Jul 2026· 2026 IEEE International Conference on Quantum Software (QSW)· pp. 48-56· 0 citations· 26 references
Abstract
Parametrized quantum circuits (PQCs) form the computational backbone of variational quantum algorithms, yet their practical utility is increasingly constrained by optimisation failures as circuit depth grows. Gradient signals decay rapidly under standard random initialisation, and existing structured approaches abandon inter-layer coordination the moment independent optimisation begins. In this work, we introduce a depthconditioned parameterisation that preserves this coordination throughout training by generating all circuit parameters from a low-dimensional Fourier model over normalised depth, anchored by a task-adapted shallow-circuit prior and augmented with perlayer residuals that retain full expressivity. Gradient information from all layers is aggregated into a compact set of shared weights, providing a principled mechanism for sustaining training signals at depth. Numerically, the proposed method achieves reliable convergence in regimes where all baseline strategies fail or succeed only sporadically, reducing final validation error twoto ten-fold and reaching convergence thresholds up to 2.6 times faster, with $\mathcal{O}\left(d_{\theta}\right)$ overhead independent of circuit depth.
This work examines when structured long-range connectivity provides a useful resource, focusing on sparse power-of-two (PWR2) coupling graphs, and identifies circuit geometry and qubit reconfigurability as task-dependent resources for variational algorithms.
Helene M. Losl, Aydin Deger, Andrew J. Daley· 0 citations
A reinforcement learning framework that embeds a deterministic Commutation-and-Reduction (CR) algorithm directly into the training environment, enabling the agent to focus its learning capacity on the non-trivial optimizations where reinforcement learning adds real value.
Khoa Dang Tao, Sumin Jin, M. Raza et al.· 0 citations
Quantum DeepONet accelerates neural-operator inference by evaluating an orthogonally parameterized network on a quantum computer, reproducing in ideal simulation the accuracy of its classical counterpart at asymptotically lower inference cost. Its trunk network, however, receives query coordinates with limited spectral structure, requiring the network to learn oscillatory features through its nonlinearities. We propose Quantum SEDONet (Spectral-Embedded Deep Operator Network), which assigns each trunk coordinate a spectral basis according to its boundary condition: Fourier features for periodic coordinates and Chebyshev features for bounded, non-periodic coordinates. The basis is selected per coordinate rather than per problem, allowing both representations within a single problem. Under unary amplitude encoding, the embedding incurs no additional qubits or circuit depth when its dimension remains within the network width, while increasing the parameter count by only a few percent. Across four benchmarks, Quantum SEDONet reduces the mean relative L2 error by 54.1% for the antiderivative, 49.6% for advection, 36.0% for Burgers, and 36.2% for a mixed-boundary channel Poisson problem. Quantum and classical evaluation paths agree to within 10^-8 throughout. The channel Poisson problem simultaneously uses Fourier features in the periodic direction and Chebyshev features in the bounded direction, demonstrating coordinate-wise boundary-matched spectral embedding without additional quantum-resource cost.
Muhammad Abid, Arth Sojitra, Bipin Tiwari et al.· 0 citations
The results indicate that symmetry compilation concentrates the expressive power of NQS on states relevant to the target problem, thereby reducing model size and training cost without sacrificing accuracy.
Turbasu Chatterjee, M. Sajjan, Songbo Xie et al.· 0 citations
This work constructs a native 2D pairwise ansatz and compares its expressibility and trainability with representative 1D ansatze at identical layer depths, despite their different circuit depths.
Although recent advances in transfer learning have simplified training, fine-tuning big language models remains an energy-intensive and expensive process, with high hardware and energy costs that make it difficult to use and scale. While quantum machine learning (QML) presents a promising theoretical tool to overcome these bottlenecks, the practical implementation is hampered by the barren plateau problem, a phenomenon well established in literature that manifests through exponentially vanishing gradients of deep parameterized quantum circuits (PQCs) with increasing circuit depth, preventing informative parameter updates by rendering the optimization landscape flat. This study presents a hybrid quantum-classical architecture in which a PQC layer is integrated into a pretrained classical LLM backbone and fine-tuned throughout this work. This iterative refinement loop is controlled by an Expected Improvement acquisition function, which actively guides the search process through parameter regimes with obvious non-vanishing gradient variance. The study proposes that this theory-driven initialization scheme reduces the onset of barren plateaus, thereby enabling efficient and stable convergence in hybrid optimization. Experiments show that the proposed method consistently outperforms purely classical fine-tuning baselines and randomly initialized quantum baselines on representative downstream natural language processing tasks. The experimental results show that principled parameter initialization leads to concrete improvements in convergence stability, gradient trainability, and task-level performance. In summary, this research provides a scalable method for integrating near-term noisy intermediate-scale Quantum (NISQ) devices into state-of-the-art deep learning pipelines, fostering the further real-world adoption of hybrid quantum-classical systems for demanding artificial intelligence tasks.
Arvindhan Muthusamy, Azween Abdullah, Daniel Arockiam· International journal of com...· 0 citations