Quantum Circuit Cutting for Classical Shadows
arXiv:2212.00761 · doi:10.1145/3665335
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.
arXiv commentgeneral edits and added analysis for specific circuit ansatz. ACM Transactions on Quantum Computing (May 2024)
References in corpus (17)
- Predicting Many Properties of a Quantum System from Very Few Measurements
- Symmetric Informationally Complete Quantum Measurements
- Quantum advantage in learning from experiments
- Simulating Large Quantum Circuits on a Small Quantum Computer
- PennyLane: Automatic differentiation of hybrid quantum-classical computations
- Efficient estimation of Pauli observables by derandomization
- Fermionic partial tomography via classical shadows
- CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations
- Avoiding barren plateaus using classical shadows
- Classical Shadows With Noise
- Quantum Circuit Cutting with Maximum Likelihood Tomography
- Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors
- Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers
- A Bayesian analysis of classical shadows
- Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
- Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations
- Foundations for learning from noisy quantum experiments