New theoretical results prove Trotter error depends on diagonal BCH elements in the Hamiltonian eigenbasis, paired with O(n) compact BCH representations and software that enable accurate error estimates up to 100+ qubits.
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PennyLane: Automatic differentiation of hybrid quantum-classical computations
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abstract
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for hardware providers including the Xanadu Cloud, Amazon Braket, and IBM Quantum, allowing PennyLane optimizations to be run on publicly accessible quantum devices. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, JAX, and Autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.
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- abstract PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework co
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representative citing papers
A fermionic permutation protocol on 2D nearest-neighbor grids achieves the optimal O(sqrt(N)) depth with O(N sqrt(N)) gates, no ancillas, and extends to Jordan-Wigner, Bravyi-Kitaev, and Parity encodings via Hilbert-curve layout.
Qvine uses vine copula-inspired quantum circuit structures to achieve linear or quadratic depth scaling for loading high-dimensional distributions with high approximation quality.
Symmetry reductions in QAOA for MaxCut can collapse DLA dimensions from exponential to quadratic depending on the fixed variable, with graph embeddings ensuring expressivity and improved trainability.
CLAIMSTAB-QC audits 455 comparative claims from 119 quantum-software papers and identifies a materialization gap where only 8 claims provide enough matched evidence for direct auditing, yielding 2 sustained, 4 unresolved, and 2 reversed outcomes.
A randomized algorithm recovers the exact Pauli decomposition of k-sparse n-qubit matrices in poly(n, k, log(1/δ)) time with high probability under sparse query access.
MetaMorphQ defines five physics-derived invariants for VQE circuits that enable oracle-free testing with zero false positives and Youden's J of 0.57 on 500 benchmarks versus 0.02 for convergence testing.
A neural network is trained to predict parameters of a fixed quantum circuit, enabling high-fidelity quantum state preparation from classical data in one inference step with up to 0.992 fidelity on unseen MNIST and Fashion-MNIST images.
A learning-based framework constructs logical operations for arbitrary quantum codes and co-designs non-additive encodings with noise models and desired gate sets via VarEFTQC.
New SelectCopy architecture and qubit-constrained optimizations reduce QROM Toffoli cost from ~2N/λ to ~(1 + 1/b)N/λ while preserving the ability to trade dirty qubits for lower gate count.
Introduces the CULT threat model with four circuit-level attacks on quantum federated learning and shows they degrade accuracy on MNIST and CIFAR-10 even when defenses like Krum are used.
QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
NVQLS introduces the first hybrid quantum-classical unsupervised operator learning method for parametric PDEs via Legendre-Galerkin weak form, sign ambiguity resolution, and neural embedding.
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
Gated QKAN-FWP combines fast weight programming with quantum-inspired Kolmogorov-Arnold networks via single-qubit DARUAN activations and gated updates to deliver a 12.5k-parameter model that outperforms larger classical RNNs on long-horizon solar forecasting while running on NISQ devices.
At fixed encoding budget, serial QNN architectures suffer unbounded structural gradient starvation via rank(J) ≤ 2L+1 while parallel ones keep full Jacobian rank and better parameter efficiency when adding feature-map layers.
The hardware-compatible Brick-Circuit generator produces quantum test states with higher expressibility and entanglement than existing generators at shallower circuit depths.
QIBP adapts interval bound propagation to quantum neural networks for certified adversarial robustness via interval and affine arithmetic implementations.
A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.
A new method for unitary synthesis on quantum hardware cuts CNOT gates by up to 36% and compiles up to 553 times faster than standard tools on square and heavy-hex lattices.
Q-SINDy adds quantum kernels to SINDy and proves that orthogonalization eliminates coefficient cannibalization bias exactly, recovering equations as accurately as classical SINDy on six tested systems.
LP2B encoding converts Lund plane jet representations into Bloch sphere qubit states, enabling a QTTN that matches classical LundNet performance on polarization tagging and W/top tagging with three orders of magnitude fewer parameters and improved low-data regime results.
Hybrid quantum PINN for hydrology reports 3x faster convergence and 44% fewer parameters than classical PINN on Sri Lankan flood data while using physics constraints for uncertainty quantification.
A quantum residual neural network using deterministic mixtures of identity and variational unitaries to enable post-selection-free residual learning with 10x fewer gates and reported accuracies of 99% binary and 80% multi-class on image datasets.
citing papers explorer
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A QPINN Framework with Quantum Trainable Embeddings for the Lid-Driven Cavity Problem
QPINN framework with QNN-based trainable embeddings solves the lid-driven cavity problem with stable training, competitive accuracy, and fewer parameters than classical PINNs.
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Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.
