Noise Tailoring for Enhancing the Capabilities of Quantum Computers
The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. However, qubits in the noisy intermediate-scale quantum (NISQ) era are short-lived and susceptible to a variety of errors and noise due to imperfect control signals and incomplete isolation from the surrounding environment. For example, systematic miscalibrations, unwanted entanglement, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors can severely limit the performance of quantum algorithms in an unpredictable manner on timescales shorter than the coherence times of qubits. In recent years, there has been growing interest in using methods which randomize the physical implementation of quantum gates to mitigate the impact of coherent errors, effectively tailoring them into a form of stochastic noise. In this thesis, we study one such method --- randomized compiling --- and show how gate errors under randomized compiling are accurately described by a stochastic Pauli noise model without coherent errors. We demonstrate significant performance gains under randomized compiling for various different quantum algorithms, such as the quantum Fourier transform. We further show that randomized compiling can improve the predictability of quantum algorithms, and enables unique forms of error mitigation for enhancing the performance of quantum computations in the NISQ era. Finally, we show that randomized compiling can reduce worst-case error rates by orders of magnitude, enabling the accurate characterization of quantum gates for fault tolerance. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day, noisy quantum processors, paving the way forward for scalable quantum computing and fault tolerant quantum error correction.