Pauli Envelope framework enables optimal loss-distance correction (d_loss ~ d) for rotated surface codes via Mid-SWAP circuits and Envelope-MLE decoder, with simulations showing up to 40% higher thresholds.
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A VMBQC model restricted to one extra trainable parameter generates distributions that the corresponding unitary model cannot learn.
A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
A trapezoidal preparation method combined with probability distribution analysis is used to pick efficient guiding states for CVQE, demonstrated on the H2 + H2+ to H3+ + H reaction.
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
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.
Adaptive quantum ansatze outperform fixed UCCSD in ph-AFQMC projected energies for stretched H chains while using more compact circuits.
The superposition of product states ansatz achieves high accuracy for ground state search in 1D and 3D tilted Ising models with short- and long-range interactions as well as random networks.
citing papers explorer
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Achieving Optimal-Distance Atom-Loss Correction via Pauli Envelope
Pauli Envelope framework enables optimal loss-distance correction (d_loss ~ d) for rotated surface codes via Mid-SWAP circuits and Envelope-MLE decoder, with simulations showing up to 40% higher thresholds.
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Minimizing classical resources in variational measurement-based quantum computation for generative modeling
A VMBQC model restricted to one extra trainable parameter generates distributions that the corresponding unitary model cannot learn.
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Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image classification.
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Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
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Probability Distribution Analysis of the Cascaded Variational Quantum Eigensolver
A trapezoidal preparation method combined with probability distribution analysis is used to pick efficient guiding states for CVQE, demonstrated on the H2 + H2+ to H3+ + H reaction.
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
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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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Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo
Adaptive quantum ansatze outperform fixed UCCSD in ph-AFQMC projected energies for stretched H chains while using more compact circuits.
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Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
The superposition of product states ansatz achieves high accuracy for ground state search in 1D and 3D tilted Ising models with short- and long-range interactions as well as random networks.