This work proposes QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input, and establishes input-conditioned ANO as an effective approach for enhancing QNNs.
Abstract
Quantum neural networks (QNNs) are typically built from variational quantum circuits (VQCs), which are limited by local measurements. Adaptive non-local observables (ANO) address this by jointly optimizing circuit parameters and multi-qubit measurements. However, existing ANO-based VQCs learn only a single static observable that remains invariant across all inputs. We propose QFWP-ANO, a novel architecture which employs a classical hypernetwork to dynamically program VQC parameters and/or non-local observables conditioned on each input. On multivariate time-series forecasting across four ETT datasets, QFWP-ANO achieves the lowest MSE in 16 of 20 settings and second-lowest in the remaining four, surpassing ANO-based and other strong baselines. On reinforcement learning tasks, QFWP-ANO consistently surpasses ANO-VQCs. Our results establish input-conditioned ANO as an effective approach for enhancing QNNs.
Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ans\"atze while using fewer entangling gates, establishing AutoQSense as a resource-aware approach to adaptive and hardware-compatible quantum sensing.
Training quantum neural networks (QNNs) on near-term hardware remains hampered by two compounding difficulties: the exponential vanishing of gradient variance known as the barren plateau, and the $\mathcal{O}(L \cdot 2^n)$ time and memory cost of differentiating through an $n$-qubit, $L$-layer circuit. We propose RLQ-G...
This paper repurposes uniformly controlled rotations from static state preparation and static data encoding into a state-encoding interface for quantum neural networks, elevating the process of integrating classical states into quantum neural networks to an independent method-ological layer and achieves a more stable p...
Jun-Chen Han, Feng-Tao Xiang, Hao Shi et al.· Chinese Physics B· 0 citations
Building a quantum machine learning (QML) model competitive with a classical baseline currently requires a practitioner to separately choose a circuit architecture, a data-encoding scheme, a model paradigm (kernel versus variational), and a set of training hyperparameters, then verify after the fact that the chosen cir...
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
This work introduces an adaptive relational learning framework for multi-instance quantum data that accesses pairwise and higher-order relations and opens routes to sensing and quantum-data applications where adaptive photonic QML can access relational features that are costly to recover with non-adaptive, measure-firs...
Marcin Jastrzębski, Yu Shang, Raj B. Patel et al.· 0 citations
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