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.
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.
Abstract We present Qibo , a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading Central Processing Unit (CPU), single Graphics Processing Unit (GPU) and multi-GPU devices.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics, enabling computers to process information in fundamentally different ways than classical computers. Unlike traditional bits, which represent data as either 0s or 1s, quantum bits, or qubits, can exist in multiple states simultaneously due to superposition. Additionally, qubits can be entangled, allowing them to be interconnected in ways that enhance computational power and efficiency. This unique behavior has the potential to run certain types of computation tasks much faster than classical computers can, marking a significant leap forward in technology. When building actual quantum computers to run practical quantum algorithms, one needs efficient ways to map the algorithms to the hardware components, similar to compiling classical algorithms onto CPU/GPU hardware. This thesis aims to tackle some of the important problems in the mapping process. First, can we address hardware errors and algorithm errors at the same time? Second, can we build a better circuit complexity metric to inspire better quantum architecture design, and can we design better quantum algorithms and compilation techniques using this improved metric? Lastly, can we use physics-level simulation results to inspire better circuit-level optimization techniques? We hope to provide positive answers to all three questions above through three separate architectural innovations.
In recent years, quantum computing has undergone significant developments and has established its supremacy in many application domains. While quantum hardware is accessible to the public through the cloud environment, a robust and efficient quantum circuit simulator is necessary to investigate the constraints and foster quantum computing development, such as quantum algorithm development and quantum device architecture exploration. In this paper, we observe that most of the publicly available quantum circuit simulators (e.g., QISKit from IBM, QDK from Microsoft, and Qsim-Cirq from Google) suffer from slow simulation and poor scalability when the number of qubits increases. To this end, we systematically investigate the deficiencies in quantum circuit simulation (QCS) and propose Q-GPU, a framework that leverages GPUs with comprehensive optimizations to allow efficient and scalable QCS. Specifically, Q-GPU features i) proactive state amplitude transfer, ii) zero state amplitude pruning, iii) delayed qubit involvement, and iv) lossless nonzero state amplitude compression. Experimental results across nine representative quantum circuits indicate that Q-GPU significantly reduces the execution time of the state-of-the-art GPU-based QCS by 71.89% (3.55× speedup). Q-GPU also outperforms the state-of-the-art OpenMP CPU implementation, the Google Qsim-Cirq simulator, and the Microsoft QDK simulator by 1.49×, 2.02×, and 10.82×, respectively.
Quantum simulation plays a critical role in advancing our understanding and development of quantum algorithms, quantum computing hardware, and quantum information science. Despite the availability of various quantum circuit simulators, they often face challenges in terms of maintainability and extensibility. In this paper, we introduce Lightweight Functional Quantum Simulator (LFQS), a compilation-based framework that addresses these issues by leveraging an intermediate language to synthesize efficient quantum simulators in C and CUDA. The intermediate language exploits the underlying structured sparsity behind the matrix representation of quantum gates. Our framework generates efficient code for multi-core CPUs and GPUs and outperforms state-of-the-art simulators, such as Qiskit, Catalyst, and QuEST, as well as a representative of dense tensor frameworks, NumPy, in both micro-benchmark and macro-benchmark circuits.
QASMTrans is a lightweight, high-performance, C++-based quantum compiler that bridges abstract quantum algorithms to device-level control and is designed for just-in-time (JIT) deployment on QPU testbeds with tightly integrated FPGAs or CPUs. We focus on achieving fast transpilation times on circuits of interest, we find more than 100x faster compilation than Qiskit in some circuits with similar circuit quality, enabling transpilation of large, high-depth circuits in seconds. Unlike existing tools, QASMTrans offers end-to-end device-pulse compilation and direct quantum control integration with QICK, closing the gap between logical circuits and hardware control enabling closed-loop optimization. QASMTrans supports latency-aware Application-tailored Gate Sets (AGS) at the pulse level, identifying high-impact gate sequences on the circuit critical path and synthesizing optimized pulse schedules using pre-defined robust circuit ansatz. Validated through integrated QuTiP pulse-level simulation, this is found to significantly reduce execution latency and can improve final-state fidelity by up to 12% in some tested circuits. QASMTrans further implements device-aware, noise-adaptive transpilation that uses device calibration data for circuit placement on high-quality qubits and can focus on the circuit critical path to reduce transpilation-pass time while maintaining comparable fidelity. Additionally, it introduces circuit space sharing via calibration-aware device partitioning, enabling concurrent execution of multiple circuits or shots on a single QPU. Moreover, QASMTrans is entirely self-contained and has no external library dependencies, making it easy for practical deployment. By combining fast compilation, pulse-level control, and noise-aware optimization, QASMTrans enables real-time adaptive algorithms such as ADAPT-VQE and ADAPT-QAOA.