Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
Near-term quantum algorithms for linear systems of equations
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Bias in SMO-VQE can be estimated without extra measurements; a regularization method that mimics error accumulation while preserving unbiased estimates improves performance across system sizes and Hamiltonians.
A hybrid algorithm reduces the SDP dimension for Bayes-optimal state discrimination from dL to NL via Gram matrix reformulation and quantum preprocessing from source circuits, enabling solutions for large changepoint and error-classification instances.
Conic extensions of parameterized quantum circuits enable jumps from barren plateaus to fertile valleys via non-unitary operations and ancilla, reducing optimal jump selection to a generalized eigenvalue problem and improving QAOA sampling in simulations.
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
citing papers explorer
-
Quantum Simulation of Differential-Algebraic Equations with Applications to Unsteady Stokes Flow
Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
-
Bias Analysis and Regularization of Sequential Minimal Optimization in Variational Quantum Eigensolvers
Bias in SMO-VQE can be estimated without extra measurements; a regularization method that mimics error accumulation while preserving unbiased estimates improves performance across system sizes and Hamiltonians.
-
A hybrid quantum-classical algorithm for Bayes-optimal quantum state discrimination using the source code
A hybrid algorithm reduces the SDP dimension for Bayes-optimal state discrimination from dL to NL via Gram matrix reformulation and quantum preprocessing from source circuits, enabling solutions for large changepoint and error-classification instances.
-
From barren plateaus through fertile valleys: Conic extensions of parameterised quantum circuits
Conic extensions of parameterized quantum circuits enable jumps from barren plateaus to fertile valleys via non-unitary operations and ancilla, reducing optimal jump selection to a generalized eigenvalue problem and improving QAOA sampling in simulations.
-
A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.