Modeling Noisy Quantum Circuits Using Experimental Characterization

Abstract

Noisy intermediate-scale quantum (NISQ) devices offer unique platforms to test and evaluate the behavior of non-fault-tolerant quantum computing. However, validating programs on NISQ devices is difficult due to fluctuations in the underlying noise sources and other non-reproducible behaviors that generate computational errors. Efficient and effective methods for modeling NISQ behaviors are necessary to debug these devices and develop programming techniques that mitigate against errors. We present a test-driven approach to characterizing NISQ programs that manages the complexity of noisy circuit modeling by decomposing an application-specific circuit into a series of bootstrapped experiments. By characterizing individual subcircuits, we generate a composite model for the original noisy quantum circuit as well as other related programs. We demonstrate this approach using a family of superconducting transmon devices running applications of GHZ-state preparation and the Bernstein-Vazirani algorithm. We measure the model accuracy using the total variation distance between predicted and experimental results, and we find that the composite model works well across multiple circuit instances. In addition, these characterizations are computationally efficient and offer a trade-off in model complexity that can be tailored to the desired predictive accuracy.

13 pages, 12 figures; updated to reflect published version found at https://journals.aps.org/pra/abstract/10.1103/PhysRevA.103.042603 including author name change

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• Pulse-level noisy quantum circuits with QuTiP
• Lindblad Tomography of a Superconducting Quantum Processor
• Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm
• Experimental accreditation of outputs of noisy quantum computers
• Thermodynamics of quantum trajectories on a quantum computer
• Stability of noisy quantum computing devices
• Decoherence predictions in a superconductive quantum device using the steepest-entropy-ascent quantum thermodynamics framework
• Self-consistent quantum measurement tomography based on semidefinite programming
• Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms
• Noise-Robust Detection of Quantum Phase Transitions
• A practical guide for building superconducting quantum devices
• Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations
• Accreditation Against Limited Adversarial Noise
• State-dependent Routing Dynamics in Noisy Quantum Computing Devices
• Data-Efficient Quantum Noise Modeling via Machine Learning