Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
Unsupervised Machine Learning on a Hybrid Quantum Computer
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abstract
Machine learning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.
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The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
The thesis compiles applications of maximal entanglement in quantum information and claims it constrains the QED vertex to predict a weak mixing angle near the measured value.
Review of proposals and experiments using coupled cavity arrays and superconducting circuits to realize many-body physics with photons, including Mott transitions, fractional quantum Hall states, and dissipative phase transitions.
citing papers explorer
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Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
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Adaptive Conformal Prediction for Quantum Machine Learning
The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
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Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Quantum neuromorphic kernels outperform parameterized quantum kernels on low-dimensional datasets like Iris but underperform on high-dimensional SDSS data in spectral clustering tasks.
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Maximal Entanglement: Applications in Quantum Information and Particle Physics
The thesis compiles applications of maximal entanglement in quantum information and claims it constrains the QED vertex to predict a weak mixing angle near the measured value.
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Many-Body Physics and Quantum Simulations with Strongly Interacting Photons
Review of proposals and experiments using coupled cavity arrays and superconducting circuits to realize many-body physics with photons, including Mott transitions, fractional quantum Hall states, and dissipative phase transitions.