Quantum parallel repetition, part 01 #
The local matrix norm instance used while elaborating part one.
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The local matrix norm instance used while elaborating part one.
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A finite two-player nonlocal game with a question distribution and winning predicate.
- questionWeight : X → Y → ℝ
The probability weight assigned to a pair of questions.
- predicate : X → Y → A → B → Bool
The Boolean predicate deciding whether a pair of answers wins.
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A finite-outcome positive operator-valued measurement.
The positive operator associated with a measurement outcome.
- positive (i : ι) : (self.effect i).PosSemidef
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A finite-dimensional entangled strategy for a nonlocal game.
- Alice : Type
Alice's finite Hilbert-space basis type.
- Bob : Type
Bob's finite Hilbert-space basis type.
- aliceDecidableEq : DecidableEq self.Alice
- bobDecidableEq : DecidableEq self.Bob
- state : DensityMatrix (self.Alice × self.Bob)
The shared bipartite state of the strategy.
Alice's measurement for each question.
Bob's measurement for each question.
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The Born probability of a question-and-answer outcome.
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- S.outcomeProbability x y a b = (S.state.matrix * QuantumParallelRepetition.Strategy.jointEffect✝ S x y a b).trace.re
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The winning probability of the strategy.
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- S.winProbability = ∑ x : X, ∑ y : Y, G.questionWeight x y * ∑ a : A, ∑ b : B, if G.predicate x y a b = true then S.outcomeProbability x y a b else 0
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The quadratic expectation construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.quadraticExpectation W z = (inner ℂ z (W z)).re
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The measurement effect for winning.
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- S.winningEffect x y = ∑ a : A, ∑ b : B, if G.predicate x y a b = true then QuantumParallelRepetition.Strategy.jointEffect✝ S x y a b else 0
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The matrix representation of pure density.
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- QuantumParallelRepetition.pureDensityMatrix z hz = { matrix := Matrix.vecMulVec z.ofLp (star z.ofLp), positive := ⋯, trace_one := ⋯ }
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The strategy implementing pure vector.
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The strategy implementing pure flagged.
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A normalized nonnegative probability law on a finite sample space.
- weight : Ω → ℝ
The probability weight of a sample point.
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The probability mass of a finite event.
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The event on which every selected coordinate wins.
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- QuantumParallelRepetition.FiniteEventLaw.winEvent wins D = {ω : Ω | ∀ i ∈ D, wins i ω = true}
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The conditional mass of failure at a selected coordinate.
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- law.failureMass wins D i = law.eventMass (QuantumParallelRepetition.FiniteEventLaw.winEvent wins D) - law.eventMass (QuantumParallelRepetition.FiniteEventLaw.winEvent wins (insert i D))
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The type used to represent strategy outcome in the exact sampling construction.
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- QuantumParallelRepetition.StrategyOutcome X Y A B = (X × Y × A × B)
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The finite probability law for strategy event.
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The repeated coordinate win construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.repeatedCoordinateWin G n i ω = G.predicate (ω.1 i) (ω.2.1 i) (ω.2.2.1 i) (ω.2.2.2 i)
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The continuous linear map implementing spectral conjugation.
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The spectral filter for spectral purification.
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The born trace pairing construction used in the quantum parallel-repetition argument.
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The conditional y given x construction used in the quantum parallel-repetition argument.
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- G.conditionalYGivenX x y = G.questionWeight x y / G.marginalX x
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The conditional x given y construction used in the quantum parallel-repetition argument.
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- G.conditionalXGivenY y x = G.questionWeight x y / G.marginalY y
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The finite average of conditional bob.
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- QuantumParallelRepetition.conditionalBobAverage G K x = ∑ y : Y, G.conditionalYGivenX x y • K y
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The finite average of conditional alice.
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- QuantumParallelRepetition.conditionalAliceAverage G H y = ∑ x : X, G.conditionalXGivenY y x • H x
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The measurement effect for conditioned alice.
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The measurement effect for conditioned bob.
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The spectral support functional construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.spectralSupportFunctional F hF f = ((Unitary.conjStarAlgAut ℂ (Matrix d d ℂ)) ⋯.eigenvectorUnitary) (Matrix.diagonal fun (i : d) => ↑(f (⋯.eigenvalues i)))
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The positive square-root construction for spectral support.
