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1. VAQEM: A Variational Approach to Quantum Error Mitigation

   https://doi.org/10.1109/HPCA53966.2022.00029  

   Ravi, Gokul Subramanian; Smith, Kaitlin N.; Gokhale, Pranav; Mari, Andrea; Earnest, Nathan; Javadi-Abhari, Ali; Chong, Frederic T. (April 2022, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA))

   Variational Quantum Algorithms (VQA) are one of the most promising candidates for near-term quantum advantage. Traditionally, these algorithms are parameterized by rotational gate angles whose values are tuned over iterative execution on quantum machines. The iterative tuning of these gate angle parameters make VQAs more robust to a quantum machine’s noise profile. However, the effect of noise is still a significant detriment to VQA’s target estimations on real quantum machines — they are far from ideal. Thus, it is imperative to employ effective error mitigation strategies to improve the fidelity of these quantum algorithms on near-term machines.While existing error mitigation techniques built from theory do provide substantial gains, the disconnect between theory and real machine execution characteristics limit the scope of these improvements. Thus, it is critical to optimize mitigation techniques to explicitly suit the target application as well as the noise characteristics of the target machine.We propose VAQEM, which dynamically tailors existing error mitigation techniques to the actual, dynamic noisy execution characteristics of VQAs on a target quantum machine. We do so by tuning specific features of these mitigation techniques similar to the traditional rotation angle parameters -by targeting improvements towards a specific objective function which represents the VQA problem at hand. In this paper, we target two types of error mitigation techniques which are suited to idle times in quantum circuits: single qubit gate scheduling and the insertion of dynamical decoupling sequences. We gain substantial improvements to VQA objective measurements — a mean of over 3x across a variety of VQA applications, run on IBM Quantum machines.More importantly, while we study two specific error mitigation techniques, the proposed variational approach is general and can be extended to many other error mitigation techniques whose specific configurations are hard to select a priori. Integrating more mitigation techniques into the VAQEM framework in the future can lead to further formidable gains, potentially realizing practically useful VQA benefits on today’s noisy quantum machines. 

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2. Neural Network Enhanced Robustness for Noisy Quantum Applications

   https://doi.org/10.1109/ICRC64395.2024.10937016  

   Breslawski, Daniel; Crimmins, Aden; Alarcon, Sonia Lopez_Alarcon (December 2024, IEEE)

   Due to the limitations of current NISQ systems, error mitigation strategies are under development to alleviate the negative effects of error-inducing noise on quantum applications. This work proposes the use of machine learning (ML) as an error mitigation strategy, using ML to identify the accurate solutions to a quantum application in the presence of noise. Methods of encoding the probabilistic solution space of a basis-encoded quantum algorithm are researched to identify the characteristics which represent good ML training inputs. A multilayer perceptron artificial neural network (MLP ANN) was trained on the results of 8-state and 16-state basis-encoded quantum applications both in the presence of noise and in noise-free simulation. It is demonstrated using simulated quantum hardware and probabilistic noise models that a sufficiently trained model may identify accurate solutions to a quantum applications with over 90% precision and 80% recall on select data. The model makes confident predictions even with enough noise that the solutions cannot be determined by direct observation, and when it cannot, it can identify the inconclusive experiments as candidates for other error mitigation techniques. 

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3. Strategies for Deploying Unreliable AI Graders in High-Transparency High-Stakes Exams

   https://doi.org/10.1007/978-3-030-52237-7  

   Azad, Sushmita; Chen, Binglin; Fowler, Maxwell; West, Matthew; Zilles, Craig (June 2020, International Conference on Artificial Intelligence in Education)

   null (Ed.)

   We describe the deployment of an imperfect NLP-based automatic short answer grading system on an exam in a large-enrollment introductory college course. We characterize this deployment as both high stakes (the questions were on an mid-term exam worth 10% of students’ final grade) and high transparency (the question was graded interactively during the computer-based exam and correct solutions were shown to students that could be compared to their answer). We study two techniques designed to mitigate the potential student dissatisfaction resulting from students incorrectly not granted credit by the imperfect AI grader. We find (1) that providing multiple attempts can eliminate first-attempt false negatives at the cost of additional false positives, and (2) that students not granted credit from the algorithm cannot reliably determine if their answer was mis-scored. 

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4. Performance of Quantum Preprocessing under Phase Noise

   https://doi.org/10.1109/GCWkshps56602.2022.10008728  

   Amiri, Zuhra; Bash, Boulat A.; Notzel, Janis (December 2022, 2022 IEEE Globecom Workshops (GC Wkshps))

   Optical fiber transmission systems form the back-bone of today’s communication networks and will be of high importance for future networks as well. Among the prominent noise effects in optical fiber is phase noise, which is induced by the Kerr effect. This effect limits the data transmission capacity of these networks and incurs high processing load on the receiver. At the same time, quantum information processing techniques offer more efficient solutions but are believed to be inefficient in terms of size, power consumption and resistance to noise. Here we investigate the concept of an all-optical joint detection receiver. We show how it contributes to enabling higher baud-rates for optical transmission systems when used as a pre-processor, even under high levels of noise induced by the Kerr effect. 

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5. Law of large numbers and central limit theorem for ergodic quantum processes

   https://doi.org/10.1063/5.0153483  

   Pathirana, Lubashan; Schenker, Jeffrey (August 2023, Journal of Mathematical Physics)

   A discrete quantum process is represented by a sequence of quantum operations, which are completely positive maps that are not necessarily trace preserving. We consider quantum processes that are obtained by repeated iterations of a quantum operation with noise. Such ergodic quantum processes generalize independent quantum processes. An ergodic theorem describing convergence to equilibrium for a general class of such processes has been recently obtained by Movassagh and Schenker. Under irreducibility and mixing conditions, we obtain a central limit type theorem describing fluctuations around the ergodic limit. 

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