That’s it for the daily selection. If you enjoyed it, please consider giving me a like or reposting to support my content. Thanks! (Remember that these papers are published on arXiv before undergoing any peer review.)
In the June 12th edition:
- Quantum circuit design by LLM agent
- Integrated hardware-software system for superconducting qubit processors
- Quantum algorithms for random number generation
- Quantum advantage for predicting chaos.
More details and links below:
A new paper develops a quantum machine learning framework for chaotic systems using entangled “quantum priors” that can store complex correlations efficiently and enable certain measurements with exponentially fewer samples than classical methods.
arxiv.org/abs/2606.13422
QuBE/Qubex is an integrated hardware–software control system for superconducting quantum processors that automates configuration and calibration while enabling scalable, high-fidelity operation.
arxiv.org/abs/2606.13010
A new work introduces a quantum random number generation algorithm that uses QFT-based mixing and Grover-style amplitude averaging to achieve a provable quadratic speedup over classical method.
arxiv.org/abs/2606.13034
An AI agent powered by LLM can autonomously design and optimize quantum circuits for tasks in quantum machine learning and quantum chemistry, achieving performance competitive with human-designed approaches.
arxiv.org/abs/2606.13380
