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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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
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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.
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.
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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.
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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.
A formal audit of 45 quantum simulators identifies 547 vulnerabilities across memory corruption, resource exhaustion, code injection, and a new QASM injection class, with all patterns verified by Z3 SAT proofs.
citing papers explorer
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Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
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.
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Lund Plane to Bloch (LP2B) Encoding for Object and Polarization Tagging with Quantum Jet Substructure
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.
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The Lie Algebra of XY-mixer Topologies and Warm Starting QAOA for Constrained Optimization
The paper decomposes dynamical Lie algebras of XY-mixer topologies and demonstrates warm-starting QAOA via pre-training on restricted generators to improve convergence on constrained optimization problems.
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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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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.
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Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.
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Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates
Angle encoding in hybrid quantum logistic regression yields the strongest performance among quantum variants, matching classical baselines in discrimination and achieving the lowest calibration error on pulsar candidate data.
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A Comprehensive Analysis of Accuracy and Robustness in Quantum Neural Networks
QCNN, QRNN, and QViT perform well on low-feature data but degrade on high-feature datasets, with QViT most robust to quantum noise and classical-style models better against adversarial noise.