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The power of entanglement in distributed quantum machine learning
Entanglement improves classification accuracy in distributed quantum ML tasks across datasets, but excessive amounts degrade performance by reducing effective parameter dimension.
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Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
Hybrid agent with variational quantum circuits for feature extraction in hierarchical RL outperforms classical baselines with 66% parameter savings, but quantum value estimation degrades results.
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Quantum Tilted Loss in Variational Optimization: Theory and Applications
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
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Geometric Quantum Physics Informed Neural Network
GQPINNs add symmetry awareness to quantum PINNs via equivariant circuits, yielding lower mean absolute error and fewer parameters than standard QPINNs on linear and nonlinear PDE benchmarks.
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Towards Real-time Control of a CartPole System on a Quantum Computer
A single-qubit quantum reinforcement learning agent solves CartPole faster than classical networks and quantifies shot-count versus control-frequency requirements for real-time closed-loop control on NISQ hardware, including direct electronics programming to reduce latency.
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Universality of Quantum Gates in Particle and Symmetry Constrained Subspaces
Hardware-efficient gates are universal for state preparation in particle-number and symmetry-constrained subspaces because commutators generate Pauli Z projectors that span the full so(w) and su(w) algebras.
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Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders
A quantum autoencoder purifies adversarial perturbations for quantum classifiers and supplies a confidence score for unrecoverable inputs, claiming up to 68% accuracy gains over prior defenses without adversarial training.
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Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.
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QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
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Beyond Single Trajectories: Optimal Control and Jordan-Lie Algebra in Hybrid Quantum Walks for Combinatorial Optimization
Hybrid quantum walks with optimal-control-derived dynamical coins generate larger Jordan-Lie algebras than QAOA and show faster convergence and higher accuracy on Max-Cut and MIS instances.
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QuanForge: A Mutation Testing Framework for Quantum Neural Networks
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
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Assessing System Capabilities and Bottlenecks of an Early Fault-Tolerant Bicycle Architecture
Syn@fac optimization reduces estimated circuit failure probability by a factor of 9 on average across non-Clifford benchmarks for bivariate bicycle code modular FTQC architectures, with additional gains from transvection deferral and Clifford insertion.
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Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface
The Eclipse Qrisp BlockEncoding interface provides high-level programming abstractions for block-encodings, enabling easier implementation of quantum algorithms such as QSVT, matrix inversion, and Hamiltonian simulation.
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Hierarchical Progressive Pauli Noise Modeling with Residual Compensation for Multi-Qubit Quantum Circuits
HPO framework reduces multi-qubit Pauli noise characterization complexity from O(4^N) to O(N·4^w) with 96.3% parameter compression on 5 qubits and raises 10-qubit HHL fidelity from 0.7431 to 0.9381.
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Double Descent in Quantum Kernel Ridge Regression
Quantum kernel ridge regression shows double descent in test risk, with the interpolation peak suppressible by regularization, via random matrix theory asymptotics in the high-dimensional limit.
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QLLVM: A Scalable Quantum-Classical Co-Compilation Framework based on LLVM
QLLVM delivers an LLVM-based end-to-end co-compiler that unifies classical HPC and quantum programs into one executable, with a three-stage quantum path via MLIR and QIR that reduces circuit depth and gate counts on MQTBench versus prior compilers.
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Learning to Concatenate Quantum Codes
A machine-learning approach adaptively chooses quantum code sequences for concatenation to achieve target logical error rates with far fewer qubits than standard methods for structured noise.
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Parameter-efficient Quantum Multi-task Learning
QMTL uses shared VQC encoding plus task-specific quantum ansatz heads to achieve linear parameter scaling with the number of tasks while matching or exceeding classical multi-task baselines on three benchmarks.
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Adaptive H-EFT-VA: A Provably Safe Trajectory Through the Trainability-Expressibility Landscape of Variational Quantum Algorithms
Adaptive H-EFT-VA maintains gradient variance Omega(1/poly(N)) during safe Hilbert space expansion, doubling fidelity over static H-EFT-VA on benchmarks up to 14 qubits.
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Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Random quantum circuits used as adversarial training data reduce successful attack rates on QML models for CIFAR-10 from 89.8% to 68.45% and for CINIC-10 from 94.23% to 78.68%.
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Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks
SBQE encodes data via learnable shot distributions over initial states to form mixed quantum representations, achieving 89.1% accuracy on Semeion and 80.95% on Fashion MNIST without encoding gates.
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Molecular Excited States using Quantum Subspace Methods: Accuracy, Resource Reduction, and Error-Mitigated Hardware Implementation of q-sc-EOM
Optimized q-sc-EOM on quantum hardware yields accurate excited-state energies for challenging molecular bond-breaking cases after reducing measurement scaling to O(N^5) and applying readout and symmetry error mitigation.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience
Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
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Qhronology: A Python package for studying quantum models of closed timelike curves
Qhronology is a Python package that simulates quantum closed timelike curves and quantum circuits for studying time-travel paradoxes.
