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In the NISQ era, where quantum information processing is hindered by the decoherence and dissipation of elementary quantum systems, developing new protocols to extend the lifetime of quantum states is of considerable practical and theoretical importance. A prominent method, called dynamical decoupling, uses a carefully designed sequence of pulses applied to a quantum system, such as a qudit (a d-level quantum system), to suppress the coupling Hamiltonian between the system and its environment, thereby mitigating dissipation. While dynamical decoupling of qubit systems has been widely studied, the decoupling of qudit systems has been far less explored and often involves complex sequences and operations. In this work, we design efficient decoupling sequences composed solely of SU(2) rotations and based on tetrahedral, octahedral, and icosahedral point groups, which we call Platonic sequences. We use a generalization of the Majorana representation for Hamiltonians to develop a simple framework that establishes the decoupling properties of each Platonic sequence and show its efficiency on many examples. These sequences are universal in their ability to cancel any type of interaction with the environment for single qudits with up to 6 levels, and they are capable of decoupling up to 5-body interactions in an ensemble of interacting qubits with only global pulses, provided that the interaction Hamiltonian has no isotropic component, with the exception of the global identity. We also discuss their inherent robustness to finite pulse duration and a wide range of pulse errors, as well as their potential application as building blocks for dynamically corrected gates., Comment: 23 pages, 8 figures

We study how a Reinforcement Learning (RL) system can remain sample-efficient when learning from an imperfect model of the environment. This is particularly challenging when the learning system is resource-constrained and in continual settings, where the environment dynamics change. To address these challenges, our paper introduces an online, meta-gradient algorithm that tunes a probability with which states are queried during Dyna-style planning. Our study compares the aggregate, empirical performance of this meta-gradient method to baselines that employ conventional sampling strategies. Results indicate that our method improves efficiency of the planning process, which, as a consequence, improves the sample-efficiency of the overall learning process. On the whole, we observe that our meta-learned solutions avoid several pathologies of conventional planning approaches, such as sampling inaccurate transitions and those that stall credit assignment. We believe these findings could prove useful, in future work, for designing model-based RL systems at scale.

We investigate the fate of dissipative phase transitions in quantum many-body systems when the individual constituents are qudits ($d$-level systems) instead of qubits. As an example system, we employ a permutation-invariant $XY$ model of $N$ infinite-range interacting $d$-level spins undergoing individual and collective dissipation. In the mean-field limit, we identify a dissipative phase transition, whose critical point is independent of $d$ after a suitable rescaling of parameters. When the decay rates between all adjacent levels are identical and $d\geq 4$, the critical point expands, in terms of the ratio between dissipation and interaction strengths, to a critical region in which two phases coexist and which increases as $d$ grows. In addition, a larger $d$ leads to a more pronounced change in spin expectation values at the critical point. Numerical investigations for finite $N$ reveal symmetry breaking signatures in the Liouvillian spectrum at the phase transition. The phase transition is furthermore marked by maximum entanglement negativity and a significant purity change of the steady state, which become more pronounced as $d$ increases. Considering qudits instead of qubits thus opens new perspectives on accessing rich phase diagrams in open many-body systems., Comment: 20 pages, 9 figures

The efficiency of a quantum metrology protocol can be considerably reduced by the interaction of a quantum system with its environment, resulting in a loss of purity and, consequently, a mixed state for the probing system. In this paper we examine the potential of mixed spin-$j$ states to achieve sensitivity comparable, and even equal, to that of pure states in the measurement of infinitesimal rotations about arbitrary axes. We introduce the concept of mixed optimal quantum rotosensors based on a maximization of the Fisher quantum information and show that it is related to the notion of anticoherence of spin states and its generalization to subspaces. We present several examples of anticoherent subspaces and their associated mixed optimal quantum rotosensors. We also show that the latter maximize negativity for specific bipartitions, reaching the same maximum value as pure states. These results elucidate the interplay between quantum metrology of rotations, anticoherence and entanglement in the framework of mixed spin states., Comment: 13 pages, 3 figures

