One of the major obstacles to the adoption of quantum computing is the requirement to define quantum circuits at the quantum gate level. Many programmers are familiar with high-level or low-level programming languages but not quantum gates nor the low-level quantum logic required to derive useful results from quantum computers. The steep learning curve involved when progressing from quantum gates to complex simulations such as Shor's algorithm has proven too much for many developers. The purpose of this paper and the software presented within, addresses this challenge by providing a Software Development Kit (SDK), translation layer, emulator and a framework of techniques for executing Intel 8080/Z80 assembler on a quantum computer, i.e. all salient points of CPU execution, logic, arithmetic and bitwise manipulation will be executed on the quantum computer using quantum circuits. This provides a novel means of displaying the equivalency and interoperability of quantum and classical computers. Developers and researchers can use the SDK to write code in Intel 8080/Z80 assembler which is executed locally via traditional emulation and remotely on a quantum computer in parallel. The emulator features side-by-side code execution with visibility of the running quantum circuit and re-usable/overridable methods. This enables programmers to learn, reuse and contrast techniques for performing any traditional CPU based technique/instruction on a quantum computer; e.g. a programmer may know how to multiply and perform checks on a classical CPU but is not able to perform the same tasks in a quantum implementation, this SDK allows the programmer to pick and choose the methods they would like to use to fulfil their requirements. The SDK makes use of open-source software, specifically Python and Qiskit for the emulation, translation, API calls and execution of user supplied code or binaries.
One of the major obstacles to the adoption of quantum computing is the requirement to define quantum circuits at the quantum gate level. Many programmers are familiar with high-level or low-level programming languages but not quantum gates nor the low-level quantum logic required to derive useful results from quantum computers. The steep learning curve involved when progressing from quantum gates to complex simulations such as Shor's algorithm has proven too much for many developers. The purpose of this paper and the software presented within, addresses this challenge by providing a Software Development Kit (SDK), translation layer, emulator and a framework of techniques for executing Intel 8080/Z80 assembler on a quantum computer, i.e. all salient points of CPU execution, logic, arithmetic and bitwise manipulation will be executed on the quantum computer using quantum circuits. This provides a novel means of displaying the equivalency and interoperability of quantum and classical computers. Developers and researchers can use the SDK to write code in Intel 8080/Z80 assembler which is executed locally via traditional emulation and remotely on a quantum computer in parallel. The emulator features side-by-side code execution with visibility of the running quantum circuit and re-usable/overridable methods. This enables programmers to learn, reuse and contrast techniques for performing any traditional CPU based technique/instruction on a quantum computer; e.g. a programmer may know how to multiply and perform checks on a classical CPU but is not able to perform the same tasks in a quantum implementation, this SDK allows the programmer to pick and choose the methods they would like to use to fulfil their requirements. The SDK makes use of open-source software, specifically Python and Qiskit for the emulation, translation, API calls and execution of user supplied code or binaries.
Abstract The use and adoption of quantum computers by the wider computing community has been impeded by the need to adopt new programming techniques. These techniques involve moving from a high-level language where the programmer can define and manipulate objects, to a quantum model where the programmer defines and configures the circuits at a gate level. A jump too far for many. Previous work by the authors aimed to ease this transition through the use of a software development kit (Qx86 SDK) that emulates a traditional CPU for execution on a quantum computer which focused on delivering the capability. This paper now presents a number of new methods that extends and improves the SDKs capability. These methods include optimizing traditional logic gate emulation, multiple gate simplification methods, reducing the number of required qubits, and alternative optimized techniques for many CPU instructions. A quantum machine code mapping method is also described to propose the emulation of a traditional/quantum hybrid CPU prototype. Whilst still orders of magnitude slower than the performance of a traditional CPU in terms of arithmetic, logic and bitwise operations, execution speed is shown to be markedly improved (in some cases by more than 1,000%) and without introducing any unrealistic requirements (that is, all execution can be performed utilizing less than 32 qubits). The usefulness of the SDK has now been enhanced as a reference guide, where the programmer/researcher can contrast traditional methods vs multiple quantum methods of execution.
The Intel(R) Quantum Software Development Kit (SDK) offers a comprehensive platform for developers to create quantum applications using an LLVM-based compiler with user-friendly C++ language extensions. This full-stack solution facilitates the expression and optimization of quantum circuits and includes a quantum runtime library for seamless hybrid execution between classical and quantum kernels. The runtime enables dynamic parameter determination for quantum circuits, allowing both quantum and classical components of variational algorithms to be defined within a single program, compiled once, and significantly reducing execution latency. The SDK features a suite of quantum simulators, including simulations of Intel's quantum hardware components such as qubit control processors, control electronics, and quantum dot qubits. With a unified interface, the Intel Quantum SDK allows users to target ideal qubit simulators, realistic Intel quantum hardware simulators, and future Intel quantum hardware, enabling efficient optimization, compilation, and execution of scalable hybrid quantum-classical variational algorithms.
