Noisy qubit devices limit the fidelity of programs executed on near-term or Noisy Intermediate Scale Quantum (NISQ) systems. The fidelity of NISQ applications can be improved by using various optimizations during prog...
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ISBN:
(纸本)9798331541279
Noisy qubit devices limit the fidelity of programs executed on near-term or Noisy Intermediate Scale Quantum (NISQ) systems. The fidelity of NISQ applications can be improved by using various optimizations during program compilation (or transpilation). These optimizations or passes are designed to minimize circuit depth (or program duration), steer more computations on devices with lowest error rates, and reduce the communication overheads involved in performing two-qubit operations between non-adjacent qubits. Additionally, standalone optimizations have been proposed to reduce the impact of crosstalk, measurement, idling, and correlated errors. However, our experiments using real IBM quantum hardware show that using all optimizations simultaneously often leads to sub-optimal performance and the highest improvement in application fidelity is obtained when only a subset of passes are used. Unfortunately, identifying the optimal pass combination is non-trivial as it depends on the application and device specific properties. In this paper, we propose COMPASS, an automated software framework for optimal Compiler Pass Selection for quantum programs. COMPASS uses dummy circuits that resemble a given program but is composed of only Clifford gates and thus, can be efficiently simulated classically to obtain its correct output. The optimal pass set for the dummy circuit is identified by evaluating the efficacy of different pass combinations and this set is then used to compile the given program. Our experiments using real IBMQ machines show that COMPASS improves the application fidelity by 4.3x on average and by upto 248.8x compared to the baseline. However, the complexity of this search scales exponential in the number of compiler steps. To overcome this drawback, we propose Efficient COMPASS (E-COMPASS) that leverages a divide-and-conquer approach to split the passes into sub-groups and exhaustively searching within each sub-group. Our evaluations show that E-COMPASS impro
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