Instead of producing quantum languages that are fit for current quantum computers, we build a language from standard classical assembler and augment it with quantum capabilities so that quantum algorithms become a subset of it. This paves the way for the development of hybrid algorithms directly from classical software, which is not feasible on today's hardware but might inspire future quantum programmers.
Instead of producing quantum languages that are fit for current quantum computers, we build a language from standard classical assembler and augment it with quantum capabilities so that quantum algorithms become a subset of it. This paves the way for the development of hybrid algorithms directly from classical software, which is not feasible on today's hardware but might inspire future quantum programmers.
What else than Hybrid Quantum Computing will dominate data centers, just within a decade? But until now, it is unclear how precisely such high-performance computers will be constructed, even the fundamental functionality is in question. Thus, it is very hard to prepare today, from a programmer perspective, in-depth for the anticipated near-term programming paradigm shift due to quantum computers. Furthermore, the industry is reluctant to invest heavily into quantum software development right now, since under this uncertainty regarding the future development of hardware and Cloud services, no one would like to see all these efforts to be ruined by a novel technology, an unforeseen new machine, which would eradicate the software in place, which the industry had developed so costly. This calls for a more generic view on Hybrid Quantum Computing, on one hand hardware agnostic, but still anticipating the fundamental laws of nature which rule any future quantum computing system, regardless of its engineered excellence. This generalization can be done with the introduction of a Virtual Quantum Processor, a piece of imaginary hardware, which is described detailed enough, to emulate a generic hybrid quantum machine based on a set of instructions within a Turing machine. If this could be accomplished, we would retrieve a Uniform Computing Model for Hybrid Quantum Software, which can be applied later to any physical representation of quantum computing hardware, but would run already today on our current machines.
Nowadays, quantum computers are capturing the attention of researchers because of their potential capability to solve problems otherwise not solvable with classical computers. In parallel to the constant research aimed at solving problems related to the physical implementation of such computers, there is also another branch of research trying to develop quantum algorithms. Many of the existing algorithms today need to perform arithmetic operations. In this thesis, the development of a software library of quantum arithmetic circuits - capable of interfacing with the main languages related to the world of Quantum Computing, such as Qiskit, Cirq and T-ket - is presented. With the aim of implementing this software library, the basics of quantum computation were first studied and subsequently an extensive bibliographic research was carried out, aimed at identifying the best performing arithmetic circuits currently available. After determining the circuits to be implemented, the software development has begun using the Python language. During the development of the library, with the aim of making simpler future development, code's modularity - by organizing the general package in many subpackages - and readability - by respecting the guidelines proposed in the PEP8 style guide - were looked for. In addition to what has already been said, a detailed documentation has been produced using Sphinx. Once the development of the library was completed, the functionality of each circuit was tested with simulations on classical computers; moreover, some components were tested on real free-accessible quantum computers, in order to characterize their behavior. Even though the results obtained on real hardware proved that many of the circuits implemented in this library will be reliably executed by more sophisticated and performing quantum computers, this library can be currently employed in an effective way in the simulation of quantum circuits on classical computers.
Recent developments in engineering and algorithms have made real-world applications in quantum computing possible in the near future. Existing quantum programming languages and compilers use a quantum assembly language composed of 1- and 2-qubit (quantum bit) gates. Quantum compiler frameworks translate this quantum assembly to electric signals (called control pulses) that implement the specified computation on specific physical devices. However, there is a mismatch between the operations defined by the 1- and 2-qubit logical ISA and their underlying physical implementation, so the current practice of directly translating logical instructions into control pulses results in inefficient, high-latency programs. To address this inefficiency, we propose a universal quantum compilation methodology that aggregates multiple logical operations into larger units that manipulate up to 10 qubits at a time. Our methodology then optimizes these aggregates by (1) finding commutative intermediate operations that result in more efficient schedules and (2) creating custom control pulses optimized for the aggregate (instead of individual 1- and 2-qubit operations). Compared to the standard gate-based compilation, the proposed approach realizes a deeper vertical integration of high-level quantum software and low-level, physical quantum hardware. We evaluate our approach on important near-term quantum applications on simulations of superconducting quantum architectures. Our proposed approach provides a mean speedup of 5×, with a maximum of 10×. Because latency directly affects the feasibility of quantum computation, our results not only improve performance but also have the potential to enable quantum computation sooner than otherwise possible.
Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical machines, while others utilize quantum resources. However, existing methods often to identify candidates to quantum execution (quantum candidates) involve a trial-and-error approach, relying on the intuition and expertise of computer scientists, resulting in varying identification durations ranging from minutes to days for a single application.
Large Language Models (LLMs) such as GPT and LLaMA have transformed artificial intelligence, but their rapid growth has brought major challenges in computation, energy use, and scalability. Quantum computing (QC) offers a fundamentally different way to process information, with the potential to accelerate key operations in training, inference, optimization, and representation learning. This survey provides a structured and comprehensive review of how QC can support the future development of LLMs. It examines quantum algorithms, hybrid quantum-classical methods, quantum neural networks, quantum embeddings, and security applications. It also evaluates their feasibility in both the current noisy intermediate-scale quantum (NISQ) era and the future fault-tolerant era. The survey highlights current progress, identifies major technical barriers, and outlines practical research directions needed to build scalable, efficient, and secure quantum-enhanced language models.