skip to main content
10.1145/3665225acmconferencesBook PagePublication PagesmodConference Proceedingsconference-collections
Q-Data '24: Proceedings of the 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications
ACM2024 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGMOD/PODS '24: International Conference on Management of Data Santiago AA Chile June 9 - 15, 2024
ISBN:
979-8-4007-0553-3
Published:
29 June 2024
Sponsors:
Recommend ACM DL
ALREADY A SUBSCRIBER?SIGN IN

Reflects downloads up to 18 Feb 2025Bibliometrics
Abstract

No abstract available.

Skip Table Of Content Section
abstract
Towards Out-of-Core Simulators for Quantum Computing
research-article
Open Access
QardEst: Using Quantum Machine Learning for Cardinality Estimation of Join Queries

Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel ...

research-article
Open Access
Leveraging Quantum Computing for Database Index Selection

We present an approach to solve the NP-hard database index selection problem on a D-Wave 2X adiabatic quantum annealer with over 1000 qubits. One of our main contributions is an efficient algorithm that maps instances of the index selection problem onto ...

research-article
Open Access
Quantum Data Encoding Patterns and their Consequences

The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we ...

short-paper
Open Access
Constrained Quadratic Model for Optimizing Join Orders

We present a quantum-based approach for the optimization of join orders in database applications. Our approach relies on a hybrid framework, where classical heuristics are combined with a quantum processor to accelerate the search over the set of ...

Contributors
  • University of Southern California
  • Cornell University

Recommendations