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IEEE International Test Conference (ITC)

qFD: Coherent and Depolarizing Fault Diagnosis for Quantum Processors

Yen-Wei Li, Cheng-Yun Hsieh, M L Wu, James Chien-Mo Li

Published Nov 3, 2024• 0 citations
Publisher

Abstract

Errors caused by faults would strongly affect the correctness of noisy intermediate-scale quantum (NISQ) circuits. In this work, we propose a technique for diagnosing coherent and incoherent faults for NISQ circuits. The proposed technique contains three phases: rough diagnosis, fine diagnosis, and depolarizing diagnosis. Rough diagnosis grid searches the Bloch sphere to locate an approximate range of a coherent fault. Fine diagnosis then precisely locates the coherent fault size based on the narrowed-down search space. At last, depolarizing diagnosis measures the depolarizing fault size. We demonstrate our technique using the Qiskit simulator with noise-free and noisy backends. The diagnosis accuracy between the diagnosed faulty gates and the injected faulty gates is over 99.95%, which is better than traditional quantum process tomography under the same conditions. Our results show that the diagnosis error of coherent faults does not affect the diagnosis accuracy of depolarizing faults. Experiments on the IBM Q devices have also been performed, and results of over 99.83% diagnosis accuracy show that our technique still preserves good resolution on real quantum circuit devices.

