Quantum 6, 635 (2022). https://doi.org/10.22331/q-2022-01-27-635 The quantum approximate optimization algorithm (QAOA) is a near-term hybrid algorithm intended to solve combinatorial optimization problems, such as MaxCut. QAOA can be made to mimic an adiabatic schedule, and […]
Quantum 6, 634 (2022). https://doi.org/10.22331/q-2022-01-27-634 Parallel operations in conventional computing have proven to be an essential tool for efficient and practical computation, and the story is not different for quantum computing. Indeed, there exists a […]
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 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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