NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
Quantum Circuit Learning
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
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A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
A shallow quantum circuit learned from conserved charges and a single dynamical observable reproduces integrable spin-chain dynamics and suffers less from simulated noise than the original deep circuit.
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.
Brickwall circuits from Haar-random, dual-unitary, and solvable two-qubit gates are tested as quantum reservoirs, showing effective fading memory and prediction accuracy on synthetic time-series data.
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.
citing papers explorer
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Ravines in quantum cost landscapes: opportunities for improved VQA predictions
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
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Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
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Effective Noise Mitigation via Quantum Circuit Learning in Quantum Simulation of Integrable Spin Chains
A shallow quantum circuit learned from conserved charges and a single dynamical observable reproduces integrable spin-chain dynamics and suffers less from simulated noise than the original deep circuit.
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PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.
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Evaluating quantum circuits in the reservoir computing paradigm
Brickwall circuits from Haar-random, dual-unitary, and solvable two-qubit gates are tested as quantum reservoirs, showing effective fading memory and prediction accuracy on synthetic time-series data.
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Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
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Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.