Dual Map Framework for Noise Characterization of Quantum Computers

Abstract

In order to understand the capabilities and limitations of quantum computers, it is necessary to develop methods that efficiently characterize and benchmark error channels present on these devices. In this paper, we present a method that faithfully reconstructs a marginal (local) approximation of the effective noise (MATEN) channel, that acts as a single layer at the end of the circuit. We first introduce a dual map framework that allows us to analytically derive expectation values of observables with respect to noisy circuits. These findings are supported by numerical simulations of the quantum approximate optimization algorithm (QAOA) that also justify the MATEN, even in the presence of non-local errors that occur during a circuit. Finally, we demonstrate the performance of the method on Rigetti's Aspen-9 quantum computer for QAOA circuits up to six qubits, successfully predicting the observed measurements on a majority of the qubits.

arXiv comment11 pages. 7 figures. 2 tables. 5 pages appendix

References in corpus (37)

• QuTiP: An open-source Python framework for the dynamics of open quantum systems
• Error mitigation for short-depth quantum circuits
• Dynamical decoupling and noise spectroscopy with a superconducting flux qubit
• Prescription for experimental determination of the dynamics of a quantum black box
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• Robust randomized benchmarking of quantum processes
• A simple formula for the average gate fidelity of a quantum dynamical operation
• Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
• Efficient variational quantum simulator incorporating active error minimisation
• Scalable Noise Estimation with Random Unitary Operators
• Direct Fidelity Estimation from Few Pauli Measurements
• Induced measures in the space of mixed quantum states
• Dynamical decoupling noise spectroscopy
• Simulating Large Quantum Circuits on a Small Quantum Computer
• Characterizing large-scale quantum computers via cycle benchmarking
• Detecting crosstalk errors in quantum information processors
• $XY$-mixers: analytical and numerical results for QAOA
• Quantum annealing initialization of the quantum approximate optimization algorithm
• Observation of classical-quantum crossover of 1/f flux noise and its paramagnetic temperature dependence
• Average fidelity between random quantum states
• Robust Calibration of a Universal Single-Qubit Gate-Set via Robust Phase Estimation
• Efficient estimation of Pauli channels
• Implementation of the XY interaction family with calibration of a single pulse
• Machine learning non-Markovian quantum dynamics
• Error mitigation for universal gates on encoded qubits
• An introduction to operational quantum dynamics
• Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
• Experimental Hamiltonian identification for controlled two-level systems
• Classical symmetries and the Quantum Approximate Optimization Algorithm
• Characterizing local noise in QAOA circuits
• Randomized benchmarking for non-Markovian noise
• Quantum algorithms with local particle number conservation: noise effects and error correction
• Character randomized benchmarking for non-multiplicity-free groups with applications to subspace, leakage, and matchgate randomized benchmarking
• Independent State and Measurement Characterization for Quantum Computers
• High-speed calibration and characterization of superconducting quantum processors without qubit reset
• Augmented fidelities for single qubit gates