We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in largescale graph data.

Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.

Key-value lookup functions have been widely applied to network applications, including FIBs, load balancers, and content distributions. Two key performance requirements of a lookup algorithm are high throughput and small memory cost. One limitation of existing fast network lookup algorithms is that they require multiple independent and uniform hash functions, which cost high computation time and might not be available on existing hardware network devices. Recently developed learned model hashing (LMH) proposes to use a linear machine learning model to replace hash functions to avoid hash computation, but they are not optimized for memory cost. We propose a novel network lookup method called Parrot hashing, which uses a learned model to distribute keys into different buckets and applies a simple perfect hashing method to resolve the collisions of the keys in a bucket. Parrot can be implemented with only one CRC-8, which is available on all network devices. We implement Parrot in three prototypes: a software program on end hosts, a software switch, and a FIB running on a hardware programmable switch. The experimental results show that Parrot achieves the highest lookup throughput on all three prototypes, compared to existing methods. Its memory cost is also significantly lower than that of LMH.

LSM tree based stores are a popular database design in modern persistent storage systems due to their efficient writes with sorted keys. However, this hierarchical log structure suffers from extensive read amplification because multiple disk accesses are required when it searches for a key. Recent optimizations of LSM trees propose caching hot keys to reduce I/Os mainly based on their access frequencies. However, our empirical studies show that keys are different in I/O costs, which should also be considered in the caching policy: caching key-value pairs with high I/O cost can effectively improve query latency. In addition, false positives incurred by the Bloom filters in LSM trees introduce a large overhead to access SSTables because the queried keys do not exist. In this work, we design and implement SpotKV, which resolves the above two problems in an LSM tree store by proposing two memory-efficient data structures, weighted Count- Min sketch for access and I/O-aware cache admission and dynamic-seed Cuckoo filters for eliminating false positives, to improve data lookup throughput. We implement SpotKV on Google's LevelDB vl.20. From extensive experimental evaluations, SpotKV achieves 1.2-3.0x read throughput while using the same or smaller memory, compared with several state- of-the-art LSM tree stores under the read-heavy workloads of the YCSB benchmarks.

Key-value lookup functions have been widely applied to network applications, including FIBs, load balancers, and content distributions. Two key performance requirements of a lookup algorithm are high throughput and small memory cost. One limitation of existing fast network lookup algorithms is that they require multiple independent and uniform hash functions, which cost high computation time and might not be available on existing hardware network devices. Recently developed learned model hashing (LMH) proposes to use a linear machine learning model to replace hash functions to avoid hash computation, but they are not optimized for memory cost. We propose a novel network lookup method called Parrot hashing, which uses a learned model to distribute keys into different buckets and applies a simple perfect hashing method to resolve the collisions of the keys in a bucket. Parrot can be implemented with only one CRC8, which is available on all network devices. We implement Parrot in three prototypes: a software program on end hosts, a software switch, and a FIB running on a hardware programmable switch. The experimental results show that Parrot achieves the highest lookup throughput on all three prototypes, compared to existing methods. Its memory cost is also significantly lower than that of LMH.