Preliminary

Oral Report

1. Phase-sensitive signal detection with a Rydberg-atom quantum receiver based on a Raman two-photon process
   Vladislav I. Katkov1, 1Innopolis University, Innopolis, Russia
2. Quantum noise correlations in optical dispersive waves
   Andrey Konyukhov, Saratov State University
3. Resource-Efficient Grover-Mixer QAOA for Higher-Order Optimization via Analytical Parameter Pre-optimization
   Higher-order unconstrained binary optimization (HUBO) problems are crucial for complex machine learning and logistics but remain challenging for near-term quantum devices due to intricate multi-variable interactions. While standard transverse-field QAOA (XM-QAOA) struggles with these complex landscapes, the Grover-mixer variant (GM-QAOA) offers a compelling global search alternative. We present a comprehensive numerical study on random hypergraphs and spin-glass models, demonstrating that unlike XM-QAOA, which rapidly plateaus at shallow depths, GM-QAOA exhibits monotonic performance improvement with increasing circuit depth. Crucially, GM-QAOA shows remarkable resilience to the increased complexity of higher-order interactions, significantly outperforming XM-QAOA beyond specific critical depths. To mitigate the high computational overhead of variational optimization, we develop an analytical framework modeling GM-QAOA dynamics. By assuming a Gaussian energy distribution and applying extreme value theory to estimate the ground-state energy, we derive a classical pre-optimization strategy for the algorithm's variational parameters. This resource-efficient approach, GM-QAOA(a), achieves approximation ratios nearly matching fully optimized layerwise GM-QAOA while drastically reducing the number of required quantum circuit evaluations. Furthermore, we show that the performance of this analytically pre-optimized variant actually improves as the locality of the cost Hamiltonian increases. Our findings establish a practical, scalable pathway for implementing global-mixer quantum algorithms on current hardware, offering a robust solution for complex, high-order combinatorial optimization tasks without the prohibitive costs of iterative hybrid training, making it highly suitable for near-term quantum processors.
4. Qudit-native simulation of the Quantum Potts model
   Maksim A. Gavreev,1,2, Evgeniy O. Kiktenko,1,2 Aleksey K. Fedorov,1,2 and Anastasiia S. Nikolaeva1,2; 1National University of Science and Technology “MISIS”, Moscow 119049, Russia; 2Russian Quantum Center, Skolkovo, Moscow 121205, Russia
5. Empirical Evaluation of the Approximation Ratio in Scalable Fixed-Point QAOA
   Andrey Yu. Chernyavskiy 1, Denis A. Kulikov 1,2, Boris I. Bantysh 1; 1 Russian Quantum Center, Skolkovo, Moscow 121205, Russia 2 Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russia
6. Adaptive neural-network decoding for quantum error correction
   Grigorii A. Grechkin, Daniil S. Bagaev, Evgeniy O. Kiktenko
7. Trapping of Ultracold Rydberg Atoms in a 1012-nm Optical Dipole Potential
   T. A. Voronova 1,2, G. A. Vishnyakova 1,2; 1 Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia 2 P. N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia
8. Entanglement Analysis of QAOA with Increasing Circuit Depth
   Boris I. Bantysh 1, Andrey Yu. Chernyavskiy 1, Denis A. Kulikov 1,2; 1 Russian Quantum Center, Skolkovo, Moscow 121205, Russia 2 Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russia