This preprint introduces a conceptual and experimental framework for integrating learned continuous representations into quantum compiler architectures. Modern quantum compilers rely on symbolic intermediate representations and rule-based transformation passes applied over circuit graphs. While effective, such approaches are inherently discrete and constrained by handcrafted rewrite rules and heuristic search strategies. This work proposes a Learned Intermediate Representation (LIR) paradigm in which quantum circuits are embedded into a continuous latent space. Within this framework: Circuit graphs are mapped to fixed-dimensional embeddings. Latent representations capture structural and operational properties of quantum circuits. Learned models guide symbolic transformation selection. Structural validity and physical correctness are enforced by a symbolic compilation layer. The proposed approach does not replace existing compilation infrastructures. Instead, it augments traditional symbolic pipelines with a learnable representation layer that enables data-driven optimization strategies while preserving correctness guarantees. The preprint includes: A formal definition of the encoder and latent representation space. A hybrid neural-symbolic decoding formulation. An experimental methodology for comparing neural-guided and rule-based compilation. Evaluation metrics focused on structural cost reduction (e.g., two-qubit gate count). Discussion of theoretical considerations, limitations, and future research directions. This work represents an initial investigation into continuous intermediate representations for quantum compilation and is intended as a foundation for further research in adaptive and data-driven compiler design.
This preprint introduces a conceptual and experimental framework for integrating learned continuous representations into quantum compiler architectures. Modern quantum compilers rely on symbolic intermediate representations and rule-based transformation passes applied over circuit graphs. While effective, such approaches are inherently discrete and constrained by handcrafted rewrite rules and heuristic search strategies. This work proposes a Learned Intermediate Representation (LIR) paradigm in which quantum circuits are embedded into a continuous latent space. Within this framework: Circuit graphs are mapped to fixed-dimensional embeddings. Latent representations capture structural and operational properties of quantum circuits. Learned models guide symbolic transformation selection. Structural validity and physical correctness are enforced by a symbolic compilation layer. The proposed approach does not replace existing compilation infrastructures. Instead, it augments traditional symbolic pipelines with a learnable representation layer that enables data-driven optimization strategies while preserving correctness guarantees. The preprint includes: A formal definition of the encoder and latent representation space. A hybrid neural-symbolic decoding formulation. An experimental methodology for comparing neural-guided and rule-based compilation. Evaluation metrics focused on structural cost reduction (e.g., two-qubit gate count). Discussion of theoretical considerations, limitations, and future research directions. This work represents an initial investigation into continuous intermediate representations for quantum compilation and is intended as a foundation for further research in adaptive and data-driven compiler design.
Abstract: The evolution of quantum computing depends not only on advancements in hardware but also on breakthroughs in compiler design that can translate high-level quantum algorithms into efficient, executable circuits. This research introduces a novel approach to quantum compiler design that leverages AI guidance in tandem with superalgebraic symmetries to optimize gate synthesis, resource usage, and noise resilience. By embedding superalgebraic representations—mathematical structures that unify bosonic and fermionic transformations—into the compilation pipeline, and training AI models to recognize optimal symmetry-preserving transformations, we construct a quantum compiler that is adaptive, structure-aware, and scalable. This framework enhances circuit depth minimization, gate reduction, and fidelity preservation in real-time quantum compilation workflows. Keywords: Quantum Compiler, Superalgebras, Artificial Intelligence, Gate Optimization, Quantum Circuits, Symmetry-Preserving Transformations, Qubit Mapping, Quantum Machine Learning
Unsupervised representation learning presents new opportunities for advancing Quantum Architecture Search (QAS) on Noisy Intermediate-Scale Quantum (NISQ) devices. QAS is designed to optimize quantum circuits for Variational Quantum Algorithms (VQAs). Most QAS algorithms tightly couple the search space and search algorithm, typically requiring the evaluation of numerous quantum circuits, resulting in high computational costs and limiting scalability to larger quantum circuits. Predictor-based QAS algorithms mitigate this issue by estimating circuit performance based on structure or embedding. However, these methods often demand time-intensive labeling to optimize gate parameters across many circuits, which is crucial for training accurate predictors. Inspired by the classical neural architecture search algorithm A r c h 2 v e c , we investigate the potential of unsupervised representation learning for QAS without relying on predictors. Our framework decouples unsupervised architecture representation learning from the search process, enabling the learned representations to be applied across various downstream tasks. Additionally, it integrates an improved quantum circuit graph encoding scheme, addressing the limitations of existing representations and enhancing search efficiency. This predictor-free approach removes the need for large labeled datasets. During the search, we employ REINFORCE and Bayesian Optimization to explore the latent representation space and compare their performance against baseline methods. We further validate our approach by executing the best-discovered MaxCut circuits on IBM's ibm_sherbrooke quantum processor, confirming that the architectures retain optimal performance even under real hardware noise. Our results demonstrate that the framework efficiently identifies high-performing quantum circuits with fewer search iterations.
