A new qubit-efficient HUBO encoding for graph partitioning problems like minimum coloring uses logarithmic bits and a lexicographic penalty to cut resources while providing provable optimality conditions.
McArdle, T
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QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
CBMD decomposes non-Hermitian operators via contour residues to enable optimal-query quantum simulation of first-order dynamics and special functions such as Bessel and Airy evolutions without requiring diagonalizability.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
Quantum circuits for single and double fermionic excitations on ion traps reduce MS gate counts by factors of 2 and 4 respectively by using global interactions for optimal parallelism.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
Quantum simulation methods for Thirring and Gross-Neveu fermionic models with arbitrary flavors, including gate complexity bounds and ground-state preparation up to 20 qubits.
Empirical scaling study reports VQS requires shallower circuits than Trotterization for time evolution as system size and simulation time grow.
svPITE is a Python package for ground-state preparation via probabilistic imaginary-time evolution, supporting state-vector and shot-based modes with exact-diagonalization benchmarking and interoperability for dynamical observables.
citing papers explorer
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Qubit-efficient and gate-efficient encodings of graph partitioning problems for quantum optimization
A new qubit-efficient HUBO encoding for graph partitioning problems like minimum coloring uses logarithmic bits and a lexicographic penalty to cut resources while providing provable optimality conditions.
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Quantum Randomized Subspace Iteration
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
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Quantum Simulation of Non-Hermitian Special Functions and Dynamics via Contour-based Matrix Decomposition
CBMD decomposes non-Hermitian operators via contour residues to enable optimal-query quantum simulation of first-order dynamics and special functions such as Bessel and Airy evolutions without requiring diagonalizability.
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Sampling (noisy) quantum circuits through randomized rounding
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
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Improved Strategies for Fermionic Quantum Simulation with Global Interactions
Quantum circuits for single and double fermionic excitations on ion traps reduce MS gate counts by factors of 2 and 4 respectively by using global interactions for optimal parallelism.
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Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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Quantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors
Quantum simulation methods for Thirring and Gross-Neveu fermionic models with arbitrary flavors, including gate complexity bounds and ground-state preparation up to 20 qubits.
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Performance and scaling analysis of variational quantum simulation
Empirical scaling study reports VQS requires shallower circuits than Trotterization for time evolution as system size and simulation time grow.
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svPITE: A Python package for the state-vector-based probabilistic imaginary-time evolution algorithm
svPITE is a Python package for ground-state preparation via probabilistic imaginary-time evolution, supporting state-vector and shot-based modes with exact-diagonalization benchmarking and interoperability for dynamical observables.