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International Conference on Computing, Communication and Networking Technologies (ICCCNT)

Optimizing Compiler for Quantum Computing Using Qiskit Terra

Peddi Gowtham Balaji, Peraboina Chamikar, Omkar Chowdary, Meena Belwal

Published Jun 24, 2024• 3 citations
Publisher

Abstract

The recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. This paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. These optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. The introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures.

DOI
10.1109/icccnt61001.2024.10726185

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The recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. This paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. These optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. The introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures.
With the increasing capabilities of quantum systems, the efficient, practical execution of quantum programs is becoming more critical. Each execution includes compilation time, which accounts for substantial overhead of the overall program runtime. To address this challenge, proposals that leverage precompilation techniques have emerged, whereby entire circuits or select components are precompiled to mitigate the compilation time spent during execution. Considering the impact of compilation time on quantum program execution, identifying the contribution of each individual compilation task to the execution time is necessary in directing the community’s research efforts towards the development of an efficient compilation and execution pipeline. In this work, we perform a preliminary analysis of the quantum circuit compilation process in Qiskit, examining the cumulative runtime of each individual compilation task and identifying the tasks that most strongly impact the overall compilation time. Our results indicate that, as the desired level of optimization increases, circuit optimization and gate synthesis passes become the dominant tasks in compiling a Quantum Fourier Transform, with individual passes consuming up to 87% of the total compilation time. Mapping passes require the most compilation time for a GHZ state preparation circuit, accounting for over 99% of total compilation time.
Quantum computing promises computational speedups over classical approaches for certain problem classes; however, in near term quantum and classical hybrid workflows, the overall performance is often constrained by classical processing overhead. The time cost associated with circuit compilation, which includes gate decomposition, topology aware qubit mapping, and depth optimization, together with protocol level communication such as circuit transfer and measurement result exchange, frequently exceeds the actual quantum circuit execution time and has become a critical bottleneck to practical deployment. In this paper, we propose QPanda 3.0, a high performance quantum programming framework optimized to accelerate the classical compilation path that underpins NISQ era hybrid integration. Leveraging an optimized compilation pipeline, a custom binary instruction stream (OriginBIS), and hardware aware execution strategies, QPanda 3.0 achieves up to 86.9 × faster encoding and 35.6 × faster decoding than OpenQASM 2.0, as well as an average 10.7 × compilation speedup over Qiskit across the four evaluated connectivity topologies, with a peak speedup of 597 × for a 118-qubit W-state circuit on a square topology. These capabilities enable the development of scalable quantum algorithms across various domains, including finance, materials science, and combinatorial optimization, supporting both industrial deployment and cloud based execution in hybrid computing environments.
We present the first universal quantum compiler based on Generative Pre-trained Transformer (GPT) architecture that provides cross-framework optimization and translation capabilities for quantum circuits. Our Quantum Compiler GPT (QC-GPT) unifies four major quantum computing frameworks—Qiskit Terra, Cirq, tket and PyQuil— into a single Al-driven compilation system. The model demonstrates significant improvements in circuit optimization, achieving 23-47% reduction in gate count, 18-35% reduction in circuit depth, and 12-28% improvement in estimated fidelity compared to existing compilers. QC-GPT also enables seamless translation between quantum frameworks with 96.3% accuracy while providing human-readable explanations for optimization decisions. Our approach leverages a quantum-aware transformer architecture with 512M parameters, trained on 104,000 circuit optimization examples across all supported frameworks. This work represents the first successful application of large language models to universal quantum compilation, opening new possibilities for Al-assisted quantum software development.
Quantum circuit compilation is a critical step in realizing quantum algorithms on real quantum hardware. Traditional compilation methods often employ static optimization strategies, neglecting the unique constraints and characteristics of the target quantum device. This paper introduces a novel adaptive quantum circuit compilation framework designed to dynamically adjust circuit designs based on hardware-specific information, aiming to minimize gate count and, consequently, error rates. The core of the framework lies in integrating a hardware simulator directly within the compilation process. This allows the compiler to evaluate the performance of various circuit designs—considering factors like qubit connectivity, gate fidelity, and decoherence models—before committing to a final layout. The proposed approach represents a shift from static to adaptive optimization, providing a more nuanced and effective strategy for translating quantum algorithms into executable code on diverse quantum hardware platforms. This research demonstrates a significant potential improvement in quantum circuit efficiency and fidelity, addressing a key bottleneck in the advancement of quantum computing.
Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.
Quantum computing is a revolution in computational theory that uses the principles of quantum mechanics to perform computations that are beyond the capacity of classical computers. It works with the special features of the quantum bits or qubits to encode and transform the information in manners conventional bits cannot imagine. A quantum compiler is a software tool designed to translate high-level quantum language code into executable quantum instructions. This paper aims to develop a quantum compiler for translating Quantum Computation Language source codes into quantum bytecodes which can later be executed on a Quantum Virtual Machine. This compiler will have a combinational structure that includes a frontend for interpreting the QCL code, an intermediate representation for optimizing quantum algorithms, and a backend for generating efficient quantum bytecode. Optimization techniques will be applied to improve the functioning of the quantum circuits. The compiler will also include an error handling module to handle the errors. The successful deployment of a quantum compiler for QCL has many implications and unlocks opportunities for advancements in quantum technologies.