ADAPT: Mitigating Idling Errors in Qubits via Adaptive Dynamical Decoupling

Abstract

The fidelity of applications on near-term quantum computers is limited by hardware errors. In addition to errors that occur during gate and measurement operations, a qubit is susceptible to idling errors, which occur when the qubit is idle and not actively undergoing any operations. To mitigate idling errors, prior works in the quantum devices community have proposed Dynamical Decoupling (DD), that reduces stray noise on idle qubits by continuously executing a specific sequence of single-qubit operations that effectively behave as an identity gate. Unfortunately, existing DD protocols have been primarily studied for individual qubits and their efficacy at the application-level is not yet fully understood.Our experiments show that naively enabling DD for every idle qubit does not necessarily improve fidelity. While DD reduces the idling error-rates for some qubits, it increases the overall error-rate for others due to the additional operations of the DD protocol. Furthermore, idling errors are program-specific and the set of qubits that benefit from DD changes with each program. To enable robust use of DD, we propose Adaptive Dynamical Decoupling (ADAPT), a software framework that estimates the efficacy of DD for each qubit combination and judiciously applies DD only to the subset of qubits that provide the most benefit. ADAPT employs a Decoy Circuit, which is structurally similar to the original program but with a known solution, to identify the DD sequence that maximizes the fidelity. To avoid the exponential search of all possible DD combinations, ADAPT employs a localized algorithm that has linear complexity in the number of qubits. Our experiments on IBM quantum machines (with 16-27 qubits) show that ADAPT improves the application fidelity by 1.86x on average and up-to 5.73x compared to no DD and by 1.2x compared to DD on all qubits. CCS CONCEPTS• Computer systems organization → Quantum computing.

Pulse-level Noise Mitigation on Quantum Applications

“…Both papers evaluate DD sequences on simple benchmarks instead of real quantum applications, and there is no experiment to illustrate applying KDD to IBM quantum devices. Moreover, the experiments in [15] show that the naive implementation of DD (inserting DD sequences to all the idle qubits when it is possible) can not always improve the circuit fidelity. Therefore, Das et al proposed an Adaptive Dynamical Decoupling framework to estimate the DD impact for each circuit and adjust DD sequence to ensure it improves the circuit fidelity [15].…”

“…Moreover, the experiments in [15] show that the naive implementation of DD (inserting DD sequences to all the idle qubits when it is possible) can not always improve the circuit fidelity. Therefore, Das et al proposed an Adaptive Dynamical Decoupling framework to estimate the DD impact for each circuit and adjust DD sequence to ensure it improves the circuit fidelity [15]. This method achieves fidelity improvement but introduces a large overhead of DD impact characterization for a given application.…”

“…There is a high probability to have idle qubits during execution due to the variation of gate latencies and limited parallelism caused by the anti-commutative gates. It has been shown in [15] that the idle qubits can be almost 10 times more subject to errors if adjacent two-qubit operations are executing at the same time on IBM superconducting device. DD plays an important role in reducing the idle error and has been used in quantum volume experiments [16], noise spectrum characterization [17], and decoherenceprotected quantum gate implementation [18], etc.…”

Analyzing Strategies for Dynamical Decoupling Insertion on IBM Quantum Computer

“…(4) What conditions lead to idle qubits and what is the strategy to insert DD to idle qubits under different conditions? The first question was discussed in [17], where the authors comprehensively evaluated the performance [5]. They demonstrated that DD performs better when applied to a subset of qubits rather than all.…”

“…Several quantum software tools were developed to help design DD insertion strategies to obtain better circuit fidelity. Das et al proposed ADAPT [5], an adaptive DD framework to insert DD pulses to a subset of idle qubits. Moreover, Ravi et al introduced VAQEM [20], which dynamically selects the length of DD sequence for variational quantum algorithms.…”