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Abstract Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with$${{{\mathcal{O}}}}(\log N)$$ $O\left(\log N\right)$circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~10⁷in 1D, ~10¹⁵to ~10²⁰in 2D, and ~10²⁴in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science. 

A conjugated ladder polymer composed of negatively curved cyclooctatetraene monomer units was synthesized in a single step from bis-zirconacyclopentadienes, resulting in microporosity and high processability. 

Abstract 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. 

Abstract Understanding psychology is an important task in modern society which helps predict human behavior and provide feedback accordingly. Monitoring of weak psychological and emotional changes requires bioelectronic devices to be stretchable and compliant for unobtrusive and high‐fidelity signal acquisition. Thin conductive polymer film is regarded as an ideal interface; however, it is very challenging to simultaneously balance mechanical robustness and opto‐electrical property. Here, a 40 nm‐thick film based on photolithographic double‐network conductive polymer mediated by graphene layer is reported, which concurrently enables stretchability, conductivity, and conformability. Photolithographic polymer and graphene endow the film photopatternability, enhance stress dissipation capability, as well as improve opto‐electrical conductivity (4458 S cm⁻¹@>90% transparency) through molecular rearrangement by π–π interaction, electrostatic interaction, and hydrogen bonding. The film is further applied onto corrugated facial skin, the subtle electromyogram is monitored, and machine learning algorithm is performed to understand complex emotions, indicating the outstanding ability for stretchable and compliant bioelectronics. 

Hydrogels, known for their mechanical and chemical similarity to biological tissues, are widely used in biotechnologies, whereas semiconductors provide advanced electronic and optoelectronic functionalities such as signal amplification, sensing, and photomodulation. Combining semiconducting properties with hydrogel designs can enhance biointeractive functions and intimacy at biointerfaces, but this is challenging owing to the low hydrophilicity of polymer semiconductors. We developed a solvent affinity–induced assembly method that incorporates water-insoluble polymer semiconductors into double-network hydrogels. These semiconductors exhibited tissue-level moduli as soft as 81 kilopascals, stretchability of 150% strain, and charge-carrier mobility up to 1.4 square centimeters per volt per second. When they are interfaced with biological tissues, their tissue-level modulus enables alleviated immune reactions. The hydrogel’s high porosity enhances molecular interactions at semiconductor-biofluid interfaces, resulting in photomodulation with higher response and volumetric biosensing with higher sensitivity. 

Abstract Wearable devices benefit from the use of stretchable conjugated polymers (CPs). Traditionally, the design of stretchable CPs is based on the assumption that a low elastic modulus (E) is crucial for achieving high stretchability. However, this research, which analyzes the mechanical properties of 65 CP thin films, challenges this notion. It is discovered that softness alone does not determine stretchability; rather, it is the degree of entanglement that is critical. This means that rigid CPs can also exhibit high stretchability, contradicting conventional wisdom. To inverstigate further, the mechanical behavior, electrical properties, and deformation mechanism of two model CPs: a glassy poly(3‐butylthiophene‐2,5‐diyl) (P3BT) with anEof 2.2 GPa and a viscoelastic poly(3‐octylthiophene‐2,5‐diyl) (P3OT) with anEof 86 MPa, are studied. Ex situ transmission X‐ray scattering and polarized UV–vis spectroscopy revealed that only the initial strain (i.e., <20%) exhibits different chain alignment mechanisms between two polymers, while both rigid and soft P3ATs showed similarly behavior at larger strains. By challenging the conventional design metric of lowEfor high stretchability and highlighting the importance of entanglement, it is hoped to broaden the range of CPs available for use in wearable devices.