# IEEE Quantum Week Workshop Focuses on AI for Quantum Circuits > Source: > Published: 2026-06-19 14:39:07.756884+00:00 # IEEE Quantum Week Workshop Focuses on AI for Quantum Circuits Per the workshop listing on Quantiki, a workshop titled "AI for Circuit Synthesis, Optimization, and Discovery" is scheduled as part of **IEEE Quantum Week 2026**, held in **Toronto, Ontario, Canada**, **13-18 September 2026**. Quantiki lists a registration deadline of **Friday, June 19, 2026** and a submission (call-for-papers) deadline of **Thursday, July 9, 2026**. The workshop's stated objective is to facilitate dissemination and exchange on applying Artificial Intelligence to gate-based quantum computing tasks: **circuit synthesis**, **circuit optimization**, and **circuit discovery**. The workshop solicits research submissions that use **deep learning** and **reinforcement learning**, and highlights hardware-aware optimization and automated ansatz discovery as focal topics (Quantiki). IEEE program pages also list related tutorials that mention circuit synthesis, generative AI, and agentic methods (qce.quantum.ieee.org snippet). ### What happened Per the workshop listing on Quantiki, the "AI for Circuit Synthesis, Optimization, and Discovery" workshop is part of **IEEE Quantum Week 2026**, running in **Toronto, Ontario, Canada**, **13-18 September 2026**. Quantiki gives a registration deadline of **Friday, June 19, 2026** and a submission deadline (call-for-papers) of **Thursday, July 9, 2026**. The listing states the workshop will focus on applying **Artificial Intelligence** to gate-based quantum computing tasks, specifically **circuit synthesis**, **circuit optimization**, and **circuit discovery**, and it invites submissions leveraging **deep learning** and **reinforcement learning** for hardware-aware optimization and automated ansatz discovery (Quantiki). ### Technical details Editorial analysis - technical context: The workshop themes, circuit synthesis, hardware-aware optimization, and ansatz discovery, map to familiar ML approaches in the quantum domain. Practitioners typically explore supervised models for pattern-based synthesis, reinforcement learning for sequential gate construction and transpilation, and generative models for proposing circuit ansatze. Hardware-aware objectives usually add constrained optimization components such as gate set compatibility, connectivity constraints, and noise-aware cost functions. IEEE program material also lists related tutorial sessions that reference diagrammatic reasoning, generative AI, and agentic approaches to circuit synthesis (qce.quantum.ieee.org snippet). ### Context and significance Conference workshops that combine ML and quantum-circuit engineering serve as a staging ground for early results that may later mature into open-source tooling or integration with frameworks like Qiskit and Cirq. Past IEEE Quantum Week programs and community writeups have featured demonstrations of reinforcement-learning-based transpilers and ML models for QPU job prediction, indicating a persistent interest in operationalizing AI for developer workflows (Juan Cruz-Benito blog on IEEE Quantum Week 2025; Quantiki). ### What to watch For observers: follow accepted papers and associated code releases to see which ML architectures and training signals practitioners adopt for hardware-aware objectives. Track whether submissions provide reproducible benchmarks on real devices or realistic noise models, and whether they release datasets or transpilation toolchains. Also watch tutorial and session listings in the official IEEE program for overlaps that indicate broader conference emphasis on generative and agentic AI methods for circuit construction (qce.quantum.ieee.org snippet). ### Takeaway for practitioners Editorial analysis: The workshop formalizes a continuing community trend that applies mainstream ML techniques to low-level quantum engineering tasks. Researchers and ML engineers interested in quantum tooling should consider the submission deadline (**July 9, 2026**) and the opportunity to present hardware-aware ML approaches that target near-term noisy and early fault-tolerant devices (Quantiki). ## Scoring Rationale This is a mid-level research-community event at a major conference that consolidates ML methods applied to quantum circuit engineering. It is relevant to practitioners building transpilers, ansatz discovery tools, and hardware-aware optimizers, but it is not a frontier-model release or major commercial announcement. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)