QC&DKM 2026Workshop Program
QC&DKM 2026 is a half-day event combining invited keynote talks and peer-reviewed paper presentations.
Tentative Schedule (September 4th 2026)
| Time | Activity |
|---|---|
| 13:45 - 15:15 | Session 1 |
| 13:45 - 14:30 |
[Keynote] Manish Kesarwani (IBM Research, India) Relational Database Engines on Quantum Platforms |
| 14:30 - 14:45 |
Evaluating a Quantum Solution: How hard can it be?
|
| 14:45 - 15:00 |
What Quantum Encoding to Use for your Knowledge Graph?
|
| 15:00 - 15:15 |
Quantum Information-Theoretical Size Bounds for Conjunctive Queries with Functional Dependencies
|
| 15:15 - 15:45 | Coffee Break |
| 15:45 - 17:15 | Session 2 |
| 15:45 - 16:30 |
[Keynote] Immanuel Trummer (Cornell University, USA) Solving Hard Database Optimization Problems via Digital and Quantum Annealing |
| 16:30 - 16:45 |
Improving Join Order Optimization on Gate-Based Quantum Computers via Structured Parameter Initialization
|
| 16:45 - 17:00 |
Quantum-Ready Approximate Data Management using Tensor-Network Physical Pages
|
| 17:00 - 17:15 |
Towards Caching Strategies for Quantum Simulators
|
Keynote Speakers
QC&DKM 2026 will feature two invited keynote talks by leading researchers at the intersection of quantum computing and data/knowledge management.
Immanuel Trummer is an associate professor of computer science at Cornell University. His research focuses on making data analysis more efficient and more user-friendly, often intersecting with other domains such as machine learning and quantum computing. His papers were selected for "Best of VLDB", "Best of SIGMOD", and the CACM Research Highlight Award. He received an NSF CAREER grant and multiple Google Faculty Research Awards. He is also the author of the book "Data Analysis with LLMs", now available in five languages.
Solving Hard Database Optimization Problems via Digital and Quantum Annealing
AbstractDatabase management systems give rise to a plethora of optimization problems that typically have the following properties. They are NP-hard to solve, finding solutions is time-critical, and solution quality has a dramatic impact on performance. In combination, those factors motivate a search for novel hardware accelerators, enabling us to explore large combinatorial search spaces more efficiently than classical machines.
In my talk, I will discuss our research efforts, spanning more than a decade, aimed at leveraging quantum computing and quantum-inspired accelerators to make database optimization more efficient. I will discuss approaches for solving query optimization variants on quantum annealers, starting with a VLDB 2016 paper, and touch on solutions developed for a variety of other database optimization problems. In the last part of my talk, I will discuss our most recent work, published at SIGMOD 2026, which aims at overcoming limits imposed by the restricted number of qubits available on current machines. I will show that quantum-inspired hardware accelerators, in combination with domain-specific problem decomposition strategies, can solve significantly larger query optimization instances than previously practical.
Manish Kesarwani is a Senior Research Scientist at IBM, India. With over a decade of experience at IBM Research, he has led projects at the intersection of relational database systems, quantum computing, artificial intelligence, and applied cryptography. He holds 35+ patents and was recognized among IBM's Top Global Contributors and Top Inventors in 2025. Manish has served on the program and organizing committees of several leading conferences, most recently as the Registration Chair of SIGMOD 2026. As part of his PhD research at the Indian Institute of Science (IISc), under the guidance of Prof. Jayant Haritsa, his work focuses on fundamental challenges in hosting database system components on quantum platforms. His research has been published in premier database conferences, including VLDB and SIGMOD.
Relational Database Engines on Quantum Platforms
AbstractQuantum computing is rapidly progressing from theory to practice, with fault-tolerant hardware expected within the coming decade. These advances raise a fundamental question for the database community: can quantum computing help solve core DBMS problems?
This keynote talk explores the applicability of quantum computing to database management systems. We begin by examining recent advances in applying quantum algorithms to core DBMS tasks, including query optimization and index configuration selection. While these early results demonstrate the potential of quantum techniques, they also expose fundamental challenges in adapting database tasks to quantum hardware. The talk discusses these challenges and their implications for future system design, before concluding with a roadmap of open research problems that must be addressed to realize practical quantum database systems.