About

Daniel Lidar is the holder of the Viterbi Professorship of Engineering at the University of Southern California, with joint appointments in the departments of Electrical & Computer Engineering, Chemistry, and Physics & Astronomy. He specializes in quantum information processing, focusing on quantum computation and quantum computers. His research encompasses quantum error correction, open quantum systems, quantum algorithms, quantum control, superconducting qubits, quantum phase transitions, adiabatic quantum computation, quantum annealing, and quantum machine learning. Lidar is the Director of the USC Center for Quantum Information Science & Technology, the USC-IBM Quantum Innovation Center, and the co-Director of the USC Center for Quantum Computing. He has received numerous awards and honors, including fellowships from the APS, IEEE, and AAAS, as well as prestigious research fellowships such as the Guggenheim and Sloan Fellowships.

Research topics

• Computer Science
• Physics
• Artificial Intelligence
• Mathematics
• Statistics
• Quantum mechanics
• Optics
• Atomic physics

Selected publications

• Error budget of parametric resonance entangling gate with a tunable coupler

  arXiv (Cornell University) · 2024

  • Physics
  • Atomic physics
  • Optics

  We analyze the experimental error budget of parametric resonance gates in a tunable coupler architecture. We identify and characterize various sources of errors, including incoherent, leakage, amplitude, and phase errors. By varying the two-qubit gate time, we explore the dynamics of these errors and their impact on the gate fidelity. To accurately capture the impact of incoherent errors on gate fidelity, we measure the coherence times of qubits under gate operating conditions. Our findings reveal that the incoherent errors, mainly arising from qubit relaxation and dephasing due to white noise, limit the fidelity of the two-qubit gates. Moreover, we demonstrate that leakage to noncomputational states is the second largest contributor to the two-qubit gates infidelity, as characterized using leakage-randomized benchmarking. The error budgeting methodology we developed here can be effectively applied to other types of gate implementations.

  DOI

• ClassiFIM: An Unsupervised Method To Detect Phase Transitions

  arXiv (Cornell University) · 2024

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Estimation of the Fisher Information Metric (FIM-estimation) is an important task that arises in unsupervised learning of phase transitions, a problem proposed by physicists. This work completes the definition of the task by defining rigorous evaluation metrics distMSE, distMSEPS, and distRE and introduces ClassiFIM, a novel machine learning method designed to solve the FIM-estimation task. Unlike existing methods for unsupervised learning of phase transitions, ClassiFIM directly estimates a well-defined quantity (the FIM), allowing it to be rigorously compared to any present and future other methods that estimate the same. ClassiFIM transforms a dataset for the FIM-estimation task into a dataset for an auxiliary binary classification task and involves selecting and training a model for the latter. We prove that the output of ClassiFIM approaches the exact FIM in the limit of infinite dataset size and under certain regularity conditions. We implement ClassiFIM on multiple datasets, including datasets describing classical and quantum phase transitions, and find that it achieves a good ground truth approximation with modest computational resources. Furthermore, we independently implement two alternative state-of-the-art methods for unsupervised estimation of phase transition locations on the same datasets and find that ClassiFIM predicts such locations at least as well as these other methods. To emphasize the generality of our method, we also propose and generate the MNIST-CNN dataset, which consists of the output of CNNs trained on MNIST for different hyperparameter choices. Using ClassiFIM on this dataset suggests there is a phase transition in the distribution of image-prediction pairs for CNNs trained on MNIST, demonstrating the broad scope of FIM-estimation beyond physics.

  DOI

• Characterizing noise for capacitively-shunted flux qubits

  Bulletin of the American Physical Society · 2020

  Senior authorCorresponding
  • Computer Science
  • Physics
  • Quantum mechanics

Frequent coauthors

• V.K. Tripathi

  8 shared

• Amy F. Brown

  Quantum Group (United States)

  5 shared

• Eyob A. Sete

  Rigetti Computing (United States)

  4 shared

• Mostafa Khezri

  Google (United States)

  4 shared

• A. R. Hamilton

  4 shared

• Howard E. Brandt

  3 shared

• Sergey M. Bezrukov

  3 shared

• Matt Reagor

  Rigetti Computing (United States)

  3 shared

Awards & honors

• 2018 USC Viterbi School of Engineering Senior Research Award
• 2017 California Institute of Technology Moore Distinguished…
• 2017 John Simon Guggenheim Foundation Guggenheim Fellowship
• 2016 University of Southern California Viterbi Professorship
• 2014 The Institute of Electrical and Electronics Engineers (…

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