Full-State Quantum Circuit Simulation by Using Data Compression

TL;DR

This paper introduces a hybrid data compression method combining lossless and tailored lossy techniques to significantly reduce memory requirements for full-state quantum circuit simulations, enabling larger simulations on supercomputers.

Contribution

The authors develop a novel hybrid compression approach with adaptive error bounds that improves quantum circuit simulation scalability by reducing memory needs while maintaining uncorrelated errors.

Findings

Reduced memory for 61-qubit simulation from 32 exabytes to 768 terabytes

Enabled simulation size increase by 2 to 16 qubits

Achieved efficient compression with controlled error bounds

Abstract

Quantum circuit simulations are critical for evaluating quantum algorithms and machines. However, the number of state amplitudes required for full simulation increases exponentially with the number of qubits. In this study, we leverage data compression to reduce memory requirements, trading computation time and fidelity for memory space. Specifically, we develop a hybrid solution by combining the lossless compression and our tailored lossy compression method with adaptive error bounds at each timestep of the simulation. Our approach optimizes for compression speed and makes sure that errors due to lossy compression are uncorrelated, an important property for comparing simulation output with physical machines. Experiments show that our approach reduces the memory requirement of simulating the 61-qubit Grover's search algorithm from 32 exabytes to 768 terabytes of memory on Argonne's Theta supercomputer using 4,096 nodes. The results suggest that our techniques can increase the simulation size by 2 to 16 qubits for general quantum circuits.