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Quantum Error Correction Fault Tolerance Quantum Machine Learning

Medusa: Detecting and Removing Failures for Scalable Quantum Computing

arXiv
Authors: Karoliina Oksanen, Quan Hoang, Alexandru Paler

Year

2025

Paper ID

16880

Status

Preprint

Abstract Read

~2 min

Abstract Words

191

Citations

N/A

Abstract

Quantum circuits will experience failures that lead to computational errors. We introduce Medusa, an automated compilation method for lowering a circuit's failure rate. Medusa uses flags to predict the absence of high-weight errors. Our method can numerically upper bound the failure rate of a circuit in the presence of flags, and fine tune the fault-tolerance of the flags in order to reach this bound. We assume the flags can have an increased fault-tolerance as a result of applying surface QECs to the gates interacting with them. We use circuit level depolarizing noise to evaluate the effectiveness of these flags in revealing the absence of the high-weight stabilizers. Medusa reduces the cost of quantum-error-correction (QEC) because the underlying circuit has a lower failure rate. We benchmark our approach using structured quantum circuits representative of ripple-carry adders. In particular, our flag scheme demonstrates that for adder-like circuits, the failure rate of large-scale implementations can be lowered to fit the failure rates of smaller-scale circuits. We show numerically that a slight improvement in the local fault-tolerance of the flag-qubits can lead to a reduction in the overall failure rate of the entire quantum circuit.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2025 reference point for readers tracking recent quantum research.
  • Quantum circuits will experience failures that lead to computational errors.

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