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Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as$${{{{{{{\mathcal{O}}}}}}}}({T}^{2}\times {{{{{{{\rm{polylog}}}}}}}}(n))$$$O\left(T^2\times \mathrm{polylog}\left(n\right)\right)$, wherenis the size of the models andTis the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

 

The abundance and environmental friendliness in nature of sulfur (S) make Li–S batteries more attractive in addition to the high theoretical capacity (1675 mAh g⁻¹) and energy density (2600 Wh kg⁻¹) of the batteries. In this study, a bio‐based S cathode with graphene (Gr) coating, capable of effectively suppressing the shuttle effect of polysulfides, is enabled via networking soy protein (SP) and polydopamine (PDA) to form a functional bio‐binder (SP‐PDA). Dopamine self‐polymerization in SP not only generates the interpenetrated network for the bio‐binder but also makes the denatured structure of SP with rich functional groups effective for trapping polysulfides. Meanwhile, the Gr coating with low impedance, and high electronic and ionic conductivity on the cathode surface further significantly reduces polysulfide dissolution. Consequently, the Li–S batteries with the bio‐cathode (SP‐PDA@Gr) demonstrate excellent rate performance and long cycling capacity. In specific, under the current density of 0.5 A g⁻¹at 70% (500 mAh g⁻¹) capacity retention, the cycle life of the Li–S cell with SP‐PDA@Gr cathode is 600 cycles, i.e.,100 times longer than that of the cell with PVDF binder. This study provides a sustainable strategy for enhancing the performance of Li–S batteries through networking natural proteins to form functional bio‐binders.

 

Fe2O3 is an appealing anode material due to its high specific capacity (1007 mAh g− 1), low cost, natural abundance, and nontoxicity. However, its unstable structure during cycling processes has hindered its potential. In this study, we present a “green” synthesis method to fabricate stable porous Fe2O3 encapsulated in a buffering hollow structure (p-Fe2O3@h-TiO2) as an effective anode material for Li-ion batteries. The synthesis process only involves glucose as an “etching” agent, without the need for organic solvents or difficult-to-control environments. Characterizations of the nanostructures, chemical compositions, crystallizations, and thermal behaviors for the intermediate/final products confirm the formation of p-Fe2O3@h-TiO2. The synthesized Fe2O3 anode material effectively accommodates volume change, decreases pulverization, and alleviates agglomeration, leading to a high capacity that is over eleven times greater than that of the as-received commercial Fe2O3 after a long cycling process. This work provides an attractive, “green” and efficient method to convert commercially abundant resources like Fe2O3 into effective electrode materials for energy storage systems.