Quantum computing is a fascinating research area which promises a revolution in computing performance. Since the launch of the IBM Quantum Experience project in 2016, the research activities in this area are strongly increased. This project provides the public access to quantum processors composed of superconducting physical computing elements known as qubits. Unfortunately, qubits are sensitive to noise and, for this reason, quantum computation can be affected by errors. As a consequence, there is a strong emergence for so- called quantum error mitigation methods aimed to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error- prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms whose fitness function uses Bhattacharyya distance is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method outperforms the traditional approach.
Quantum computing is a fascinating research area which promises a revolution in computing performance. Since the launch of the IBM Quantum Experience project in 2016, the research activities in this area are strongly increased. This project provides the public access to quantum processors composed of superconducting physical computing elements known as qubits. Unfortunately, qubits are sensitive to noise and, for this reason, quantum computation can be affected by errors. As a consequence, there is a strong emergence for so- called quantum error mitigation methods aimed to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error- prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms whose fitness function uses Bhattacharyya distance is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method outperforms the traditional approach.
Noisy Intermediate Scale Quantum (NISQ) devices are expected to demonstrate the real potential of quantum computing in solving hard problems. However, quantum noise that characterizes this kind of devices still remains an obstacle for their practical exploitation in real world scenarios. As a consequence, there is a strong emergence for error correction techniques aimed at making NISQ devices stable and fully operative. Unfortunately, current approaches for quantum error correction are prohibitive for NISQ devices because of the enormous multiplicative cost in resources that they require. For this reason, so-called quantum error mitigation methods are emerging as alternative approaches able to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error-prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method is comparable with or better than a conventional algebraic approach in terms of the Hellinger fidelity.
Recently, a significant interest is arising in developing techniques capable of correcting errors in noisy quantum computations without using additional quantum resources. These techniques, known as mitigation methods, are aimed at post-processing the quantum outcome without working at quantum hardware level. Among the most error-prone operations on the current quantum devices, there is surely the quantum measurement. Currently, the most popular mitigation method for quantum measurement error consists of computing a so-called mitigation matrix to be applied on the results outputted by a quantum processor to transform them and make them closer to the ideal ones. In this paper, a new measurement error mitigation method based on a competent memetic algorithm is proposed to generate an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method shows better results when compared with the conventional method and the state of the art among the evolutionary approaches.
In Phys. Rev. A 108, L060402 (2023), we introduced a Bayesian measurement error mitigation algorithm, which leveraged complete information from the readout signal, and validated the protocol on a quantum device with five superconducting qubits. Here, we present an improved algorithm's implementation, tailored for multiqubit experiments on near-term superconducting qubit quantum devices. In particular, we provide a detailed algorithm workflow, from calibrating the detector response functions to the postprocessing of measurement outcomes, offering a computationally efficient solution for the output size typical of current quantum computing devices. We show how the numerical representation of the noise function affects the performance of the error mitigation algorithm and test the convergence criteria. We benchmark our protocol on actual quantum computers with superconducting qubits, where the readout signal encodes the measurement information as unprocessed analog data before qubit state assignment. Finally, we compare the performance of our algorithm against other measurement error mitigation methods, such as iterative Bayesian unfolding and the Mthree method, and show how our method can be integrated on top of other readout error mitigation protocols.
Computational methods become essential in the face of complex problems where human intuition and traditional methods fail.Recent works present artificial neural networks capable of efficiently performing tasks intractable by conventional algorithms using machine learning, rendering it one of the most popular methods.Concomitantly, genetic algorithms, inspired by the biological processes of natural selection and mutation, have been used as a metaheuristic method to find solutions to optimization problems.We then raise the question of whether genetic algorithms have the potential to solve problems in the context of quantum computing, where human intuition decreases as physical systems grow.Specifically, we focus on the evolution of quantum error-correcting codes within the stabilizer code formalism.By specifying an appropriate fitness function, we show that we can evolve celebrated codes, such as the Perfect and Shor's code with respectively 5 and 9 qubits, in addition to new unanticipated examples.Additionally, we compared it with a brute force random search and verified an increasing superiority of the genetic algorithm as the total number of qubits increases.Given the results, we foresee that genetic algorithms can become valuable tools to perform complex applications in quantum systems and produce tailored circuits that satisfy restrictions imposed by hardware.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
In quantum computing, error mitigation is a method to improve the results of an error-prone quantum processor by post-processing them on a classical computer. In this work, we improve the General Error Mitigation (GEM) method for scalability. GEM relies on the use of a matrix to represent the device error, which requires the execution of 2n+1 calibration circuits on the quantum hardware, where n is the number of qubits. With our improved method, the number of calibration runs is independent of the number of qubits and depends only on the number of non-zero states in the output distribution. We run 1853 randomly generated circuits with widths between 2-7 qubits and depths between 10-140 gates on IBMQ superconducting devices. The experiments show that the mitigation works comparably well to GEM, while requiring a fraction of the calibration runs. Finally, an experiment to mitigate errors in a 100 qubit circuit demonstrates the scalable features of our method.