8 papers
Quantum thermodynamics and semidefinite optimization: Boltzmann, Fermi-Dirac, and Bose-Einstein frameworks
Michele Minervini, Nana Liu, Dhrumil Patel +1
Here we argue how quantum thermodynamics offers a unifying interpretation for a wide class of semidefinite programs (SDPs) that arise in quantum information. Three SDP variable con…
Quantum Spectral Anomaly Detection
Yewei Yuan, Michele Minervini, Mark M. Wilde +1
A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal…
Quantum principal component analysis without eigenvector recovery
Yewei Yuan, Michele Minervini, Mark M. Wilde +1
Principal component analysis (PCA) is traditionally implemented through a covariance or kernel matrix, leading-eigenvector extraction, and hard rank- projection. These steps can…
Bose-Einstein thermal operators for semidefinite optimization
Michele Minervini, Nana Liu, Mark M. Wilde
We establish that semidefinite programs (SDPs) over the unbounded positive semidefinite cone are mathematically equivalent to thermodynamic systems of independent bosonic modes: th…
Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
Michele Minervini, Madison Chin, Jacob Kupperman +6
A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system…
Evolved Quantum Boltzmann Machines
Michele Minervini, Dhrumil Patel, Mark M. Wilde
We introduce evolved quantum Boltzmann machines as a variational ansatz for quantum optimization and learning tasks. Given two parameterized Hamiltonians and , an ev…