QETLAB:Change log/v0.9
From QETLAB
New functions
- BCSGameLB: Computes a lower bound on the quantum value of a binary constraint system (BCS) game.
- BCSGameValue: Computes the maximum value of a binary constraint system (BCS) game. In classical and no-signalling settings, the value computed is exact, but the quantum value is just an upper bound.
- InducedMatrixNorm: Computes a lower bound on the induced p->q norm of a matrix.
- InducedSchattenNorm: Computes a lower bound on the induced Schatten p->q norm of a superoperator.
- L1NormCoherence: Computes the ℓ_{1}-norm of coherence of a quantum state.
- NonlocalGameLB: Computes a lower bound on the quantum value of a two-player non-local game.
- RandomPOVM: Computes a random POVM of a specified size and with a specified number of outcomes.
- RelEntCoherence: Computes the relative entropy of coherence of a quantum state.
- RobustnessCoherence: Computes the robustness of coherence of a quantum state.
- TraceDistanceCoherence: Computes the trace distance of coherence of a quantum state.
- helpers/bcs_to_nonlocal: Converts a description of a binary constraint system (BCS) game into a form that can be presented as a general non-local game.
- helpers/pure_to_mixed: Converts a state vector or density matrix representation of a state to a density matrix.
Changes to existing functions
- Entropy: Fixed a bug that would cause NaN output for some low-rank input states.
- kpNorm: Can now be used as the objective function or as a constraint in a CVX optimization problem, regardless of k and p (only certain special values of k and p were supported previously).
- kpNormDual: Can now be used as the objective function or as a constraint in a CVX optimization problem, regardless of k and p (only certain special values of k and p were supported previously).
- NonlocalGameValue: Added the REPT optional input argument, which lets the user specify the number of times that the non-local game will be repeated in parallel.
- SchattenNorm: Can now be used as the objective function or as a constraint in a CVX optimization problem, regardless of p (only p = 1, p = 2, and p = Inf were supported previously).