James E. Steck

Experimental pairwise entanglement estimation for an N-qubit system :A machine learning approach for programming quantum hardware

2019 · arXiv (Cornell University)

Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such …

Learning quantum annealing

2016 · arXiv (Cornell University)

We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate …

Training microwave pulses using quantum machine learning

2023

<p>A gate sequence of single qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning …

Training microwave pulses using quantum machine learning

2024 · arXiv (Cornell University)

A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is …

Robust and Scalable Quantum Repeaters Using Machine Learning

2025 · Information

Quantum repeaters are integral systems to quantum computing and quantum communication as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that general quantum states …

Robust Adaptive Quantum Feedback Under Practical Uncertainties- A Lightweight Approach

2025

Quantum systems are highly sensitive to timing jitter, measurement noise, and partial observability, which degrade feedback control performance. We present a lightweight adaptive quantum feedback strategy inspired by Deep Deterministic Policy Gradient (DDPG), but designed …