Quantum Divide and Compute: Hardware Demonstrations and Noisy Simulations

Abstract

Noisy, intermediate-scale quantum computers come with intrinsic limitations in terms of the number of qubits (circuit "width") and decoherence time (circuit "depth") they can have. Here, for the first time, we demonstrate a recently introduced method that breaks a circuit into smaller subcircuits or fragments, and thus makes it possible to run circuits that are either too wide or too deep for a given quantum processor. We investigate the behavior of the method on one of IBM's 20-qubit superconducting quantum processors with various numbers of qubits and fragments. We build noise models that capture decoherence, readout error, and gate imperfections for this particular processor. We then carry out noisy simulations of the method in order to account for the observed experimental results. We find an agreement within 20% between the experimental and the simulated success probabilities, and we observe that recombining noisy fragments yields overall results that can outperform the results without fragmentation.

arXiv commentAccepted in ISVLSI 2020

References in corpus (7)

• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Superconducting Qubits: Current State of Play
• Trapped-Ion Quantum Computing: Progress and Challenges
• Validating quantum computers using randomized model circuits
• Distance measures to compare real and ideal quantum processes
• Simulating Large Quantum Circuits on a Small Quantum Computer

Cited by in corpus (12)

• Fast quantum circuit cutting with randomized measurements
• Quantum Circuit Cutting with Maximum Likelihood Tomography
• Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
• Quantum Divide and Compute: Exploring The Effect of Different Noise Sources
• Cutting multi-control quantum gates with ZX calculus
• Simulating noisy variational quantum eigensolver with local noise models
• $i$-QER: An Intelligent Approach towards Quantum Error Reduction
• Quantum Model Learning Agent: characterisation of quantum systems through machine learning
• Divide-and-conquer verification method for noisy intermediate-scale quantum computation
• QuTracer: Mitigating Quantum Gate and Measurement Errors by Tracing Subsets of Qubits
• Quantum Circuit Cutting for Classical Shadows
• Distributing Quantum Computations, Shot-wise