Quantum computing is rapidly evolving especially after the discovery of several efficient quantum algorithms solving intractable classical problems such as Shor's factoring algorithm. However the realization of a large-scale physical quantum computer is very challenging and the number of qubits that are currently under development is still very low, namely less than 15. In the absence of large size platforms, quantum computer simulation is critical for developing and testing quantum algorithms and investigating the different challenges facing the design of quantum computer hardware. What makes quantum computer simulation on classical computers particularly challenging are the memory and computational resource requirements. In this paper, we introduce a universal quantum computer simulator, called QX, that takes as input a specially designed quantum assembly language, called QASM, and provides, through agressive optimisations, high simulation speeds and large number of qubits. QX allows the simulation of up to 34 fully entangled qubits on a single node using less than 270 GB of memory. Our experiments using different quantum algorithms show that QX achieves significant simulation speedup over similar state-of-the-art simulation environment.
Quantum computing is rapidly evolving especially after the discovery of several efficient quantum algorithms solving intractable classical problems such as Shor's factoring algorithm. However the realization of a large-scale physical quantum computer is very challenging and the number of qubits that are currently under development is still very low, namely less than 15. In the absence of large size platforms, quantum computer simulation is critical for developing and testing quantum algorithms and investigating the different challenges facing the design of quantum computer hardware. What makes quantum computer simulation on classical computers particularly challenging are the memory and computational resource requirements. In this paper, we introduce a universal quantum computer simulator, called QX, that takes as input a specially designed quantum assembly language, called QASM, and provides, through agressive optimisations, high simulation speeds and large number of qubits. QX allows the simulation of up to 34 fully entangled qubits on a single node using less than 270 GB of memory. Our experiments using different quantum algorithms show that QX achieves significant simulation speedup over similar state-of-the-art simulation environment.
Quantum computation is becoming an increasingly interesting field, especially with the rise of real quantum computers. However, current quantum processors contain a few tens of error-prone qubits and the realization of large-scale quantum computers is still very challenging. Therefore, quantum computer simulators are particularly suitable for testing and analysing quantum algorithms without having a real quantum computer at one's disposal. In this thesis, different quantum algorithms such as Grover's and Shor's algorithm as well as key quantum routines such as the Quantum Fourier Transform (QFT) and a quantum adder/subtractor are described and analysed (optimal number of iterations, time complexity). Some of them have been implemented for an arbitrary number of qubits and have been simulated using two different quantum simulators, the QX simulator developed at QuTech and the Liquid simulator from Microsoft. In addition, how errors affect the success rate of the algorithms has been investigated.
Quantum computing has the potential to revolutionise multiple fields by solving complex problems that cannot be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. In addition, the development of new quantum computers and their components also depends on simulations. Given the characteristics of a quantum computer, their simulation is a very demanding application in terms of both computation and memory. As such, simulations do not scale well in current classical systems. Thus different optimisation and approximation techniques need to be applied at different levels. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an in-depth analysis of different state-of-the-art acceleration approaches. Besides the optimisations that can be performed at the algorithmic level, this review presents the most promising hardware-aware optimisations and future directions that can be explored for improving the performance and scalability of the simulations.
Quantum computing has emerged as a transformative paradigm capable of solving complex computational problems that are intractable for classical computers. Central to the advancement of this technology is the development of efficient quantum algorithms and high fidelity simulations that enable the design, testing, and optimization of quantum solutions before deployment on physical hardware. This study focuses on the design and implementation of quantum algorithms tailored for solving a variety of quantum problems, including quantum chemistry, optimization, and cryptography. Using state of the art simulation platforms such as Qiskit, Cirq, and Pennylane, algorithm performance was evaluated in terms of accuracy, execution time, scalability, and resilience to noise. Benchmarks demonstrate that algorithms such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) achieve significant speedups in problem specific domains, while hybrid quantum–classical methods provide robust pathways for near term quantum advantage. Simulation results reveal that algorithmic efficiency can be significantly improved through optimized circuit depth, qubit connectivity mapping, and advanced error mitigation techniques. The findings highlight the potential of simulation driven quantum algorithm development in accelerating the practical realization of quantum computing applications across disciplines such as materials discovery, molecular modeling, secure communications, and complex optimization tasks.
This thesis describes the design and implementation of an environment to allow quantum computation to be simulated on classical computers. Although it is believed that quantum computers cannot in general be efficiently simulated classically, it is nevertheless possible to simulate small but interesting systems, on the order of a few tens of quantum bits. Since the state of the art of physical implementations is less than 10 bits, simulation remains a useful tool for understanding the behavior of quantum algorithms. To create a suitable envrionment for simulation, we constructed a 32-node cluster of workstation class computers linked with a high speed (gigabit Ethernet) network. We then wrote an initial simulation environment based on parallel linear algebra libraries with a Matlab front end. These libraries operated on large matrices representing the problem being simulated. The parallel Matlab environment demonstrated a degree of parallel speedup as we added processors, but overall execution times were high, since the amount of data scaled exponentially with the size of the problem. This increased both the number of operations that had to be performed to compute the simulation, and the volume of data that had to be communicated between the nodes as they were computing. The scaling also affected memory utilization, limiting us to a maximum problem size of 14 qubits. In an attempt to increase simulation efficiency, we revisited the design of the simulation environment. Many quantum algorithms have a structure that can be described using the tensor product operator from linear algebra. We believed that a new simulation environment based on this tensor product structure would be substantially more efficient than one based on large matrices. We designed a new simulation envrionment that exploited this tensor product structure. Benchmarks that we performed on the new simulation environment confirmed that it was substantially more efficient, allowing us to perform simulations of the quantum Fourier transform and the discrete approximation to the solution of 3-SAT by adiabatic evolution up to 25 qubits in a reasonable time. Thesis Supervisor: Stephen A. Benton Title: Professor of Media Arts and Sciences
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.
The field of quantum computing has evolved into a large interdisciplinary community where significant resources are invested worldwide. As a result, accelerated progress is achieved and we are at the beginning of a new age of computation, developing programmable quantum systems towards universal quantum computers. Quantum computers promise to solve certain mathematical problems that are intractable to classical computers. Quantum computing systems are built from the bottom up reaching the limits of what can be classically simulated. The IBM Quantum Development Roadmap describes our vision of creating a quantum computing ecosystem delivering quantum applications through the cloud. This requires developing the entire quantum computing stack starting from the qubit and quantum processor technology, control electronics to software, algorithms and applications for quantum computing, implemented in the cloud and integrated with high performance computing. In this presentation the recent developments of our quantum computing systems and the scientific advances that enabled scaling superconducting quantum processors to 127 qubits are presented. Besides scale, also quality and speed will be discussed building the key metric for measuring the performance of quantum computation. Examples of applications where the computational power of quantum computing could make a difference are provided.