This paper serves as an addendum to my previously published work, which delves into the experimentation with the Google Sycamore quantum processor under the title "Debating the Reliability and Robustness of the Learned Hamiltonian in the Traversable Wormhole Experiment." In the preceding publication, I extensively discussed the quantum system functioning as a dual to a traversable wormhole and the ongoing efforts to discover a sparse model that accurately depicts the dynamics of this intriguing phenomenon. In this paper, I bring to light an important insight regarding applying Nancy Cartwright's ideas about reliability and reproducibility, which are deeply rooted in classical scientific practices and experiments. I show that when applied to the realm of quantum devices, such as Google's Sycamore quantum processor and other Noisy Intermediate-Scale Quantum (NISQ) devices, these well-established notions demand careful adaptation and consideration. These systems' inherent noise and quantum nature introduce complexities that necessitate rethinking traditional perspectives on reliability and reproducibility. In light of these complexities, I propose the term "noisy reliability" as a means to effectively capture the nuanced nature of assessing the reliability of quantum devices, particularly in the presence of inherent quantum noise. This addendum seeks to enrich the discussion by highlighting the challenges and implications of assessing quantum device reliability, thereby contributing to a deeper understanding of quantum experimentation and its potential applications in various domains.
This paper serves as an addendum to my previously published work, which delves into the experimentation with the Google Sycamore quantum processor under the title "Debating the Reliability and Robustness of the Learned Hamiltonian in the Traversable Wormhole Experiment." In the preceding publication, I extensively discussed the quantum system functioning as a dual to a traversable wormhole and the ongoing efforts to discover a sparse model that accurately depicts the dynamics of this intriguing phenomenon. In this paper, I bring to light an important insight regarding applying Nancy Cartwright's ideas about reliability and reproducibility, which are deeply rooted in classical scientific practices and experiments. I show that when applied to the realm of quantum devices, such as Google's Sycamore quantum processor and other Noisy Intermediate-Scale Quantum (NISQ) devices, these well-established notions demand careful adaptation and consideration. These systems' inherent noise and quantum nature introduce complexities that necessitate rethinking traditional perspectives on reliability and reproducibility. In light of these complexities, I propose the term "noisy reliability" as a means to effectively capture the nuanced nature of assessing the reliability of quantum devices, particularly in the presence of inherent quantum noise. This addendum seeks to enrich the discussion by highlighting the challenges and implications of assessing quantum device reliability, thereby contributing to a deeper understanding of quantum experimentation and its potential applications in various domains.
Noisy, intermediate-scale quantum (NISQ) computing devices offer opportunities to test the principles of quantum computing but are prone to errors arising from various sources of noise. Fluctuations in the noise itself lead to unstable devices that undermine the reproducibility of NISQ results. Here we characterize the reliability of NISQ devices by quantifying the stability of essential performance metrics. Using the Hellinger distance, we quantify the similarity between experimental characterizations of several NISQ devices by comparing gate fidelities, duty cycles, and register addressability across temporal and spatial scales. Our observations collected over 22 months reveal large fluctuations in each metric that underscore the limited scales on which current NISQ devices may be considered reliable.
Noisy intermediate-scale quantum (NISQ) devices are valuable platforms for testing the tenets of quantum computing, but these devices are susceptible to errors arising from de-coherence, leakage, cross-talk, and other sources of noise. This raises concerns regarding the stability of results when using NISQ devices since strategies for mitigating errors generally require well-characterized and stationary error models. Here, we quantify the reliability of NISQ devices by assessing the necessary conditions for generating stable results within a given tolerance. We use similarity metrics derived from device characterization data to derive and validate bounds on the stability of a 5-qubit implementation of the Bernstein-Vazirani algorithm. Simulation experiments conducted with noise data from IBM washington, spanning January 2022 to April 2023, revealed that the reliability metric fluctuated between 41% and 92%. This variation significantly surpasses the maximum allowable threshold of 2.2% needed for stable outcomes. Consequently, the device proved unreliable for consistently reproducing the statistical mean in the context of the Bernstein-Vazirani circuit.
