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High-fidelity gate operations are essential to the realization of a fault-tolerant quantum computer. In addition, the physical resources required to implement gates must scale efficiently with system size. A longstanding goal of the superconducting qubit community is the tight integration of a superconducting quantum circuit with a proximal classical cryogenic control system. Here we implement coherent control of a superconducting transmon qubit using a Single Flux Quantum (SFQ) pulse driver cofabricated on the qubit chip. The pulse driver delivers trains of quantized flux pulses to the qubit through a weak capacitive coupling; coherent rotations of the qubit state are realized when the pulse-to-pulse timing is matched to a multiple of the qubit oscillation period. We measure the fidelity of SFQ-based gates to be ~95% using interleaved randomized benchmarking. Gate fidelities are limited by quasiparticle generation in the dissipative SFQ driver. We characterize the dissipative and dispersive contributions of the quasiparticle admittance and discuss mitigation strategies to suppress quasiparticle poisoning. These results open the door to integration of large-scale superconducting qubit arrays with SFQ control elements for low-latency feedback and stabilization.
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Quantum State Estimation and Tracking for Superconducting Processors Using Machine Learning
Quantum technology has been rapidly growing; in particular, the experiments that have been performed with superconducting qubits and circuit QED have allowed us to explore the light-matter interaction at its most fundamental level. The study of coherent dynamics between two-level systems and resonator modes can provide insight into fundamental aspects of quantum physics, such as how the state of a system evolves while being continuously observed. To study such an evolving quantum system, experimenters need to verify the accuracy of state preparation and control since quantum systems are very fragile and sensitive to environmental disturbance. In this thesis, I look at these continuous monitoring and state estimation problems from a modern point of view. With the help of machine learning techniques, it has become possible to explore regimes that are not accessible with traditional methods: for example, tracking the state of a superconducting transmon qubit continuously with dynamics fast compared with the detector bandwidth. These results open up a new area of quantum state tracking, enabling us to potentially diagnose errors that occur during quantum gates. In addition, I investigate the use of supervised machine learning, in the form of a modified denoising autoencoder, to simultaneously remove experimental noise while encoding one and two-qubit quantum state estimates into a minimum number of nodes within the latent layer of a neural network. I automate the decoding of these latent representations into positive density matrices and compare them to similar estimates obtained via linear inversion and maximum likelihood estimation. Using a superconducting multiqubit chip, I experimentally verify that the neural network estimates the quantum state with greater fidelity than either traditional method. Furthermore, the network can be trained using only product states and still achieve high fidelity for entangled states. This simplification of the training overhead permits the network to aid experimental calibration, such as the diagnosis of multi-qubit crosstalk. As quantum processors increase in size and complexity, I expect automated methods such as those presented in this thesis to become increasingly attractive.
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- Award ID(s):
- 1915015
- PAR ID:
- 10353085
- Date Published:
- Journal Name:
- Ph.D. Thesis
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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