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The positive operator-valued measurement implementing purified refined.
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- QuantumParallelRepetition.purifiedRefinedPOVM F hF Γ hΓ effect hpositive hsum a₀ = { effect := QuantumParallelRepetition.purifiedRefinedEffect✝ F hF Γ effect a₀, positive := ⋯, complete := ⋯ }
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The set of coordinates remaining after full history.
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- QuantumParallelRepetition.fullHistoryRemaining n D L = (Finset.univ \ D) \ L
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A partial transcript split into conditioned, revealed, and remaining coordinates.
- aliceConditioned : ↥D → X
Alice's questions on conditioned coordinates.
- bobConditioned : ↥D → Y
Bob's questions on conditioned coordinates.
- aliceRevealed : ↥L → X
Alice's questions on revealed coordinates.
- bobRemaining : ↥(fullHistoryRemaining n D L) → Y
Bob's questions on the remaining coordinates.
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The full history alice question construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.fullHistoryAliceQuestion h hidden i = if hiD : i ∈ D then h.aliceConditioned ⟨i, hiD⟩ else if hiL : i ∈ L then h.aliceRevealed ⟨i, hiL⟩ else hidden ⟨i, ⋯⟩
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The full history bob question construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.fullHistoryBobQuestion h hidden i = if hiD : i ∈ D then h.bobConditioned ⟨i, hiD⟩ else if hiL : i ∈ L then hidden ⟨i, hiL⟩ else h.bobRemaining ⟨i, ⋯⟩
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The probability weight for full history.
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The probability weight for full history hidden alice.
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- QuantumParallelRepetition.fullHistoryHiddenAliceWeight G h hidden = ∏ i : ↥(QuantumParallelRepetition.fullHistoryRemaining n D L), G.conditionalXGivenY (h.bobRemaining i) (hidden i)
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The probability weight for full history hidden bob.
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- QuantumParallelRepetition.fullHistoryHiddenBobWeight G h hidden = ∏ i : ↥L, G.conditionalYGivenX (h.aliceRevealed i) (hidden i)
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The spectral filter for full history alice.
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The spectral filter for full history bob.
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The indicator function for full history win.
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- QuantumParallelRepetition.fullHistoryWinIndicator G h α β = if ∀ (i : ↥D), G.predicate (h.aliceConditioned i) (h.bobConditioned i) (α i) (β i) = true then 1 else 0
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The weighted spectral filter variance construction used in the quantum parallel-repetition argument.
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The potential function controlling full history alice entropy.
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The finite atom representing positive matrix spectral.
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The quantum state representing raw embezzlement.
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The harmonic number construction used in the quantum parallel-repetition argument.
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- QuantumParallelRepetition.harmonicNumber n = ∑ j : Fin n, (↑↑j + 1)⁻¹
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The quantum state representing embezzlement.
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The type used to represent bipartite unit vector in the exact sampling construction.
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The overlap quantity for spectral atom.
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The finite outcome encoding for coherent binary joint.
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- QuantumParallelRepetition.coherentBinaryJointOutcome P Q z a b = WithLp.toLp 2 ((Matrix.kroneckerMap (fun (x1 x2 : ℂ) => x1 * x2) (P.effect a) (Q.effect b)).mulVec z.ofLp)
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The state vector representing finite tensor.
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- QuantumParallelRepetition.finiteTensorVector v = WithLp.toLp 2 fun (q : ι → d) => ∏ i : ι, (v i).ofLp (q i)
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The probability of binary born.
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The probability of binary continue.
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The probability of binary joint success.
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The probability of binary mismatch.
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The positive operator-valued measurement implementing transpose.
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The target object for tensor embezzlement.
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The operator action for local unitary.
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- QuantumParallelRepetition.localUnitaryAction U V ψ = WithLp.toLp 2 ((Matrix.kroneckerMap (fun (x1 x2 : ℂ) => x1 * x2) ↑U ↑V).mulVec ψ.ofLp)
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The overlap quantity for unitary basis.
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The quantum state representing diagonal schmidt.
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- QuantumParallelRepetition.diagonalSchmidtState σ = WithLp.toLp 2 fun (q : d × d) => if q.1 = q.2 then ↑(σ q.1) else 0
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The state vector representing schmidt.
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The unitary operator implementing orthonormal basis.
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The unitary operator implementing conjugate.