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We characterize the robustness of cat qubits to drift with the application of a PPO neural network optimization algorithm. Through this, we seek to gain a deeper physical understanding of cat qubits.
We built a benchmark framework for optimizing dissipative cat qubits under realistic hardware drift. We benchmark CMA-ES against REINFORCE across three drift scenarios and compare our results.
Implementing quantum feature augmentation based on MRU and pairwise/rotational entanglement operators to isolate nonlinear feature relationships in noisy data.
Beyond standard heuristics: A hybrid CVRP solver using rotation-optimized clustering and hardware-ready backends to redefine logistics efficiency.
Hybrid approaches to solve capacitated vehicle routing problems (CVRPs).
YQuantum 2026 Competition Track: QuEra Challenge Team Name: Calbits Shuhul Mujoo,1 Nandana Madhukara,1 Thuwaragesh Jayachandran,1 Joshua Mu,1 and Emiliano Nolasco2 1Caltech 2SDSU
Transversal gates and zoned architectures are key to the meteoric rise of neutral atom platforms, pioneered by QuEra in 2023, but Eastin-Knill doesn't allow everything to be so easy. To T or not to T?
OMNY-ANALYST: While others hallucinate, we automate. 12 months of compliance in 10 mins with Gemini 2.0. Scalable. Fast.
A real-time decision tool that compares classical optimization with quantum-inspired methods (QAOA and DQI) to maximize insurance bundle profitability.
Financial signals hide in noise. We tested 1,620 quantum circuit designs and found that ensembles of random quantum circuits extract nonlinear features that beat the best classical methods by 5%.
Do quantum computers present a significant advantage over classical computers for predicting financial markets?
We hereby introduce the turtle method: our optimization of insurance profit using QAOA and DQI compared to a classical benchmark.
Use reinforcement learning model to stablize cat qubits
Implementation of QAOA and DQI in Quantinuum's Guppy language.
We want to put quantum portfolio optimization in your hands.
QuBots uses quantum computing to solve insurance portfolio optimization — maximizing returns under risk constraints using QAOA on neutral atom hardware.
In this challenge we explored how one qubit can sometimes be harder to implement than 2 qubits, especially under Clifford+T. We also explored how the STAR architecture can help with error detection.
AWS x Statestreet Challenge Quantum Feature Augmentation Comparisons
Physics-driven portfolio optimization using Rydberg atoms instead of manual tuning
If you are pursuing simplicity, look elsewhere. A single qubit will send you into a coma.
Team Qpitome's submission for QuEra's track at YQuantum 2026
Solved peaked circuits 1-9 of BlueQubit's Peaked Circuits challenge using a combination of their SDK, some of our own methods to simplify circuits, as well as IBM's novel approach.
Quantum portfolio optimization for insurers using real Hartford data, QUBO modeling, and Bloqade-based neutral-atom simulation.
Using a hybrid approach using QAOA, QUBO, and COBYLA, to understand if there exists quantum advantage for CVRP problems.
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