In this paper, we present SCALAR (Symbolic Conjecture and LLM-Assisted Reasoning), a neurosymbolic framework for automated conjecture generation in quantum circuit analysis built on top of the CUDA-Q open source framework. The system integrates quantum simulation, symbolic conjecture generation, and LLM-based interpretation. We evaluate SCALAR on 82 MaxCut instances from the MQLib benchmark dataset and extend the analysis to 2,000 randomly generated graphs across four topologies: regular, Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz. The framework generates conjectured bounds relating optimal QAOA parameters to graph invariants, including known relationships such as periodicity constraints on the phase separation parameter
γ. SCALAR also recovers previously reported parameter transfer phenomena across structurally similar instances. Additionally, the system identifies correlations between graph structural features and optimization landscape properties, which we characterize through invariant-based descriptors. Using CUDA-Q tensor network simulator, we scale experiments to instances of up to 77 qubits. We discuss the accuracy, generality, and limitations of the generated conjectures, including sensitivity to graph class and quantum circuit depth.
Variational quantum circuits (VQCs) constitute a central building block of near-term quantum machine learning (QML), yet the principled design of expressive and trainable architectures remains a major open challenge. The VQC design space grows combinatorially with the number of qubits, layers, entanglement structures, and gate parameterizations, rendering manual circuit construction inefficient and often suboptimal. We introduce an autonomous agent-based framework for VQC architecture search that integrates high-level reasoning with a quantum simulation environment. The agent proposes candidate circuit architectures, evaluates them through fully automated training and validation pipelines, and iteratively improves its design strategy via performance-driven feedback. Empirically, we show that the agent autonomously evolves circuit architectures from simple initial ansätze toward increasingly expressive designs, progressively trying to improve task performance. This demonstrates that agentic AI can effectively navigate and refine the VQC design landscape with minimal human intervention, providing a scalable methodology for automated quantum model development in the Noisy Intermediate-Scale Quantum (NISQ) regime.
The emerging intersection of quantum computing and large scale data processing is generally referred to an early research field named as Quantum-Assisted Artificial Intelligence (QAAI).The goal of this research is to integrate quantum algorithms with commonly used AI models, so as to tackle computational bottlenecks in dealing with large amounts of data.Some well known quantum algorithms, for the classification problem for example one can mention Quantum Support Vector Machines (QSVM) and Quantum Nearest Neighbor Methods can offer improvement in the classification accuracy at the cost of having a substantially smaller computational complexity.This is achieved with the proposed methodology of using variational quantum circuits for optimizing learning parameters and hybrid quantum classical architectures for efficient training of the model.Experiment results show that the complexity of datasets could be accelerated by 30-40% in training speed while comparable accuracy could be retained compared with its traditional AI counterpart.Furthermore, randomized measurement protocol is implemented to improve the system's performance in dealing with high dimensional data with low resource overhead.In addition, the integration of QAAI models includes greater resilience in the presence of noisy input data.This enabled us to develop providing more effective solutions for big data analytics, financial forecasting, and real time decision making system.This, however, still remains to be done, as integrating quantum-enhanced data encoding strategies for running quantum algorithms and exploring new frameworks for quantum assisted learning can make QAAI solutions an effective way to tackle the aforementioned scaling issues that all classical AI systems have.
We introduce DeepQuantum, an open-source, PyTorch-based software platform for quantum machine learning and photonic quantum computing. This AI-enhanced framework enables efficient design and execution of hybrid quantum-classical models and variational quantum algorithms on both CPUs and GPUs. For photonic quantum computing, DeepQuantum implements Fock, Gaussian, and Bosonic backends, catering to different simulation needs. To our knowledge, it is the first framework to realize closed-loop integration of three paradigms of quantum computing, namely quantum circuits, photonic quantum circuits, and measurement-based quantum computing, thereby enabling robust support for both specialized and universal photonic quantum algorithm design. Furthermore, DeepQuantum supports large-scale simulations based on tensor network techniques and a distributed parallel computing architecture. We demonstrate these capabilities through comprehensive benchmarks and illustrative examples. With its unique features, DeepQuantum is intended to be a powerful platform for both AI for Quantum and Quantum for AI.