DOI
10.1109/itc51657.2024.00038

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Errors caused by faults would strongly affect the correctness of noisy intermediate-scale quantum (NISQ) circuits. In this work, we propose a technique for diagnosing coherent and incoherent faults for NISQ circuits. The proposed technique contains three phases: rough diagnosis, fine diagnosis, and depolarizing diagnosis. Rough diagnosis grid searches the Bloch sphere to locate an approximate range of a coherent fault. Fine diagnosis then precisely locates the coherent fault size based on the narrowed-down search space. At last, depolarizing diagnosis measures the depolarizing fault size. We demonstrate our technique using the Qiskit simulator with noise-free and noisy backends. The diagnosis accuracy between the diagnosed faulty gates and the injected faulty gates is over 99.95%, which is better than traditional quantum process tomography under the same conditions. Our results show that the diagnosis error of coherent faults does not affect the diagnosis accuracy of depolarizing faults. Experiments on the IBM Q devices have also been performed, and results of over 99.83% diagnosis accuracy show that our technique still preserves good resolution on real quantum circuit devices.
Currently, noisy intermediate-scale quantum (NISQ) circuits may not always generate correct outputs due to noise and faults. In this work, we propose a diagnosis technique for NISQ circuits. The proposed technique contains static diagnosis and dynamic diagnosis. Static diagnosis uses a fault dictionary that contains output probability distribution for each fault. Dynamic diagnosis uses binary search to find the accurate fault locations of faulty quantum circuits. We demonstrate our technique using the Qiskit simulator with realistic noise models. We evaluate 15 benchmarks with unitary and non-unitary faults injected. Simulation results show that the average accuracy and resolution are 97.70% and 1.81. Experiments on the IBM Q devices have also been performed, and results show that our technique is feasible on real quantum circuit devices.
A new approach to detecting and diagnosing faults in quantum circuits is introduced. In order to account for the probabilistic nature of quantum circuits, collections of test experiments, called binary tomographic tests (BTTs), are generated. A BTT can identify a fault with respect to some user-defined confidence threshold τ. We present an algorithm to generate BTTs that either detect, or ensure the absence of, all modeled faults in a given circuit. We also present an adaptive diagnostic method to locate quantum faults. While classical circuits, even probabilistic ones, only handle ordinary probabilities, quantum circuits deal with quantum states, which have phase as an extra probabilistic parameter. The tomographic testing methods introduced previously for probabilistic circuits are unable to detect differences in phase, and therefore leave many quantum faults undetected. In contrast, we develop a design-for-test method which is specifically intended to detect faults that only affect the phase of a quantum state. We give experimental results for benchmark and random circuits which show high coverage of quantum faults by BTTs, and good resolution in the case of the adaptive diagnosis method.
Some of the most problematic issues that limit the implementation of applications on Noisy Intermediate Scale Quantum (NISQ) machines are the adverse impacts of both incoherent and coherent errors. We conducted an in-depth study of coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We report here on the results from these computations using several error mitigation protocols that profile these errors and provide an indication of the hardware qubit stability. Through a detailed set of measurements we identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
Detection and isolation of faults is a crucial step in the physical realization of quantum circuits. Even though quantum gates and circuits compute reversible functions, the standard techniques of automatic test pattern generation (ATPG) for classical reversible circuits are not directly applicable to quantum circuits. For faulty quantum circuits under the widely accepted single fault assumption, we show that their behavior can be fully characterized by the (single) faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy for fault detection and diagnosis. Building on top of these, we design randomized algorithms which are able to detect every nontrivial single-gate fault with minimal probability of error. We also describe similar algorithms for fault diagnosis. We evaluate our algorithms by the number of output samples that needs to be collected and the probability of error. Both of these can be related to the eigenvalues of the operators corresponding to the circuit gates. We experimentally compare all our strategies with the state-of-the-art ATPG techniques for quantum circuits under the “single missing faulty gate” model and demonstrate that significant improvement is possible if we can exploit the quantum nature of circuits.
Abstract This practical guide presents methods for constructing noise models using the Qiskit Aer noise module within the IBM Q ecosystem. The objective is to simulate quantum circuits under realistic error conditions, enabling accurate emulation of noisy quantum processors. The guide demonstrates how to: Use the Qiskit Aer noise module to define custom noise models. Incorporate gate errors, readout errors, and decoherence effects into simulations. Apply noise channels (e.g., depolarizing, amplitude damping, phase damping). Attach noise models to quantum circuit simulations. Analyze how noise affects algorithm performance and output distributions. Building noise models is essential for studying the behavior of quantum algorithms in the NISQ (Noisy Intermediate-Scale Quantum) era. It allows researchers to evaluate algorithm robustness, optimize circuit depth, and test error mitigation strategies before execution on real IBM Quantum hardware. This approach provides a bridge between ideal theoretical circuits and physically realistic quantum devices, enhancing the reliability and applicability of quantum computing experiments.
Near-term quantum computers have been built as intermediate-scale quantum devices and are fragile against quantum noise effects, namely, NISQ devices. Traditional quantum-error-correcting codes are not implemented on such devices and to perform quantum computation in good accuracy with these machines we need to develop alternative approaches for mitigating quantum computational errors. In this work, we propose quantum error mitigation (QEM) scheme for quantum computational errors which occur due to couplings with environments during gate operations, i.e., decoherence. To establish our QEM scheme, first we estimate the quantum noise effects on single-qubit states and represent them as groups of quantum circuits, namely, quantum-noise-effect circuit groups. Then our QEM scheme is conducted by subtracting expectation values generated by the quantum-noise-effect circuit groups from those obtained by the quantum circuits for the quantum algorithms under consideration. As a result, the quantum noise effects are reduced, and we obtain approximately the ideal expectation values via the quantum-noise-effect circuit groups and the numbers of elementary quantum circuits composing them scale polynomial with respect to the products of the depths of quantum algorithms and the numbers of register bits. To numerically demonstrate the validity of our QEM scheme, we run noisy quantum simulations of qubits under amplitude damping effects for four types of quantum algorithms. Furthermore, we implement our QEM scheme on IBM Q Experience processors and examine its efficacy. Consequently, the validity of our scheme is verified via both the quantum simulations and the quantum computations on the real quantum devices. Our QEM scheme is solely composed of quantum-computational operations (quantum gates and measurements), and thus, it can be conducted by any type of quantum device. In addition, it can be applied to error mitigation for many other types of quantum noise effects as well as noisy quantum computing of long-depth quantum algorithms.