Quantum compilers typically follow the circuit model, representing programs as fixed sequences of gates. This static view breaks down in hybrid quantum-classical applications, where gate choices depend on runtime data or measurement results. We introduce a new Intermediate Representation (IR) that elevates gates to first-class values, enabling their dynamic creation, composition, and control. This unified representation allows classical computation to steer quantum behaviour, capturing phenomena including stochastic gate selection, adaptive error correction, and measurement-driven computation within a single framework. Case studies in noise modelling, randomised compilation, error correction, and measurement-based quantum computing show that our IR expresses these programs compactly and supports optimisations that were not possible in the circuit model. Evaluation on a benchmark suite of hybrid quantum-classical programs indicates that our IR represents programs compactly and facilitates compiler analysis and transformation.
We propose a learning-based approach to quantum compilation by translating OpenQASM to Quantum Intermediate Representation (QIR) using a fine-tuned CodeBERT model. Trained on 10,000 synthetic QASM-QIR pairs, the model captures code semantics while addressing QIR verbosity and the 512-token limit via a custom token compression scheme. Finetuning was performed on the Frontier supercomputer, with results showing syntactic correctness and stable validation loss reduction. Our method moves toward enabling flexible, language-modeldriven quantum software tools. It also introduces syntax error handling and the possibility of incorporating classical control constructs, addressing limitations in existing rule-based compilers like qBraid-QIR. While the current model has been validated on quantum-only circuits, we propose future evaluations on hybrid quantum-classical examples. This poster will provide architecture insights, compression examples, training loss plots, and QIR outputs. Our work highlights the potential for scalable, adaptable compilation in future quantum toolchains.
High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate-set tomography (GST) data that bypasses the traditional two-step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms. Instead, a generative concept space is directly learnt from GST data, enabling conditional synthesis of quantum circuits on a desired output distribution. Our approach tokenizes GST germ circuits and embeds them into a structured latent space using a curriculum-learning-motivated strategy, starting with short circuits and progressively incorporating longer ones with diverse output statistics. The embedded sequences are processed by a set-vision transformer with permutation-invariant pooling, producing k-seed vectors that represent the learned concept space of the quantum device. Aggregating data across multiple circuits makes this latent representation inherently context-aware, capturing the shared physical noise environment (e.g., crosstalk, drift) that isolated gate metrics miss. We propose an unconditional diffusion model to sample from the concept space. During inference, a user provides a target measurement distribution, and the model generates a corresponding circuit. To ensure fidelity and robustness, the output is denoised using a diffusion model that operates on the target conditional covariance matrix. This end-to-end framework is a step towards context-aware, hardware-native circuit synthesis directly from raw GST data, which offers a new paradigm for integrating quantum control and compilation. The QMLC framework is particularly suited for near-term quantum devices with complex calibration procedures.
Hybridizing machine learning (ML) with quantum computing has become an attractive strategy to increase computational power on Noisy Intermediate Scale Quantum (NISQ) devices. Although these can be potentially quantum advantage devices, their qubit numbers and noise are bounded and there are challenges when it comes to practical quantum circuit realization. This work concentrates on the creation of optimized quantum circuits via ML-based strategies, to enhance the performance of the circuits, to reduce the error rate and to accommodate the particular noise profile of the hardware of NISQ devices. The resulting suboptimal mappings with little or no consideration of noise and hardware topology in traditional quantum compilers can ruin the fidelity of quantum algorithm. Leveraging machine learning, and specifically reinforcement and supervised learning methods, our method learns to optimize compilation strategy for specific quantum hardware architecture. The paper proposes a framework that uses machine learning models to predict qubit mappings, gate decompositions, and transpilation paths that minimize gate depth and error accumulation. We also investigate transfer learning methods for propagating the learned compilation strategies to novel NISQ machines. Such methods are empirically validated on both simulated and real quantum hardware backends, providing evidence of circuit depth, execution time and error reductions. The results show that ML-assisted compilation can achieve better success probabilities for benchmark quantum algorithms like VQE and QAOA than conventional compilers. Furthermore, the work examines the interpretability of ML models providing hardware-aware optimization recommendations and understanding the noise patterns and methods of error correction. Our work highlights the key role of ML for transferring between theoretical quantum algorithms and their practical NISQ implementations. This integration boosts the immediate applicability of existing quantum devices and provides a basis for adaptive and scalable likelihood-based compilation for the era of fault-tolerant quantum computation. Our results motivate further investigation of hybrid classical-quantum workflows, in which ML iteratively improves compilation in accordance with changing hardware noise and capabilities. By improving quantum circuit compilation onNISQ computers, this work is taking steps toward the realization of quantum advantage in application scenarios such as optimization, cryptography, and quantum simulation without needing to wait for fully fault-tolerant large-scale quantum computers.