74 citations · 75 across the 5 of their papers we have counts for
5 papers
Exact learning of quantum noise with tensor networks
Nicola Pancotti, Vedika Saravanan, Krysta Svore
Accurate noise models are essential for high-performance quantum error correction, yet characterizing the noise of a quantum device typically requires dedicated experiments. We pre…
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty +11
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer ch…
QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding
Shuxiang Cao, Zijian Zhang, Abhishek Agarwal +29
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no system…
Supervised quantum gate "teaching" for quantum hardware design
Leonardo Banchi, Nicola Pancotti, Sougato Bose
We show how to train a quantum network of pairwise interacting qubits such that its evolution implements a target quantum algorithm into a given network subset. Our strategy is ins…
Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions
Leonardo Banchi, Nicola Pancotti, Sougato Bose
We put forward a strategy to encode a quantum operation into the unmodulated dynamics of a quantum network without the need of external control pulses, measurements or active feedb…