Assessment and mitigation of aerosol airborne SARS-CoV-2 transmission in laboratory and office environments.

Bioaerosols are known to be an important transmission pathway for SARS-CoV-2. We report a framework for estimating the risk of transmitting SARS-CoV-2 via aerosols in laboratory and office settings, based on an exponential dose-response model and analysis of air flow and purification in typical heating, ventilation, and air conditioning (HVAC) systems. High-circulation HVAC systems with high-efficiency particulate air (HEPA) filtration dramatically reduce exposure to the virus in indoor settings, and surgical masks or N95 respirators further reduce exposure. As an example of our risk assessment model, we consider the precautions needed for a typical experimental physical science group to maintain a low risk of transmission over six months of operation. We recommend that, for environments where fewer than five individuals significantly overlap, work spaces should remain vacant for between one (high-circulation HVAC with HEPA filtration) to six (low-circulation HVAC with no filtration) air exchange times before a new worker enters in order to maintain no more than 1% chance of infection over six months of operation in the workplace. Our model is readily applied to similar settings that are not explicitly given here. We also provide a framework for evaluating infection mitigation through ventilation in multiple occupancy spaces.

Microwave signal processing using an analog quantum reservoir computer.

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on microwave superconducting circuits to classify microwave signals that are analog-continuous in time. However, while there have been theoretical proposals of analog QRC, to date QRC has been implemented using the circuit model-imposing a discretization of the incoming signal in time. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our work demonstrates processing of ultra-low-power microwave signals within our superconducting circuit, a step towards achieving a quantum sensing-computational advantage on impinging microwave signals.