Hybrid Conferencee

International Conference on Quantum Algorithms for Chemical Systems (ICQACS - 26)

31st - 1st September 2026 | Venice, Italy

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Certificate of Presentation:
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Networking with Global Experts:
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Keynote Speaker Sessions:
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Best Paper & Best Paper Presentation Award:
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SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 9
SDG 9 Industry, Innovation and Infrastructure
SDG 12
SDG 12 Responsible Consumption and Production
Track 01

Quantum Algorithms in Theoretical Chemistry

This track focuses on the development and application of quantum algorithms specifically tailored for theoretical chemistry. Participants will explore innovative approaches to solving complex chemical problems using quantum computing techniques.

Track 02

Molecular Simulations with Quantum Computing

This session will delve into the integration of quantum computing in molecular simulations, highlighting advancements in accuracy and efficiency. Researchers will present case studies demonstrating the impact of quantum methodologies on traditional simulation techniques.

Track 03

Quantum Machine Learning for Chemical Systems

This track investigates the intersection of quantum machine learning and chemical systems, emphasizing novel algorithms that leverage quantum properties. Discussions will include applications in data analysis and predictive modeling within chemistry.

Track 04

Quantum Circuit Design for Chemical Applications

Focusing on the design of quantum circuits, this session will cover methodologies that enhance the performance of quantum algorithms in chemical contexts. Participants will share insights on optimizing circuit architectures for specific chemical tasks.

Track 05

Hamiltonian Simulation Techniques

This track will explore state-of-the-art Hamiltonian simulation techniques in quantum computing, addressing their relevance to chemical systems. Researchers will discuss theoretical advancements and practical implementations in simulating quantum dynamics.

Track 06

Variational Algorithms in Quantum Chemistry

This session will focus on variational algorithms and their applications in quantum chemistry, emphasizing their role in optimizing chemical calculations. Presentations will cover both theoretical foundations and experimental results.

Track 07

Quantum Phase Estimation in Chemical Systems

This track will examine the application of quantum phase estimation techniques in the context of chemical systems. Participants will discuss the implications of these methods for understanding molecular properties and dynamics.

Track 08

Ab Initio Calculations Using Quantum Computing

This session will highlight the advancements in ab initio calculations facilitated by quantum computing technologies. Researchers will present methodologies that improve the accuracy and efficiency of electronic structure calculations.

Track 09

Computational Quantum Chemistry Advances

Focusing on the latest developments in computational quantum chemistry, this track will cover new algorithms and techniques that enhance chemical modeling. Discussions will include challenges and future directions in the field.

Track 10

Quantum Dynamics and Chemical Reactions

This session will explore the role of quantum dynamics in understanding chemical reactions at a fundamental level. Participants will present research that bridges quantum mechanics and chemical kinetics.

Track 11

Resource Estimation and Quantum Error Correction

This track will address the critical aspects of resource estimation and error correction in quantum algorithms for chemical applications. Researchers will discuss strategies to optimize resource usage while ensuring computational reliability.