We propose a method for performing software pipelining on quantum for-loop programs to exploit parallelism in and across iterations. We redefined concepts useful in program optimization, including array aliasing, instruction dependency, and resource conflict required in optimizing quantum programs. Using these concepts, we present a software pipelining framework exploiting instruction-level parallelism in quantum loop programs. This method is further enhanced with several improvements to reduce total gate count and program depth. The optimization method is then evaluated on some popular quantum algorithms like Grover and QAOA, and compared under different configurations and with several baseline compilers. The evaluation results show that our approach can schedule loop programs with depth close to the depth of the entire loop unrolling while generating smaller code sizes and consuming much less time. This is the first step towards optimization of a quantum program with such loop control flow, as far as we know.
We propose a method for performing software pipelining on quantum for-loop programs to exploit parallelism in and across iterations. We redefined concepts useful in program optimization, including array aliasing, instruction dependency, and resource conflict required in optimizing quantum programs. Using these concepts, we present a software pipelining framework exploiting instruction-level parallelism in quantum loop programs. This method is further enhanced with several improvements to reduce total gate count and program depth. The optimization method is then evaluated on some popular quantum algorithms like Grover and QAOA, and compared under different configurations and with several baseline compilers. The evaluation results show that our approach can schedule loop programs with depth close to the depth of the entire loop unrolling while generating smaller code sizes and consuming much less time. This is the first step towards optimization of a quantum program with such loop control flow, as far as we know.
As quantum computers continue to improve and support larger, more complex computations, smart control hardware and compilers are needed to efficiently leverage the capabilities of these systems.
This paper introduces a novel approach to enhance the execution of quantum algorithms on distributed quantum systems. The proposed method involves the development of a hardware design that supports parallel instruction execution and a compiler that modifies the order of instructions to increase parallelism opportunities. The hardware design can be flexibly configured to facilitate parallel execution of instructions that have identical parameters. Furthermore, the compiler uses the underlying hardware constraints to intelligently reorder and decompose instructions to avoid dependencies.
The compiler, hardware, and their combination are evaluated using a runtime calculator and a benchmark quantum algorithm set. The results demonstrate a significant speedup, achieving a maximum average speedup of 16.5x and a maximum single-benchmark speedup of 56.2x relative to a baseline, serial execution model. Furthermore, we show a speedup can be obtained across all benchmarks using any of the proposed hardware schemes, although the degree of speedup is largely dependent on the type of quantum algorithm. Taken together, the results of this paper represent a significant step towards realizing high-performance quantum computing systems.
Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.
Quantum computing, leveraging the principles of superposition and entanglement, holds the potential to revolutionize computational problem-solving across a wide array of fields, from cryptography to complex system simulations. However, unlocking the full power of quantum computing requires the development of advanced quantum compilation techniques that can efficiently map high-level algorithms onto physical quantum hardware. This paper delves into state-of-the-art optimization strategies within quantum compilers, with a particular focus on methods such as Relaxed Peephole Optimization (RPO), gate reordering techniques for the Quantum Approximate Optimization Algorithm (QAOA), and reinforcement learning (RL)-driven frameworks. It also explores innovative approaches like distributed quantum compilation, parity quantum optimization, and the Paulihedral framework, all designed to minimize gate counts, reduce circuit depth, and optimize overall resource utilization. A key contribution of this work is the introduction of error mitigation techniques that enhance the fidelity and scalability of quantum circuits, particularly on noisy intermediate-scale quantum (NISQ) devices. Through comprehensive empirical evaluations across a variety of quantum algorithms, the paper demonstrates significant improvements in critical performance metrics such as circuit depth, gate count, and execution efficiency, providing a promising direction for optimizing quantum circuit design tailored to diverse quantum hardware platforms.
Quantum computers exploit the particular behavior of quantum physical systems to solve some problems in a different way than classical computers. We are now approaching the point where quantum computing could provide real advantages over classical methods. The computational capabilities of quantum systems will soon be available in future supercomputer architectures as hardware accelerators called Quantum Processing Units (QPU). From optimizing compilers to task scheduling, the High-Performance Computing (HPC) software stack could benefit from the advantages of quantum computing. We look here at the problem of register allocation, a crucial part of modern optimizing compilers. We propose a simple proof-of-concept hybrid quantum algorithm based on QAOA to solve this problem. We implement the algorithm and integrate it directly into GCC, a well-known modern compiler. The performance of the algorithm is evaluated against the simple Chaitin-Briggs heuristic as well as GCC's register allocator. While our proposed algorithm lags behind GCC's modern heuristics, it is a good first step in the design of useful quantum algorithms for the classical HPC software stack.
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.
Quantum computing is poised to revolutionize computational paradigms by leveraging quantum mechanics principles such as superposition and entanglement.However, the full-scale deployment of quantum applications remains constrained by hardware limitations, including high error rates and quantum decoherence.Quantum Software Engineering (QSE) emerges as a critical field addressing these challenges by optimizing algorithm design, error mitigation, and compiler strategies to enhance fault tolerance.Algorithm design in QSE focuses on developing quantum algorithms that efficiently exploit quantum parallelism while minimizing resource overhead.Key advancements include quantum variational algorithms, hybrid quantum-classical frameworks, and novel quantum heuristics tailored for optimization and cryptographic problems.Error mitigation techniques play a pivotal role in extending quantum circuit reliability without requiring full quantum error correction.Methods such as zero-noise extrapolation, probabilistic error cancellation, and quantum embedding techniques help reduce computational inaccuracies.Additionally, compiler optimization ensures efficient quantum program execution by minimizing gate depth, optimizing qubit mapping, and leveraging noise-adaptive scheduling to enhance quantum hardware performance.This paper explores the synergy between these three pillars of QSE, analyzing their impact on improving the feasibility of fault-tolerant quantum computing.It also examines emerging trends, including AI-driven quantum compilers, adaptive error mitigation techniques, and hardware-aware quantum software development.By bridging the gap between theoretical advancements and practical implementations, QSE provides a structured approach to accelerating quantum computing adoption across domains such as cryptography, materials science, and artificial intelligence.The findings underscore the necessity of interdisciplinary collaboration in developing robust quantum software solutions that maximize computational efficiency while mitigating inherent quantum hardware limitations.