In this work, we present some of the latest efforts made at Huawei Research in order to improve the overall performance of ProjectQ, the quantum computing framework used as the foundation of our quantum research. Since a few years, performance assessment of the framework using profiling tools has shown that a significant portion of the compilation time is spent in doing memory management linked to the lifetime and access of Python objects. The main purpose of this work is therefore aimed at addressing some of these limitations by introducing a new C++ processing backend for ProjectQ, as well as starting a complete rewrite of the simulator code already written in C++. The core of this work is centered around on a new C++ API for ProjectQ that moves most of the compiler processing into natively compiled code. We achieve this by providing a new compiler engine to perform the conversion from Python to C++, which ensures that we retain maximum compatibility with existing user code while providing significant speed-ups. We also introduce some of our work aimed at porting the existing C++ code to offload the more demanding calculations onto GPUs for better performance. We then investigate the performance of this new API by comparing it with the original ProjectQ implementation, as well as some other existing quantum computing frameworks. The preliminary results show that the C++ API is able to considerably reduce the cost of compilation, to the point that the compilation process becomes mostly limited by the performance of the simulator. Unfortunately, due to the some last minutes developments and the time frame required for the submission of the present manuscript, we are unable to provide conclusive benchmark results for the GPU-capable implementation of the simulator. These will most likely be presented during the keynote presentation at the DATE conference in 2021.
In this work, we present some of the latest efforts made at Huawei Research in order to improve the overall performance of ProjectQ, the quantum computing framework used as the foundation of our quantum research. Since a few years, performance assessment of the framework using profiling tools has shown that a significant portion of the compilation time is spent in doing memory management linked to the lifetime and access of Python objects. The main purpose of this work is therefore aimed at addressing some of these limitations by introducing a new C++ processing backend for ProjectQ, as well as starting a complete rewrite of the simulator code already written in C++. The core of this work is centered around on a new C++ API for ProjectQ that moves most of the compiler processing into natively compiled code. We achieve this by providing a new compiler engine to perform the conversion from Python to C++, which ensures that we retain maximum compatibility with existing user code while providing significant speed-ups. We also introduce some of our work aimed at porting the existing C++ code to offload the more demanding calculations onto GPUs for better performance. We then investigate the performance of this new API by comparing it with the original ProjectQ implementation, as well as some other existing quantum computing frameworks. The preliminary results show that the C++ API is able to considerably reduce the cost of compilation, to the point that the compilation process becomes mostly limited by the performance of the simulator. Unfortunately, due to the some last minutes developments and the time frame required for the submission of the present manuscript, we are unable to provide conclusive benchmark results for the GPU-capable implementation of the simulator. These will most likely be presented during the keynote presentation at the DATE conference in 2021.
We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.
Quantum technologies are moving towards the development of novel hardware devices based on quantum bits (qubits). In parallel to the development of quantum devices, efficient simulation tools are needed in order to design and benchmark quantum algorithms and applications before deployment on quantum hardware. In this context, we present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique on multiple hardware architectures and configurations based on single-node central processing units (CPUs) and graphics processing units (GPUs). One of the major challenges in scientific code development is to balance the level of complexity between algorithms and programming techniques without losing performance or degrading code readability. In this context, we have developed qibojit : a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python. We perform systematic performance benchmarks between our JIT approach and a subset of relevant publicly available libraries for quantum computing. We show that our novel approach simplifies the complex aspects of the implementation without deteriorating performance.
Exponential increase and global access to read/write memory states in quantum computing (QC) simulation limit both the number of qubits and quantum transformations which can be currently simulated. Although QC simulation is parallel by nature, spatial and temporal complexity are major performance hazards, making this a nontrivial application for high performance computing. A new methodology employing reduction and decomposition optimizations has shown relevant results, but its GPU implementation could be further improved. In this work, we develop a new kernel for in situ GPU simulation that better explores its resources without requiring further hardware. Shor’s and Grover’s algorithms are simulated up to 25 and 21 qubits respectively and compared to our previous version, to [Formula: see text] simulator and to ProjectQ framework, showing better results with relative speedups up to 4.38×, 3357.76× and 333× respectively.
Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum Computer platforms, Circuit Simulator, Open-source Tools- Cirq, TensorFlow Quantum, ProjectQ etc. that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges, and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
The ongoing rise of interest in quantum computing (QC) is fueling considerable advances in quantum algorithms, as well as work on accelerating their simulation before sufficiently-capable QC prototypes are available. Since classical state-vector simulation time of QC systems scales exponentially with the number of qubits, QC simulator performance plays a significant role in algorithm and systems development. This paper provides a systems-level performance characterization of NVIDIA's CUDA Quantum (CUDA-Q) package, across several key metrics, on multiple GPU types. Our goal is to identify patterns or limitations in the implementation that may suggest future optimization opportunities. Our characterization also helps users on mid-scale GPU clusters plan likely runtime trajectories with problem size.11Professor Martonosi's research is supported in part by the US Department of Energy and the National Science Foundation.
To address the growing needs for scalable High Performance Computing (HPC) and Quantum Computing (QC) integration, we present our HPC-QC full stack framework and its hybrid workload development capability with modular hardware/device-agnostic software integration approach. The latest development in extensible interfaces for quantum programming, dispatching, and compilation within existing mature HPC programming environment are demonstrated. Our HPC-QC full stack enables high-level, portable invocation of quantum kernels from commercial quantum SDKs within HPC meta-program in compiled languages (C/C++ and Fortran) as well as Python through a quantum programming interface library extension. An adaptive circuit knitting hypervisor is being developed to partition large quantum circuits into sub-circuits that fit on smaller noisy quantum devices and classical simulators. At the lower-level, we leverage Cray LLVM-based compilation framework to transform and consume LLVM IR and Quantum IR (QIR) from commercial quantum software frontends in a retargetable fashion to different hardware architectures. Several hybrid HPC-QC multi-node multi-CPU and GPU workloads (including solving linear system of equations, quantum optimization, and simulating quantum phase transitions) have been demonstrated on HPE EX supercomputers to illustrate functionality and execution viability for all three components developed so far. This work provides the framework for a unified quantum-classical programming environment built upon classical HPC software stack (compilers, libraries, parallel runtime and process scheduling).