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Here are some scientific papers that caught our eye this month:
Quantum machine learning is a hot topic. In this work, collaborators, including members of QuEra’s team, explore the role that analog computing features—common in neutral-atom platforms—can play in these types of applications. Particular emphasis is given to the synergistic outcomes of analog and single-qubit gate-based digital capabilities operating in tandem. The findings in this work indicate that this hybrid operation mode can lead to shorter circuit depths and increased robustness to error.
All quantum computer architectures have restrictions on system size (in number of qubits) due to physical limitations. While neutral-atom platforms offer quite optimistic numbers, indicating that cores with orders of 10,000s of atoms are possible (in comparison to the few 10s or 100s in competing architectures), interconnection between those cores will eventually be necessary. This work from J. Thompson’s group, based at Princeton University, proposes experiments where an optical cavity is loaded with 100+ atoms via tweezers, and entanglement protocols are deployed using light shifts. Estimates of their protocol suggest it is possible to achieve long-distance Bell-pair generation at a 0.1 MHz rate with 99.9% fidelity. If realized experimentally, this could serve as a meaningful architecture choice for multi-node neutral-atom quantum computers.
An exciting achievement for QuEra’s team and collaborators: This paper demonstrates a novel experimental analysis of quantum matter phenomena using Aquila. Long quasi-1D arrays, or ladders, of neutral atoms are used to demonstrate the emergence of quasi-long-range order in what is known as a 'quantum floating phase'. This demonstration showcases Aquila’s coherence and capacity to probe quantum phases. It also motivates further experimental studies of commensurate-incommensurate phase transitions, their dynamics, and phenomena related to high-energy physics via conformally invariant systems.
More experimental results have been obtained on Aquila, this time from a team based at UMD. In this work, the authors implement a novel method for efficient simulations of quantum systems. They have dubbed it 'Hamiltonian embedding'. The method explicitly utilizes Hamiltonian evolution, a key task where analog quantum computers excel, demonstrating the capacity to solve problems beyond the native spaces of the original devices. These demonstrations have been conducted on both ion-trap and neutral-atom hardware, including Aquila, and encompass real-space Schrödinger equation dynamics in 2D.