Although the concept of quantum computing has existed for decades, the technology needed to successfully implement a quantum computing system has not yet reached the level of sophistication, reliability, and scalability necessary for commercial viability until very recently. Significant progress on this front was made in the past few years, with IBM planning to create a 1000-qubit chip by the end of 2023, and Google already claiming to have achieved quantum supremacy. Other major industry players such as Intel and Microsoft have also invested significant amounts of resources into quantum computing research. Any viable computing system requires both hardware and software to work together harmoniously in order to perform useful computations. While the achievements of IBM and other companies represent a large step forward for quantum hardware, many gaps remain to be filled with respect to the corresponding software. Specifically, there is currently no clear path towards a complete process for translating quantum algorithms into physical operations that are directly executable on quantum hardware. Such a process is analogous to a compiler that translates programs written in a high-level language into executable machine instructions on a conventional digital computer, and it is necessary if quantum computers are to be harnessed to perform practically useful computations. Existing work has addressed individual components of this process, but so far no unified method for translating the whole of a quantum algorithm into executable operations has been described. I make substantial progress towards filling this gap by describing a set of high-level and low-level quantum circuit design techniques, which when taken together reduce the need of a circuit designer to be concerned with low-level details. On the high-level side, I describe an approach or strategy to designing quantum oracles for Grover's algorithm that allows it to be applied to several types of problems. This approach involves designing oracles in terms of high-level blocks such as counters, multiplexers, comparators, and arbitrary Boolean functions. The implementations of these blocks in terms of lower-level quantum gates are demonstrated in a way that makes it clear that scaled-up versions of them can be generated in a completely automated fashion. For a specific class of problems, which I call state-space path planning problems, I also introduce a paradigm for quantum oracle design that involves representing the problem in terms of individual states and moves. Problems of this sort have applications in robotics and games. Low-level techniques that I introduce
In 2019 a quantum computer took 200 seconds for a task for which a state-of-the-art classical supercomputer would need roughly 10,000 years. Quantum supremacy was shown and the development of quantum technology proceeds quickly. However, several aspects limit the implementation and execution of quantum circuits. The vendors of quantum computers provide their own proprietary software development kit (SDK) to execute quantum circuits on their quantum processing units (QPUs). Furthermore, the computers differ regarding various properties like the native gate set and the qubit connectivity. Therefore, quantum circuits cannot be executed on arbitrary QPUs, but there exists a tight coupling between a QPU and a quantum algorithm implementation. To decouple the development of a circuit from the QPU, this thesis proposes a quantum circuit analysis and transpilation framework that integrates quantum SDKs and enables the comparison and analysis of quantum circuits, as well as the export of executable circuits in the respective assembly language of QPUs from different vendors. Currently existing QPUs, also called Noisy Intermediate-Scale Quantum (NISQ) machines, have a very limited number of qubits and are prone to failure. Thus, the integration of different QPUs enhances the possibilities of quantum circuit developers and avoids the SDK lock-in. Additionally, a graphical user interface is developed to support the user in the whole process of importing, visualizing, editing, and simulating a quantum circuit, as well as, choosing a suitable QPU to execute the circuit.
The rapid development of quantum computing technology has led to an increasing need for efficient and accessible programming tools that can bridge the gap between classical and quantum computing paradigms. In this paper, we introduce the Intel®Quantum SDK, a powerful software development kit designed to facilitate hybrid quantum-classical programming using the C++ Quantum Extension. The SDK provides a comprehensive set of tools and libraries that enable developers to harness the power of quantum computing while leveraging their existing knowledge of classical programming techniques. By incorporating quantum algorithms and operations within a familiar C++ framework, the Intel Quantum SDK empowers programmers to create innovative hybrid applications that can tackle complex problems and drive advancements in various fields, including cryptography, optimization, and materials science. This paper will guide readers through the essential concepts, features, and capabilities of the Intel Quantum SDK. This seamless integration of quantum and classical programming paradigms paves the way for the development of innovative and efficient solutions to complex computational challenges, ultimately driving the advancement of quantum computing technology and its real-world applications.
Quantum software development is challenged by quantum-mechanical semantics and fragmented software development kits (SDKs), which complicate cross-SDK code translation and execution. In this paper, we propose an LLM-based approach for translating executable programs between high-level quantum SDKs. We develop a quantum-aware translation workflow whose prompt design and validation reflect quantum semantics and accommodate the version evolution of quantum ecosystems. We conduct a preliminary evaluation of 4 LLM configurations on 43 tasks, translating Qiskit programs to PennyLane and Q#. LLMs achieve a zero-shot Pass@1 of up to 72.33%, while one-shot prompting further improves the translation effectiveness. These results suggest potential for quantum code translation by LLMs.