Search

Extended Abstract Background: Maize is an important crop that is cultivated in many parts of the world. The evaluation of genotypes in breeding programs often faces two important challenges, the genotype × environment interaction effect for the target trait and unfavorable relationships between the target traits. Even though many methods have been offered for stability analysis, especially graphical tools and their relatively good efficiency in interpreting the results, it seems that the best linear unbiased predictions (BLUP) method estimates the means with high accuracy, especially in mixed models, in multi-environmental trails (MET). Therefore, the stability index of weighted average absolute scores (WAASB), which is estimated from the integration of the two stability methods of additive main effect and multiplicative interaction (AMMI) and best linear unbiased predictions, can be used in METs to estimate more accurately the stability of genotypes. Maize breeding programs prioritize high grain yields and earliness as important traits. The multi-trait stability index (MTSI) is a valuable tool for the simultaneous selection of multiple traits. It is estimated based on the average performance and simultaneous stability of genotypes in different traits and environments. Therefore, the current research aimed to identify stable and high grain yield maize hybrids along with the optimal level of grain moisture percentage at harvest time and days to physiological maturity using the integration of AMMI and BLUP methods with WAASB, WAASBY, and MTSI indices. Methods: This study involved the evaluation of seven promising maize hybrids along with four commercial check varieties, including SC647, TWC647, SC704, and SC715, in maize METs based on a randomized complete block design with four replications across 10 regions (Karaj, Moghan, Shiraz, Kermanshah, Kerman, Mashhad, Dezful, Miyandoab, Jiroft, and Mazandaran) during two cropping seasons of 2019-2020. The recorded traits were grain yield adjusted at 14% moisture content, grain moisture percentage at harvest time, and days to physiological maturity. The WAASB was used to estimate genotypic stability for each genotype. It was computed from the singular value decomposition (SVD) of the matrix of best linear unbiased predictions of genotype vs. environment interaction effects generated by a linear mixed-effect model. The WAASBY index for simultaneous selection based on grain yield (Y) and stability (WAASB) was estimated by assigning different weights to grain yield and stability. The simultaneous selection for grain yield and stability based on several traits was conducted using the scores obtained from an exploratory factor analysis (MTSI). Results: Based on the grain yield across 10 environments over two years, promising hybrid NO. 3 had the highest grain yield with 12.80 tons per hectare. According to the likelihood ratio test (LRT), the genotype-by-environment interaction was significant for the traits of grain yield, grain moisture percent at harvest time, and the days to physiological maturity. Therefore, BLUP analysis can be performed on these data due to the significant genotype by environment interaction. The BLUPs performed for hybrids were followed by stability analysis using the AMMI method on these BLUPs. The results indicated that the first and second components justified 27.7% and 24.6% of the hybrid by environment interaction variances, respectively. The highest predicted grain yield by the BLUP method belonged to hybrids No. 3, 2, 4, and 1, with higher than average predicted grain yields. Based on the biplot for the first principal component of the environments against the nominal grain yield, hybrids 2, 6, 3, and 1, having the lowest scores of the first principal component (coefficient b or line slope), had a negligible contribution to the hybrid by environment interaction and were distinguished stable. To enable simultaneous selection based on both grain yield and stability, the WAASBY index was estimated by integrating grain yield (Y) and the WAASB stability index. Considering the 50% contribution of each of the two grain yield and yield stability components, five hybrids (1, 2, 3, 6, and 4) showed above-average WAASBY. Among these, hybrids 1, 2, and 3 had significantly higher WAASBY than the other hybrids. All four control cultivars SC647, TWC647, SC704, and SC715 had lower-than-average WAASBY. Based on the MTSI, hybrid 3 was selected as the best hybrid. In addition, the estimated variance components by restricted maximum likelihood (REML) for grain yield indicated that 75.72% and 7.57% of the phenotypic variance were explained by the environment and GEI variances, respectively, whereas the contribution of residual variance to the phenotypic variance was 16.77%. Conclusion: Based on the results, hybrid 3 (K47/2-2-1-4-2-1-1-1× MO17) was identified as a high-yielding hybrid, which can be introduced to farmers as a new superior maize hybrid. It seems that the use of the ratio of the WAASB stability index to grain yield (WAASB/Y) and the selection of superior genotypes based on the MTSI could identify hybrids with high grain yields, stability, and desirable levels of important agronomic traits.

