Simulating quantum dynamics: Evolution of algorithms in the HPC context

Abstract

Due to complexity of the systems and processes it addresses, the development of computational quantum physics is influenced by the progress in computing technology. Here we overview the evolution, from the late 1980s to the current year 2020, of the algorithms used to simulate dynamics of quantum systems. We put the emphasis on implementation aspects and computational resource scaling with the model size and propagation time. Our minireview is based on a literature survey and our experience in implementing different types of algorithms.

Qibo: a framework for quantum simulation with hardware acceleration

“…The simulation of circuit-based quantum processors is already implemented by several research collaborations and companies. Some notable examples of simulation software which are based on linear algebra approach are Cirq [19] and TensorFlow quantum (TFQ) [20] from Google, Qiskit from IBM Q [21], PyQuil from Rigetti [22], Intel-QS (qHipster) from Intel [23], QCGPU [24] and Qulacs [25], among others [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. While the simulation techniques and hardware-specific configurations are well defined for each simulation software, despite the availability of recent implementations based on field programmable gate arrays [46,47], there are no simulation tools that can take full advantage of hardware acceleration in single and double precision computations, through a simple interface which allows the user to switch from multithreading CPU, single GPU, and distributed multi-GPU/CPU setups.…”

Qibo: a framework for quantum simulation with hardware acceleration

“…The simulation of circuit-based quantum processors is already implemented by several research collaborations and companies. Some notable examples of simulation software which are based on linear algebra approach are Cirq [19] and TensorFlow Quantum (TFQ) [20] from Google, Qiskit from IBM Q [21], PyQuil from Rigetti [22], Intel-QS (qHipster) from Intel [23] , QCGPU [24] and Qulacs [25], among others [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. While the simulation techniques and hardware-specific configurations are well defined for each simulation software, there are no simulation tools that can take full advantage of hardware acceleration in single and double precision computations, through a simple interface which allows the user to switch from multithreading CPU, single GPU, and distributed multi-GPU/CPU setups.…”