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.
Quantum Circuit Learning
6 Pith papers cite this work. Polarity classification is still indexing.
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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 learned shallow circuit trained on conserved charges and limited dynamics preserves observables better than direct noisy simulation of deeper circuits in integrable spin chain models.
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.
Brickwall quantum circuits with Haar-random, dual-unitary, and solvable two-qubit gates serve as effective reservoirs for temporal processing tasks, with performance correlated to circuit dynamics and validated on synthetic prediction benchmarks.
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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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 learned shallow circuit trained on conserved charges and limited dynamics preserves observables better than direct noisy simulation of deeper circuits in integrable spin chain models.
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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 quantum circuits with Haar-random, dual-unitary, and solvable two-qubit gates serve as effective reservoirs for temporal processing tasks, with performance correlated to circuit dynamics and validated on synthetic prediction benchmarks.
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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.