4 papers
Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning
Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson +2
Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defin…
End-to-End Analysis of Charge Stability Diagrams with Transformers
Rahul Marchand, Lucas Schorling, Cornelius Carlsson +8
Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to anal…
Automated All-RF Tuning for Spin Qubit Readout and Control
Cornelius Carlsson, Jaime Saez-Mollejo, Federico Fedele +5
Efficient tuning of spin qubits remains a major bottleneck in scaling semiconductor quantum dot-based quantum processors. A key challenge is the rapid identification of gate voltag…
Identifying and mitigating errors in hole spin qubit readout
Eoin Gerard Kelly, Leonardo Massai, Bence Hetényi +14
High-fidelity readout of spin qubits in semiconductor quantum dots can be achieved by combining a radio-frequency (RF) charge sensor together with spin-to-charge conversion and Pau…