Reconstructing critical-sized craniofacial bone defects is a global healthcare challenge. Current methods, like autologous bone transplantation, face limitations. Bone tissue engineering offers an alternative to autologous bone, with traditional approaches focusing on stimulating osteogenesis via the intramembranous ossification (IMO) pathway. However, IMO falls short in addressing larger defects, particularly in clinical scenarios where there is insufficient vascularisation. This review explores redirecting bone regeneration through endochondral ossification (ECO), a process observed in long bone healing stimulated by hypoxic conditions. Despite its promise, gaps exist in applying ECO to bone tissue engineering experiments, requiring the elucidation of key aspects such as cell sources, biomaterials and priming protocols. This review discusses various scaffold biomaterials and cellular sources for chondrogenesis and hypertrophic chondrocyte priming, mirroring the ECO pathway. The review highlights challenges in current endochondral priming and proposes alternative approaches. Emphasis is on segmental mandibular defect repair, offering insights for future research and clinical application. This concise review aims to advance bone tissue engineering by addressing critical gaps in ECO strategies.

The transistor celebrated its 75th birthday in 2022. The continued scaling of the transistor defined by Moore’s law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning (ML) and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

Drowning poses a significant threat, resulting in unexpected injuries and fatalities. To promote water sports activities, it is crucial to develop surveillance systems that enhance safety around pools and waterways. This paper presents an overview of recent advancements in drowning detection, with a specific focus on image processing and sensor-based methods. Furthermore, the potential of artificial intelligence (AI), machine learning algorithms (MLAs), and robotics technology in this field is explored. The review examines the technological challenges, benefits, and drawbacks associated with these approaches. The findings reveal that image processing and sensor-based technologies are the most effective approaches for drowning detection systems. However, the image-processing approach requires substantial resources and sophisticated MLAs, making it costly and complex to implement. Conversely, sensor-based approaches offer practical, cost-effective, and widely applicable solutions for drowning detection. These approaches involve data transmission from the swimmer’s condition to the processing unit through sensing technology, utilising both wired and wireless communication channels. This paper explores the recent developments in drowning detection systems while considering costs, complexity, and practicality in selecting and implementing such systems. The assessment of various technological approaches contributes to ongoing efforts aimed at improving water safety and reducing the risks associated with drowning incidents.

Learning the properties of dynamical quantum systems underlies applications ranging from nuclear magnetic resonance spectroscopy to quantum device characterization. A central challenge in this pursuit is the learning of strongly interacting systems, where conventional observables decay quickly in time and space, limiting the information that can be learned from their measurement. In this work, we introduce a new class of observables into the context of quantum learning—the out-of-time-order correlator—which we show can substantially improve the learnability of strongly interacting systems by virtue of displaying informative physics at large times and distances. We identify two general scenarios in which out-of-time-order correlators provide a significant learning advantage: (i) when experimental access to the system is spatially restricted, for example, via a single “probe” degree of freedom, and (ii) when one desires to characterize weak interactions whose strength is much less than the typical interaction strength. We numerically characterize these advantages across a variety of learning problems, and find that they are robust to both read-out error and decoherence. Motivated by these physical scenarios, we introduce several learning tasks—including Clifford tomography, and learning the connectivity of an unknown unitary—in which out-of-time-order experiments have a provable exponential advantage over any learning protocol involving only time-ordered operations.

Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.

Expectations for quantum machine learning are high, but there is currently a lack of rigorous results on which scenarios would actually exhibit a quantum advantage. Here, the authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one.

Experimental demonstration of quantum speedup that scales with the system size is the goal of near-term quantum computing. Here, the authors demonstrate such scaling advantage for a D-Wave quantum annealer over analogous classical algorithms in simulations of frustrated quantum magnets.