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H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
H-EFT-VA enforces a UV-cutoff initialization to guarantee inverse-polynomial gradient variance while preserving volume-law entanglement and near-Haar purity in variational quantum algorithms.
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DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing
DeepQuantum is a PyTorch platform that unifies quantum circuits, photonic quantum circuits, and measurement-based quantum computing in one open-source framework for hybrid models and variational algorithms.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
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Tensor-Programmable Quantum Circuits for Solving Differential Equations
A quantum solver for PDEs is introduced via flexible matrix product operator representations with mid-circuit measurements and state-dependent norm correction to handle non-unitary dynamics.
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Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
Progressive widening MCTS with sampling action space automates quantum circuit design, cutting evaluations 10-100x and CNOT gates up to 3x versus prior MCTS on chemistry and linear-equation tasks.
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Experimentally validated quantum-secure federated learning over a multi-user quantum network
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
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Bridging Quantum Computing Paradigms toward Semiconductor Yield: A Controlled CV-versus-DV Comparison on Wafer-Map Defect Classification
Controlled comparison finds CV-QNN heads reach 79.7% accuracy versus 61.6% for DV-QNN heads on eight-class wafer defect classification, with largest gains on localized defects, though both trail the classical baseline of 85%.
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Learning Low-Energy Subspace Overlaps in Many-Body Systems with Measurement-Based and Coherent Quantum Strategies
Compares shadow-based CNNs and physics-informed QCNNs for predicting low-energy subspace overlaps in quenched 10-qubit Heisenberg chains, reporting regime-dependent R^2 performance with QCNNs more stable overall.
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Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
Training in graph-regularized quantum networks increases spectral dimension by 0.23 and enables anomaly detection via Bloch drift (ROC-AUC ≥0.9) while bosonic enhancement correlates with Fiedler splits (r=-0.50).
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A High-Performance Pauli-Algebra Framework for Large-Scale Quantum Simulations
A Julia/C++ framework with compact binary symplectic encoding and sparse Pauli representations accelerates Hamiltonian construction, VQE, and real-time dynamics in quantum many-body simulations.
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
JGRA framework extracts geometric descriptors from noise-conditioned Jacobians in QNNs after entropy-matched calibration and noise-aware training, and empirically shows these descriptors predict robustness under unseen noise.
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Effect of isotropic errors on the complexity of Grover's algorithm
Numerical simulations indicate isotropic errors degrade Grover's algorithm performance and success probability on noisy quantum hardware.
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Rademacher Complexity Bounds for Parameterized Quantum Circuits Generated by Pauli Strings
Derives Rademacher complexity bounds O(L^{3/2}/sqrt(M)) for full parameter domain and O(L/sqrt(M)) for restricted domain on Pauli-string-generated parameterized unitaries, plus comparison suggesting possible quantum advantage over classical linear models.
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PauLIB: A High-Performance Library for Processing Pauli Strings
PauLIB implements a compact bit-packed symplectic representation and SoA layout for Pauli strings, delivering 14x–21,000x speedups and 7.3x memory reduction versus existing Python frameworks at 500 qubits.
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QGCL: Quantum-Guided Clause Learning for Cryptanalytic SAT
A hybrid quantum-classical SAT solver reduces conflicts by up to 86% on AES cryptanalytic instances by using Grover search on dynamically extracted subformulas.
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PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
PennySynth raises pass@5 success on QHack quantum coding challenges by 25-28 points over a base LLM by retrieving from a curated PennyLane dataset using code-aware embeddings.
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Hybrid Quantum-Classical Corrective Diffusion Modeling for Meteorological Downscaling
Hybrid quantum-classical corrective diffusion model improves MAE and CRPS on 2020 validation wind data but exhibits a generalization gap on 2021 out-of-distribution tests.
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A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
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Q-SYNTH: Hybrid Quantum-Classical Adversarial Augmentation for Imbalanced Fraud Detection
Q-SYNTH is a hybrid framework using a parameterized quantum circuit as the generator in a GAN to create synthetic minority-class fraud samples for tabular data, which shows reduced distribution mismatch compared to classical GANs and competitive performance in downstream detection tasks.
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Large-Scale Quantum Kernels for Hyperspectral Data Classification
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.
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Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Wavelet scaling α = 1/2 separates classically simulable area-law from volume-law phases for quantum kernels in world-model latents, with empirical VideoMAE latents and a Θ(d^{-2}) variance bound implying simulation hardness and quadratic measurement costs.