The Projector Variational Ansatz (PVA) is a new VQE ansatz that can match ISQ-QSP or ADAPT-VQE structures and converges with shallower circuits than standard ADAPT-VQE in experiments.
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A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.
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Projector Quantum Variational Ansatz
The Projector Variational Ansatz (PVA) is a new VQE ansatz that can match ISQ-QSP or ADAPT-VQE structures and converges with shallower circuits than standard ADAPT-VQE in experiments.
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Quantum hardware noise learning via differentiable Kraus representation on tensor networks
A differentiable tensor-network framework learns CPTP noise channels from single-circuit measurement data on IBM hardware and generalizes the model to unrelated circuits.
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Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.