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Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture, specific exploitation of quantum advantage, or result quality, one common feature is their rooting in the Schrödinger equation. However, through contracting (or projecting) the eigenvalue equation, more efficient strategies can be designed for near-term quantum devices. Here we demonstrate that when combined with the Rayleigh–Ritz variational principle for mixed quantum states, the ground-state contracted quantum eigensolver (CQE) can be generalized to compute any number of quantum eigenstates simultaneously. We introduce twoexcited-state(anti-Hermitian) CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm, such as its scalability. To showcase our approach, we study several model and chemical Hamiltonians and investigate the performance of different implementations.

 

Soft machines will require soft materials that exhibit a rich diversity of functionality, including shape morphing and photoresponsivity. The combination of these functionalities enables useful behaviors in soft machines that can be further developed by synthesizing materials that exhibit localized responsivity. Localized responsivity of liquid crystal elastomers (LCEs), which are soft materials that exhibit shape morphing, can be enabled by formulating composite inks for direct ink writing (DIW). Gold nanorods (AuNRs) can be added to LCEs to enable photothermal shape change upon absorption of light through a localized surface plasmon resonance. We compared LCE formulations, focusing on their amenability for printing by DIW and the photoresponsivity of AuNRs. The local responsivity of different three-dimensional architectures enabled soft machines that could oscillate, crawl, roll, transport mass, and display other unique modes of actuation and motion in response to light, making these promising functional materials for advanced applications. 

Mechanical characterization of dynamic DNA nanodevices is essential to facilitate their use in applications like molecular diagnostics, force sensing, and nanorobotics that rely on device reconfiguration and interactions with other materials. A common approach to evaluate the mechanical properties of dynamic DNA nanodevices is by quantifying conformational distributions, where the magnitude of fluctuations correlates to the stiffness. This is generally carried out through manual measurement from experimental images, which is a tedious process and a critical bottleneck in the characterization pipeline. While many tools support the analysis of static molecular structures, there is a need for tools to facilitate the rapid characterization of dynamic DNA devices that undergo large conformational fluctuations. Here, we develop a data processing pipeline based on Deep Neural Networks (DNNs) to address this problem. The YOLOv5 and Resnet50 network architecture were used for the two key subtasks: particle detection and pose (i.e. conformation) estimation. We demonstrate effective network performance (F1 score 0.85 in particle detection) and good agreement with experimental distributions with limited user input and small training sets (~ 5 to 10 images). We also demonstrate this pipeline can be applied to multiple nanodevices, providing a robust approach for the rapid characterization of dynamic DNA devices.

 

Quantum computers are promising tools for simulating many-body quantum systems due to their potential scaling advantage over classical computers. While significant effort has been expended on many-fermion systems, here we simulate a model entangled many-boson system with the contracted quantum eigensolver (CQE). We generalize the CQE to many-boson systems by encoding the bosonic wavefunction on qubits. The CQE provides a compact ansatz for the bosonic wave function whose gradient is proportional to the residual of a contracted Schrödinger equation. We apply the CQE to a bosonic system, whereNquantum harmonic oscillators are coupled through a pairwise quadratic repulsion. The model is relevant to the study of coupled vibrations in molecular systems on quantum devices. Results demonstrate the potential efficiency of the CQE in simulating bosonic processes such as molecular vibrations with good accuracy and convergence even in the presence of noise.

 

In-memory key-value caches are widely used as a performance-critical layer in web applications, disk-based storage, and distributed systems. The Least Recently Used (LRU) replacement policy has become the de facto standard in those systems since it exploits workload locality well. However, the LRU implementation can be costly due to the rigid data structure in maintaining object priority, as well as the locks for object order updating. Redis as one of the most effective and prevalent deployed commercial systems adopts an approximated LRU policy, where the least recently used item from a small, randomly sampled set of items is chosen to evict. This random sampling-based policy is lightweight and shows its flexibility. We observe that there can exist a significant miss ratio gap between exact LRU and random sampling-based LRU under different sampling size $K$ s. Therefore existing LRU miss ratio curve (MRC) construction techniques cannot be directly applied without loss of accuracy. In this paper, we introduce a new probabilistic stack algorithm named KRR to accurately model random sampling based-LRU, and extend it to handle both fixed and variable objects in key-value caches. We present an efficient stack update algorithm that reduces the expected running time of KRR significantly. To improve the performance of the in-memory multi-tenant key-value cache that utilizes random sampling-based replacement, we propose kRedis, a reference locality- and latency-aware memory partitioning scheme. kRedis guides the memory allocation among the tenants and dynamically customizes $K$ to better exploit the locality of each individual tenant. Evaluation results over diverse workloads show that our model generates accurate miss ratio curves for both fixed and variable object size workloads, and enables practical, low-overhead online MRC prediction. Equipped with KRR, kRedis delivers up to a 50.2% average access latency reduction, and up to a 262.8% throughput improvement compared to Redis. Furthermore, by comparing with pRedis, a state-of-the-art design of memory allocation in Redis, kRedis shows up to 24.8% and 61.8% improvements in average access latency and throughput, respectively. 

Connecting pre-bent liquid crystal elastomer fibers into a loop generates a self-regulated synchronized motion with snap through. 

The giant circular photo‐galvanic effect is realized in chiral metals when illuminated by circularly polarized light. However, the structure itself is not switchable nor is the crystal chirality in the adjacent chiral domains. Here spindle‐shaped liquid crystalline elastomer microparticles that can switch from prolate to spherical to oblate reversibly upon heating above the nematic to isotropic transition temperature are synthesized. When arranged in a honeycomb lattice, the continuous shape change of the microparticles leads to lattice reconfiguration, from a right‐handed chiral state to an achiral one, then to a left‐handed chiral state, without breaking the translational symmetry. Accordingly, the sign of rotation of the polarized light passing through the lattices changes as measured by time‐domain terahertz spectroscopy. Further, it can locally alter the chirality in the adjacent domains using near‐infrared light illumination. The reconfigurable chiral microarrays will allow us to explore non‐trivial symmetry‐protected transport modes of topological lattices at the light–matter interface. Specifically, the ability to controllably create chiral states at the boundary of the achiral/chiral domains will lead to rich structures emerging from the interplay of symmetry and topology.