A new qubit-efficient HUBO encoding for graph partitioning problems like minimum coloring uses logarithmic bits and a lexicographic penalty to cut resources while providing provable optimality conditions.
Cerezoet al., Variational quantum al- gorithms, Nat
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Fractional OAM charge ℓ=1.5 optimizes twisted GKP lattices, cutting error probability by 23.9× versus square lattices at fixed Fisher information.
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
Harmoniq approximates a quantum-harmonic-analysis data augmentation operator as a mixture of at most quadratic-depth n-qubit circuits, enabling modular combination with other quantum subroutines for signal denoising.
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
Feedback calibration policies outperform open-loop baselines in low-latency quantum runtime regimes when workloads are quality-sensitive and start with aged calibrations.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.
A commutativity-based dynamic ansatz within DMET enables ground-state simulations of molecules up to 144 qubits using at most 20 qubits at a time with improved accuracy and lower gate counts than standard approaches.
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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.
Compact binary-register encoding and divide-and-conquer execution enable high-success variational quantum solutions to small TSP instances with reduced qubit overhead.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
Most institutions should start with the smallest quantum capability layer that delivers repeatable near-term value and builds expertise rather than acquiring large on-premises systems.
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.
citing papers explorer
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Qubit-efficient and gate-efficient encodings of graph partitioning problems for quantum optimization
A new qubit-efficient HUBO encoding for graph partitioning problems like minimum coloring uses logarithmic bits and a lexicographic penalty to cut resources while providing provable optimality conditions.
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OAM-Induced Lattice Rotation Reveals a Fractional Optimum in Fault-Tolerant GKP Quantum Sensing
Fractional OAM charge ℓ=1.5 optimizes twisted GKP lattices, cutting error probability by 23.9× versus square lattices at fixed Fisher information.
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The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Harmoniq: Efficient Data Augmentation on a Quantum Computer Inspired by Harmonic Analysis
Harmoniq approximates a quantum-harmonic-analysis data augmentation operator as a mixture of at most quadratic-depth n-qubit circuits, enabling modular combination with other quantum subroutines for signal denoising.
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Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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Runtime Calibration as State-Trajectory Feedback Control in Quantum-Classical Workflows
Feedback calibration policies outperform open-loop baselines in low-latency quantum runtime regimes when workloads are quality-sensitive and start with aged calibrations.
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Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
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One Coordinate at a Time: Convergence Guarantees for Rotosolve in Variational Quantum Algorithms
Rotosolve converges to ε-stationary points for smooth non-convex objectives and ε-suboptimal points under PL, with explicit worst-case rates in the finite-shot regime, outperforming or matching RCD in nuanced ways.
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Advancing Practical Quantum Embedding Simulations via Operator Commutativity Based State Preparation for Complex Chemical Systems
A commutativity-based dynamic ansatz within DMET enables ground-state simulations of molecules up to 144 qubits using at most 20 qubits at a time with improved accuracy and lower gate counts than standard approaches.
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Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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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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A Resource-Efficient Variational Quantum Framework for the Traveling Salesman Problem
Compact binary-register encoding and divide-and-conquer execution enable high-success variational quantum solutions to small TSP instances with reduced qubit overhead.
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Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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What quantum computer to buy?
Most institutions should start with the smallest quantum capability layer that delivers repeatable near-term value and builds expertise rather than acquiring large on-premises systems.
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Quantum Decoding Algorithms: Quantum Speedups in Optimization
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.