SESSION 7: Open-system dynamics and simulation

Chair: (Todd Brun)
10:45am - 11:30amHoward Carmichael, University of Auckland (invited)
Monitored quantum jumps: The view from quantum trajectory theory
Abstract. Quantum jumps are emblematic of all things quantum. Certainly that is so in the popular mind…and more than just an echo from the past, the term “quantum jump” still holds a prominent position within the lexicon of modern physics. What, however, is the character of the jump on close inspection? Is it discontinuous and discrete, as in Bohr’s original conception? Or is it some form of continuous Schrödinger evolution that might be monitored and reconstructed, even interrupted and turned around? I consider the jumps of single trapped ions observed in the mid-1980s [1], where an understanding drawn from quantum trajectory theory favours the latter option. I present that understanding and its connection to the modern view of continuous quantum measurement, and support this view from the theory side with experimental results [2], which recover the continuous and deterministic path of quantum jumps in a superconducting circuit using conditional quantum state tomography. [1] W. Nagourney et al., Phys. Rev. Lett. 56, 2797 (1986); T. Sauter et al., Phys. Rev. Lett. 57, 1696 (1986); J. C. Bergquist et al., Phys. Rev. Lett. 57, 1699 (1986). [2] Z. K. Minev, S. O. Mundhada, S. Shankar, P. Rheinhold, R. Gutiérrez-Jáuregui, R. J. Schoelkopf, M. Mirrahimi, H. J. Carmichael, and M. H. Devoret, arXiv:1803.00545 (2018).
11:30am - 12:00pmKathleen Hamilton, Oak Ridge National Laboratory
Noisy circuit training of generative models with superconducting qubits
Abstract. Many NISQ devices with < 20 qubits are becoming available for public use, but the lack of detailed noise models makes it difficult to use simulation to predict performance on hardware. We have used a recently introduced class of generative models [1] to quantify the performance of noisy superconducting qubits. Many sources of error and noise can be identified (e.g. decoherence, gate fidelities and measurement errors), and while the use of noise-robust stochastic optimizers can train circuits to a reasonable degree of accuracy, the cohesive incorporation of error mitigation into circuit training remains an open question. Our work focuses on how the performance of generative models on noisy qubits can be improved without error mitigation: by minimizing the number of noisy gates in a circuit and using sampling rates to improve the dynamics of gradient-based circuit training. [1] Liu, Jin-Guo, and Lei Wang. "Differentiable learning of quantum circuit Born machine." arXiv:1804.04168 (2018). This work was supported as part of the ASCR Testbed Pathfinder Program at Oak Ridge National Laboratory under FWP #ERKJ332

SQuInT Chief Organizer
Akimasa Miyake, Associate Professor

SQuInT Local Organizers
Rafael Alexander, Postdoctoral Fellow
Chris Jackson, Postdoctoral Fellow

SQuInT Administrator
Gloria Cordova
505 277-1850

SQuInT Assistant
Wendy Jay

SQuInT Founder
Ivan Deutsch, Regents' Professor, CQuIC Director

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