Software Techniques to Mitigate Errors on Noisy Quantum Computers

Quantum computers are domain-specific accelerators that can provide a large speedup for important problems. Quantum computers with few tens of qubits have already been demonstrated, and machines with 100+ qubits are expected soon. These machines face significant reliability and scalability challenges. The high hardware error rates limit quantum computers. To enable quantum speedup, it is essential to mitigate hardware errors. Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid error-prone qubits. Our second work looks at executing different versions of the programs tuned to cause diverse mistakes so that the machine is less vulnerable to correlated errors, thereby making it easier to infer the correct answer. Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to measure the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability.Ph.D

Quantum Vulnerability Analysis to Guide Robust Quantum Computing System Design

While quantum computers provide exciting opportunities for information processing, they currently suffer from noise during computation that is not fully understood. Incomplete noise models have led to discrepancies between quantum program success rate (SR) estimates and actual machine outcomes. For example, the estimated probability of success (ESP) is the state-of-the-art metric used to gauge quantum program performance. The ESP suffers poor prediction since it fails to account for the unique combination of circuit structure, quantum state, and quantum computer properties specific to each program execution. Thus, an urgent need exists for a systematic approach that can elucidate various noise impacts and accurately and robustly predict quantum computer success rates, emphasizing application and device scaling. In this article, we propose quantum vulnerability analysis (QVA) to systematically quantify the error impact on quantum applications and address the gap between current success rate (SR) estimators and real quantum computer results. The QVA determines the cumulative quantum vulnerability (CQV) of the target quantum computation, which quantifies the quantum error impact based on the entire algorithm applied to the target quantum machine. By evaluating the CQV with well-known benchmarks on three 27-qubit quantum computers, the CQV success estimation outperforms the estimated probability of success state-of-the-art prediction technique by achieving on average six times less relative prediction error, with best cases at 30 times, for benchmarks with a real SR rate above 0.1%. Direct application of QVA has been provided that helps researchers choose a promising compiling strategy at compile time