Norma Announces 2 Quantum AI Studies Accepted to IEEE QCE 2026
TECHWORLD ·
✦ AI Summary
Norma said 2 quantum AI research results had been accepted by IEEE QCE 2026.
The accepted items were 1 regular paper and 1 poster on its GPU-based simulation engine, Pado Pauli.
The regular paper, "SAFE ma-QAOA," addressed ways to reduce the hardware resource consumption of ma-QAOA.
Norma said on the 17th that 2 quantum AI research results had been accepted by IEEE QCE 2026, and that it presented 2 research outputs at IEEE QCE 2026, which was held in Toronto, Canada. IEEE QCE, officially named IEEE International Coference on Quantum Computing and Engineering, is a leading international conference in the fields of quantum computing and engineering organized by the IEEE Computer Society.
The materials Norma unveiled were 1 regular paper that significantly reduced the hardware resources consumed by a quantum optimization algorithm, and 1 poster related to its GPU-based simulation engine, Pado Pauli.
The accepted paper is titled "SAFE ma-QAOA," and the paper identifier is arXiv:2605.23377.
The paper addressed the fundamental dilemma of multi-angle QAOA (ma-QAOA). ma-QAOA is an expanded version of the quantum approximate optimization algorithm (QAOA).
The ma-QAOA structure assigns independent variational parameters to each term in the circuit. As a result, ma-QAOA offers improved expressiveness compared with conventional QAOA at the same circuit depth, and it also improves solution quality at the same circuit depth. Its limitation, however, is the rapid increase in the number of parameters, which leads to a surge in the number of QPU calls and measurements during optimization.
In the current NISQ environment, the number of qubits is limited and execution time is also constrained. Because of these structural issues in the NISQ environment, the hardware costs that come with performance gains undermine the practicality of the algorithm. In response, Norma's Q AI team chose an approach that redesigns the optimization procedure rather than improving hardware performance.
The research team proposed the SAFE (Surrogate-Assisted and Fine-tuning Enhanced) framework. The SAFE approach divides the optimization process into 3 stages and shifts most of the computation to low-cost classical computing resources. In this process, the classical surrogate model first explores a broad parameter space and narrows down promising regions, while the QPU adjusts the surrogate results to match real hardware conditions. SAFE's quantum hardware operation uses actual quantum hardware only in the final fine-tuning stage.
The team explained that the significance of the results lies in expanding the definition of quantum advantage beyond breaking through hardware physical limits and showing that practical results are possible even with current hardware through the division of roles between classical and quantum resources.
Norma's poster focused on Pado Pauli, the GPU-accelerated simulation engine independently developed by Norma. Pado Pauli replaces direct state-vector expansion and centers on Pauli propagation, which propagates observables through circuits in the Pauli basis. This approach is especially advantageous for variational algorithms such as VQE and QAOA, which repeatedly evaluate the same circuit several thousand times.
CEO Jeong Hyeon-cheol of Norma emphasized that the fact that a new optimization framework and its in-house simulation engine were both accepted by the same conference means Norma is a team that can complete not only idea proposals but also verifiable implementations. He also said the company will continue focusing on shortening the timeline for the adoption of practical quantum computing through a quantum-classical hybrid approach.
Source: TECHWORLD · Lee Gwang-jae
Original: https://www.epnc.co.kr/news/articleView.html?idxno=407069
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Source: TECHWORLD
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