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%.
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%.
Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.
Quantum computing is an up-and-coming technology that is expected to revolutionize the computation paradigm in the next few years. Qubits, the primary computing elements of quantum circuits, exploit the quantum physics proprieties to increase the parallelism and speed of computation drastically. 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 285, 249, 536 injections on the Qiskit simulator and 53, 248 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.
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 4.6 x 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.
Abstract Current quantum computers are noisy intermediate-scale quantum (NISQ) devices. Execution of quantum algorithms, represented as quantum circuits in the quantum circuit model, on such hardware requires transformation of the original circuits to comply with the hardware’s physical constraints, a process known as transpilation. One of the main constraints is limited qubit connectivity. In this work, we present an algorithm that transforms a quantum hashing circuit to satisfy architectural connectivity constraints while minimizing the number of CNOT gates (CNOT cost). The considered algorithm constructs a mapping from logical qubits to physical qubits that minimizes the CNOT cost of the resulting circuit. We optimize circuits for IBM Quantum devices.
Abstract Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many open-source quantum programs are affected by quantum-specific code smells, with long circuit being the most common. While the study provided relevant insights into the prevalence of code smells in quantum circuits, it did not explore the potential effect of transpilation, a necessary step for executing quantum computer programs, on the emergence of code smells. Indeed, transpilation might alter those characteristics employed to detect the presence of a smell on a circuit. To address this limitation, we present a new study investigating the impact of transpilation on quantum-specific code smells and how different target gate sets affect the results. We conducted experiments on 17 open-source quantum programs alongside a set of 100 synthetic circuits. We found that transpilation can significantly alter the metrics that are used to detect code smells, even into previously smell-free circuits, with the long circuit smell being the most susceptible to transpilation. Furthermore, the choice of the gate set significantly influences the presence and severity of code smells in transpiled circuits, highlighting the need for careful gate set selection to mitigate their impact. These findings have implications for circuit optimization and high-quality quantum software development. Further research is needed to understand the consequences of code smells and their potential impact on quantum computations, considering the characteristics and constraints of different gate sets and hardware platforms.
Transpilation is a crucial process in preparing quantum circuits for execution on hardware, transforming virtual gates to match device-specific topology by introducing swap gates and basis gates, and applying optimizations that reduce circuit depth and gate count, particularly for two-qubit gates. As the number of qubits increases, the cost of transpilation escalates significantly, especially when trying to find the optimal layout with minimal noise under the qubit connectivity constraints imposed by device topology. In this work, we use the Rivet transpiler, which accelerates transpilation by reusing previously transpiled circuits. This approach is relevant for cases such as quantum chemistry, where multiple Pauli terms need to be measured by appending a series of rotation gates at the end for non-commuting Paulis, and for more complex cases when quantum circuits need to be modified iteratively, as occurs in quantum layerwise learning. We demonstrate up to 600% improvement in transpilation time for quantum layerwise learning using the Rivet transpiler compared to standard transpilation without reuse.