Quantum Circuit Cutting for Classical Shadows

Abstract

Classical shadow tomography is a sample-efficient technique for characterizing quantum systems and predicting many of their properties. Circuit cutting is a technique for dividing large quantum circuits into smaller fragments that can be executed more robustly using fewer quantum resources. We introduce a divide-and-conquer circuit cutting method for estimating the expectation values of observables using classical shadows. We derive a general formula for making predictions using the classical shadows of circuit fragments from arbitrarily cut circuits, and provide the sample complexity analysis for the case when observables factorize across fragments. Then, we numerically show that our divide-and-conquer method outperforms traditional uncut shadow tomography when estimating high-weight observables that act non-trivially on many qubits, and discuss the mechanisms for this advantage.

general edits and added analysis for specific circuit ansatz. ACM Transactions on Quantum Computing (May 2024)

References in corpus (6)

• Quantum advantage in learning from experiments
• CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
• Avoiding barren plateaus using classical shadows
• Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors
• Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations
• Foundations for learning from noisy quantum experiments

Cited by in corpus (2)

• Resource-efficient Generalized Quantum Subspace Expansion
• Classical Shadows with Improved Median-of-Means Estimation