The recent pace of experimental progress in quantum technologies suggests we may soon reach a time when quantum devices can be applied to solve problems of practical interest. Despite this, we are still far from being able to abstract away the effects of noise on quantum computation. The reliable execution of computations on current devices remains a significant challenge. This holds true both for computation with noisy physical qubits and for computation using the limited forms of error correction available on early fault-tolerant quantum devices. In this thesis, I propose methods for improving the reliability of quantum devices, as the field transitions from the regime of noisy intermediate-scale quantum (NISQ) devices to that of early fault-tolerance. These span the topics of quantum error mitigation, quantum error correction and quantum certification. In the first instance, I propose methods for improving the performance of quantum devices affected by limited amounts of noise. Specifically, I present a method for mitigating the effects of time-dependent noise. This adapts a computational certification protocol called accreditation into a method for postselecting on weaker time-dependent noise behaviours. The method is analytically shown to lead to a provable reduction in errors for the case of depolarising noise, and demonstrated in experiments on superconducting hardware. I also present a method for mitigating the effects of photon loss in photonic quantum devices. Through a combination of analytical and numerical analysis, it is shown that there are wide regimes of photon loss for which this method outperforms postselection, the standard method of handling loss in photonic devices. Additionally, evidence is provided that the method enhances the performance of a quantum machine learning model known as a Quantum Circuit Born Machine in the presence of photon loss. Next, I introduce a framework for certifying the correctness of computations run on encoded logical qubits. This is sensitive to considerably more general forms of noise than is usually considered in quantum error correction. It can be applied to efficiently certify the correctness of quantum computations for large system sizes, beyond the capabilities of methods relying on classical simulation. In numerical experiments, the framework was applied to certify the accuracy of instantaneous quantum polynomial (IQP) circuit sampling and Trotterised Hamiltonian simulation. It can be used to efficiently assess whether quantum error mitigation may be usefully applied to a computation, to upper-bound the infidelity of the encoded logical state, and to extend the method of entropy density benchmarking to logical computation. The methods presented in this thesis contribute to the wider effort of building towards reliable quantum computation: both in the sense of reducing the effects of noise on computational output, and in the sense of certifying the correctness of computation beyond the capabilities of classical simulators. Only with reliable quantum computation can the full potential of this technology be realised.
Quantum computing has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
The advent of noisy intermediate-scale quantum (NISQ) devices has not only opened up new possibilities to studying quantum computational benefits, but it has also presented new challenges associated with noise and short coherence times. Entanglement is one of the inherent resources that can be used to perform quantum computation and implement the performance and reliability of quantum algorithms. This paper looks at entanglement dynamics in NISQ machines subjected to natural noises. The study focuses on the problem of examining the role of various noise channels in the creation, dynamics, and decay of the entanglement during quantum operations, such as depolarizing, amplitude damping, and phase damping. The study is based on theoretical modeling and numerical simulations of multi-qubit quantum circuits to determine the stability of entangled states with respect to circuit depth and intensity of noise. Concurrence and von Neumann entropy are examples of entanglement measures that are used to characterize correlations between qubits as well as to trace the dynamics of these correlations across computational interactions. The results show that noise has major impacts on entanglement distribution, which tends to cause rapid decays in highly entangled states with increase in circuit complexity. The findings, however, also show that there are some circuit architectures and error-mitigation schemes that can somewhat sustain entanglement with moderate levels of noise. The paper also emphasizes the relevance of minimalizing sequence of gates, and reduced circuit depth in an effort to preserve useful entanglement in present-day quantum hardware. These observations help in understanding the behaviour of quantum correlations better in real world quantum devices and provide a way of developing more resilient quantum algorithms that would work on NISQ systems. In general, the article offers a systematic view on entanglement behavior in disordered quantum systems and highlights its value in the development of quantum computing applications in the near term.
In recent years, Noisy Intermediate Scale Quantum (NISQ) computers have been widely used as a test bed for quantum dynamics. This work provides a new hardware-agnostic framework for modelling the Markovian noise and dynamics of quantum systems in benchmark procedures used to evaluate device performance. As an accessible example, the application and performance of this framework is demonstrated on IBM Quantum computers. This framework serves to extract multiple calibration parameters simultaneously through a simplified process which is more reliable than previously studied calibration experiments and tomographic procedures. Additionally, this method allows for real-time calibration of several hardware parameters of a quantum computer within a comprehensive procedure, providing quantitative insight into the performance of each device to be accounted for in future quantum circuits. The framework proposed here has the additional benefit of highlighting the consistency among qubit pairs when extracting parameters, which leads to a less computationally expensive calibration process than evaluating the entire device at once.