a quantum circuit $mathcal{A}$ into a target circuit $mathcal{B}$, with the goal that both circuits have the same action on a specific input […]
Quantum computers made from the same raw materials as standard computer chips hold obvious promise, but so far they’ve struggled with high error rates. That seems set to change after new research showed silicon qubits […]
Quantum 6, 627 (2022). https://doi.org/10.22331/q-2022-01-20-627 A promising application of neural-network quantum states is to describe the time dynamics of many-body quantum systems. To realize this idea, we employ neural-network quantum states to approximate the implicit […]
Quantum 6, 626 (2022). https://doi.org/10.22331/q-2022-01-20-626 The characterization of mixtures of non-interacting, spectroscopically similar quantum components has important applications in chemistry, biology, and materials science. We introduce an approach based on quantum tracking control that allows […]
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