One of the major application areas of interest for both near-term and fault-tolerant quantum computers is the optimization of classical objective functions. In this work, we develop intuitive constructions for a large class of these algorithms based on connections to simple dynamics of quantum systems, quantum walks, and classical continuous relaxations. We focus on developing a language and tools connected with kinetic energy on a graph for understanding the physical mechanisms of success and failure to guide algorithmic improvement. This physical language, in combination with uniqueness results related to unitarity, allow us to identify some potential pitfalls from kinetic energy fundamentally opposing the goal of optimization. This is connected to effects from wavefunction confinement, phase randomization, and shadow defects lurking in the objective far away from the ideal solution. As an example, we explore the surprising deficiency of many quantum methods in solving uncoupled spin problems and how this is both predictive of performance on some more complex systems while immediately suggesting simple resolutions. Further examination of canonical problems like the Hamming ramp or bush of implications show that entanglement can be strictly detrimental to performance results from the underlying mechanism of solution in approaches like the quantum approximate optimization algorithm (QAOA). Kinetic energy and graph Laplacian perspectives provide new insights to common initialization and optimal solutions in QAOA as well as new methods for more effective layerwise training. Connections to classical methods of continuous extensions, homotopy methods, and iterated rounding suggest new directions for research in quantum optimization. Throughout, we unveil many pitfalls and mechanisms in quantum optimization using a physical perspective, which aim to spur the development of novel quantum optimization algorithms and refinements.

Quantum tunnelling may be advantageous for quantum annealing, but multiqubit tunnelling has not yet been observed or characterized theoretically. Here, the authors demonstrate that 8-qubit tunnelling plays a role in a D-Wave Two device through a nonperturbative theory and experimental data.

We introduce a phenomenological theory for many-body control of critical phenomena by engineering causally-induced gaps for quantum Hamiltonian systems. The core mechanisms are controlling information flow within and/or between clusters that are created near a quantum critical point. To this end, we construct inhomogeneous quantum phase transitions via designing spatiotemporal quantum fluctuations. We show how non-equilibrium evolution of disordered quantum systems can create new effective correlation length scales and effective dynamical critical exponents. In particular, we construct a class of causally-induced non-adiabatic quantum annealing transitions for strongly disordered quantum Ising chains leading to exponential suppression of topological defects beyond standard Kibble–Zurek predictions. Using exact numerical techniques for 1D quantum Hamiltonian systems, we demonstrate that our approach exponentially outperforms adiabatic quantum computing. Using strong-disorder renormalization group (SDRG), we demonstrate the universality of inhomogeneous quantum critical dynamics and exhibit the reconstructions of causal zones during SDRG flow. We derive a scaling relation for minimal causal gaps showing they narrow more slowly than any polynomial with increasing size of system, in contrast to stretched exponential scaling in standard adiabatic evolution. Furthermore, we demonstrate similar scaling behavior for random cluster-Ising Hamiltonians with higher order interactions.

We introduce an inhomogeneous protocol to drive a weakly disordered quantum spin chain quasi-adiabatically across a quantum phase transition and minimize the residual energy of the final state. The number of spins that simultaneously reach the critical point is controlled by the length scale in which the magnetic field is modulated, introducing an effective size that favors adiabatic dynamics. The dependence of the residual energy on this length scale and the velocity at which the magnetic field sweeps out the chain is shown to be nonmonotonic. We determine the conditions for an optimal suppression of the residual energy of the final state and show that inhomogeneous driving can outperform conventional adiabatic schemes based on homogeneous control fields by several orders of magnitude.

Quantum state engineering and quantum computation rely on information erasure procedures that, up to some fidelity, prepare a quantum object in a pure state. Such processes occur within Landauer's framework if they rely on an interaction between the object and a thermal reservoir. Landauer's principle dictates that this must dissipate a minimum quantity of heat, proportional to the entropy reduction that is incurred by the object, to the thermal reservoir. However, this lower bound is only reachable for some specific physical situations, and it is not necessarily achievable for any given reservoir. The main task of our work can be stated as the minimisation of heat dissipation given probabilistic information erasure, i.e., minimising the amount of energy transferred to the thermal reservoir as heat if we require that the probability of preparing the object in a specific pure state $\left|{\varphi }_{1}\right.\rangle $ be no smaller than ${p}_{{\varphi }_{1}}^{\mathrm{max}}-\delta $ . Here ${p}_{{\varphi }_{1}}^{\mathrm{max}}$ is the maximum probability of information erasure that is permissible by the physical context, and $\delta \geqslant 0$ the error. To determine the achievable minimal heat dissipation of quantum information erasure within a given physical context, we explicitly optimise over all possible unitary operators that act on the composite system of object and reservoir. Specifically, we characterise the equivalence class of such optimal unitary operators, using tools from majorisation theory, when we are restricted to finite-dimensional Hilbert spaces. Furthermore, we discuss how pure state preparation processes could be achieved with a smaller heat cost than Landauer's limit, by operating outside of Landauer's framework.