Understanding the Impact of Cutting in Quantum Circuits Reliability to Transient Faults

Quantum Computing is a highly promising new computation paradigm. Unfortunately, quantum bits (qubits) are extremely fragile and their state can be gradually or suddenly modified by intrinsic noise or external perturbation. In this paper, we target the sensitivity of quantum circuits to radiation-induced transient faults. We consider quantum circuit cuts that split the circuit into smaller independent portions, and understand how faults propagate in each portion. As we show, the cuts have different vulnerabilities, and our methodology successfully identifies the circuit portion that is more likely to contribute to the overall circuit error rate. Our evaluation shows that a circuit cut can have a 4.6x higher probability than the other cuts, when corrupted, to modify the circuit output. Our study, identifying the most critical cuts, moves towards the possibility of implementing a selective hardening for quantum circuits

Understanding the Effect of Transpilation in the Reliability of Quantum Circuits

Transpiling is a necessary step to map a logical quantum algorithm to a circuit executed on a physical quantum machine, according to the available gate set and connectivity topology. Different transpiling approaches try to minimize the most critical parameters for the current transmon technology, such as Depth and CNOT number. Crucially, these approaches do not take into account the reliability of the circuit. In particular, transpilation can modify how radiation-induced transient faults propagate. In this paper, we aim at advancing the understanding of transpilation impact on fault propagation by investigating the low-level reliability of several transpiling approaches. We considered 4 quantum algorithms transpiled for 2 different architectures, increasing the number of qubits, and all possible logical-to-physical qubit mapping, adding to a total of 4, 640 transpiled circuits. We inject a total of 202, 124 faults and track their propagation. Our experiments show that by simply choosing the proper transpilation, the reliability of the circuit can improve by up to 14%

QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Quantum computing is a new technology that is expected to revolutionize the computation paradigm in the next few years. Qubits exploit the quantum physics proprieties to increase the parallelism and speed of computation. Unfortunately, besides being intrinsically noisy, qubits have also been shown to be highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. We propose a framework to identify the quantum circuits sensitivity to radiation-induced faults and the probability for a fault in a qubit to propagate to the output. Based on the latest studies and radiation experiments performed on real quantum machines, we model the transient faults in a qubit as a phase shift with a parametrized magnitude. Additionally, our framework can inject multiple qubit faults, tuning the phase shift magnitude based on the proximity of the qubit to the particle strike location. As we show in the paper, the proposed fault injector is highly flexible, and it can be used on both quantum circuit simulators and real quantum machines. We report the finding of more than 285M injections on the Qiskit simulator and 53K injections on real IBM machines. We consider three quantum algorithms and identify the faults and qubits that are more likely to impact the output. We also consider the fault propagation dependence on the circuit scale, showing that the reliability profile for some quantum algorithms is scale-dependent, with increased impact from radiation-induced faults as we increase the number of qubits. Finally, we also consider multi qubits faults, showing that they are much more critical than single faults. The fault injector and the data presented in this paper are available in a public repository to allow further analysis

Agreement on classification of clinical photographs of pigmentary lesions: exercise after a training course with young dermatologists.

Smartphone apps may help promoting the early diagnosis of melanoma. The reliability of specialist judgment on lesions should be assessed. Hereby, we evaluated the agreement of 6 young dermatologists, after a specific training. Clinical judgment was evaluated during 2 online sessions, 1 month apart, on a series of 45 pigmentary lesions. Lesions were classified as highly suspicious, suspicious, non-suspicious or not assessable. Cohen's and Fleiss' kappa were used to calculate intra- and inter-rater agreement. The overall intra-rater agreement was 0.42 (95% confidence interval - CI: 0.33-0.50), varying between 0.12-0.59 on single raters. The inter-rater agreement during the first phase was 0.29 (95% CI: 0.24-0.34). When considering the agreement for each category of judgment, kappa varied from 0.19 for not assessable to 0.48 for highly suspicious lesions. Similar results were obtained in the second exercise. The study showed a less than satisfactory agreement among young dermatologists. Our data point to the need for improving the reliability of the clinical diagnoses of melanoma especially when assessing small lesions and when dealing with thin melanomas at a population level

Towards practical Quantum Credit Risk Analysis

In recent years, a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio's asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model's inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented

A More General Quantum Credit Risk Analysis Framework

Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector

Quantum Computing Reliability: Problems, Tools, and Potential Solutions

Quantum computing is a new computational paradigm, expected to revolutionize the computing field in the next few years. Qubits, the atomic units of a quantum circuit, exploit the quantum physics properties to increase the parallelism and speed of computation. Unfortunately, qubits are both intrinsically noisy and highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. The observed error rate is so high that researchers are questioning the large-scale adoption of quantum computers. The reliability and dependability community is asked to act to find innovative solutions to improve the reliability of quantum applications. This tutorial aims at providing the DSN community with the tools to do so and to train the attendees on quantum fault injection

Towards An End-To-End Approach For Quantum Principal Component Analysis

Quantum Machine Learning has gained significant attention in recent years as a way to leverage the relationship between quantum information and machine learning. Principal Component Analysis (PCA) is a fundamental technique in machine learning, and the potential for its quantum acceleration has been extensively studied. However, an algorithmic end-to-end implementation remains challenging. This paper covers quantum PCA implementation up to extracting the principal components. We extend existing processes for quantum state tomography to extract the eigenvectors from the output state, addressing the challenges of dealing with complex amplitudes in the case of non-integer eigenvalues. Finally, we apply our implementation to a practical quantum finance use case related to interest rate risk, and present the results of our experiments