We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent offline model-free approaches successfully use online fine-tuning to either improve the performance of the agent over the data collection policy or adapt to novel tasks. At the same time, model-based RL algorithms have achieved significant progress in sample efficiency and the complexity of the tasks they can solve, yet remain under-utilized in the fine-tuning setting. In this work, we argue that existing model-based offline RL methods are not suitable for offline-to-online fine-tuning in high-dimensional domains due to issues with distribution shifts, off-dynamics data, and non-stationary rewards. We propose an on-policy model-based method that can efficiently reuse prior data through model-based value expansion and policy regularization, while preventing model exploitation by controlling epistemic uncertainty. We find that our approach successfully solves tasks from the MetaWorld benchmark, as well as the Franka Kitchen robot manipulation environment completely from images. To the best of our knowledge, MOTO is the first method to solve this environment from pixels., Comment: This is an updated version of a manuscript that originally appeared at CoRL 2023. The project website is here https://sites.google.com/view/mo2o

Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) have shown high potential in their ability to solve complex differential equations. One example is the use of PINNs to solve the gravity field modeling problem -- learning convenient representations of the gravitational potential from position and acceleration data. These PINN gravity models, or PINN-GMs, have demonstrated advantages in model compactness, robustness to noise, and sample efficiency when compared to popular alternatives; however, further investigation has revealed various failure modes for these and other machine learning gravity models which this manuscript aims to address. Specifically, this paper introduces the third generation Physics-Informed Neural Network Gravity Model (PINN-GM-III) which includes design changes that solve the problems of feature divergence, bias towards low-altitude samples, numerical instability, and extrapolation error. Six evaluation metrics are proposed to expose these past pitfalls and illustrate the PINN-GM-III's robustness to them. This study concludes by evaluating the PINN-GM-III modeling accuracy on a heterogeneous density asteroid, and comparing its performance to other analytic and machine learning gravity models., Comment: 40 pages, 10 figures, submitted to The Journal of Astronautical Sciences

The persistent separability of certain quantum states, known as symmetric absolutely separable (SAS), under symmetry-preserving global unitary transformations is of key significance in the context of quantum resources for bosonic systems. In this work, we develop criteria for detecting SAS states of any number of qubits. Our approach is based on the Glauber-Sudarshan $P$ representation for finite-dimensional quantum systems. We introduce three families of SAS witnesses, one linear and two nonlinear in the eigenvalues of the state, formulated respectively as an algebraic inequality or a quadratic optimization problem. These witnesses are capable of identifying more SAS states than previously known counterparts. We also explore the geometric properties of the subsets of SAS states detected by our witnesses, shedding light on their distinctions., Comment: 16 pages, 5 figures

The COVID-19 pandemic has had far-reaching implications for global public health. As we continue to grapple with its consequences, it becomes increasingly clear that post-COVID-19 complications are a significant concern. Among these complications, renal impairment has garnered particular attention due to its potential long-term health impacts. This study, conducted with a cohort of 821 post-COVID-19 patients from diverse regions of Iraq across the years 2021, 2022, and 2023, endeavors to predict the risk of long-term renal impairment using advanced machine learning algorithms. Our findings have the potential to revolutionize post-COVID-19 patient care by enabling early identification and intervention for those at risk of renal impairment, ultimately improving clinical outcomes. This research encompasses comprehensive data collection and preprocessing, feature selection, and the development of predictive models using various machine learning algorithms. The study's objectives are to assess the incidence of long-term renal impairment in post-COVID-19 patients, identify associated risk factors, create predictive models, and evaluate their accuracy. We anticipate that our machine learning models, drawing from a rich dataset, will provide valuable insights into the risk of renal impairment, ultimately enhancing patient care and quality of life. In conclusion, the research presented herein offers a critical contribution to the field of post-COVID-19 care. By harnessing the power of machine learning, we aim to predict long-term renal impairment risk accurately. These predictions have the potential to inform healthcare professionals, enabling them to take proactive measures and provide targeted interventions for post-COVID-19 patients at risk of renal complications, thus minimizing the impact of this serious health concern.

The vastness of the space of possible multicomponent metal alloys is hoped to provide improved structural materials but also challenges traditional, low-throughput materials design efforts. Computational screening could narrow this search space if models for materials stability and desired properties exist that are sufficiently inexpensive and accurate to efficiently guide experiments. Towards this effort, here we develop a method to rapidly assess the thermodynamic stability of a metal alloy composition of arbitrary number of elements, stoichiometry, and temperature based on density functional theory (DFT) data. In our model, the Gibbs free energy of the solid solution contains binary enthalpy contributions and ideal configurational entropy, whereas only enthalpy is considered for intermetallic competing phases. Compared to a past model for predicting the formation of single-phase high-entropy alloys [Phys. Rev. X 5, 011041 (2015)], our method is similarly inexpensive, since it assesses enthalpies based on existing DFT data, but less heuristic, more broadly applicable, and more accurate (70--75%) compared to experiment.

The COVID-19 pandemic has left a lasting impact on global healthcare systems, with increasing evidence of pulmonary fibrosis emerging as a post-infection complication. This study presents a comprehensive analysis of pulmonary fibrosis patterns in post-COVID-19 patients from South and Central Iraq, employing advanced machine learning algorithms. Data were collected from 390 patients, and their medical records were systematically analyzed. Our findings reveal distinct patterns of pulmonary fibrosis in this cohort, shedding light on the heterogeneous nature of post-COVID-19 lung complications. Machine learning models demonstrated robust predictive capabilities, offering valuable insights into the characterization of fibrotic changes. The identification of specific patterns contributes to early diagnosis and personalized treatment strategies for affected individuals. This research underscores the importance of data-driven approaches in understanding post-COVID-19 complications, particularly in regions with unique demographic and healthcare characteristics. It emphasizes the potential for machine learning to enhance clinical decision-making and improve patient care in the aftermath of the pandemic. Further investigations are warranted to validate these findings and explore additional factors influencing pulmonary fibrosis in post-COVID-19 patients.

The COVID-19 pandemic has brought to light a concerning aspect of long-term neurological complications in post-recovery patients. This study delved into the investigation of such neurological sequelae in a cohort of 500 post-COVID-19 patients, encompassing individuals with varying illness severity. The primary aim was to predict outcomes using a machine learning approach based on diverse clinical data and neuroimaging parameters. The results revealed that 68% of the post-COVID-19 patients reported experiencing neurological symptoms, with fatigue, headache, and anosmia being the most common manifestations. Moreover, 22% of the patients exhibited more severe neurological complications, including encephalopathy and stroke. The application of machine learning models showed promising results in predicting long-term neurological outcomes. Notably, the Random Forest model achieved an accuracy of 85%, sensitivity of 80%, and specificity of 90% in identifying patients at risk of developing neurological sequelae. These findings underscore the importance of continuous monitoring and follow-up care for post-COVID-19 patients, particularly in relation to potential neurological complications. The integration of machine learning-based outcome prediction offers a valuable tool for early intervention and personalized treatment strategies, aiming to improve patient care and clinical decision-making. In conclusion, this study sheds light on the prevalence of long-term neurological complications in post-COVID-19 patients and demonstrates the potential of machine learning in predicting outcomes, thereby contributing to enhanced patient management and better health outcomes. Further research and larger studies are warranted to validate and refine these predictive models and to gain deeper insights into the underlying mechanisms of post-COVID-19 neurological sequelae.

We investigate the impact of dissipation on weak measurements. While weak measurements have been successful in signal amplification, dissipation can compromise their usefulness. More precisely, we show that in systems with non-degenerate eigenstates, weak values always converge to the expectation value of the measured observable as dissipation time tends to infinity, in contrast to systems with degenerate eigenstates, where the weak values can remain anomalous, i.e., outside the range of eigenvalues of the observable, even in the limit of an infinite dissipation time. In addition, we propose a method for extracting information about the dissipative dynamics of a system using weak values at short dissipation times. Specifically, we explore the amplification of the dissipation rate in a two-level system and the use of weak values to differentiate between Markovian and non-Markovian dissipative dynamics. We also find that weak measurements operating around a weak atom-cavity coupling can probe the atom dissipation through the weak value of non-Hermitian operators within the rotating-wave approximation of the weak interaction.