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PhyslibAlpha.AlgebraicFramework.OrderUnit.State.Discrimination

State discrimination

i. Overview

A system is prepared in state `ω₀` with prior probability `p₀`, or `ω₁` with prior `p₁`. Guess which from a single yes/no test (an effect `e`): guess `0` if `e` clicks, `1` otherwise. The optimal guess succeeds with probability

`optimalSuccessProb ω₀ ω₁ p₀ p₁ = p₁ + sup_e (p₀ * ω₀ e - p₁ * ω₁ e)` (`optimalSuccessProb_eq`)

— the Helstrom bound, in the same spirit as the familiar `(1 + ‖p₀ρ₀ - p₁ρ₁‖₁) / 2` trace-distance formula, here with the effect supremum playing the trace norm's role directly.

ii. Key definitions and results

  • `successProb`, `optimalSuccessProb`
  • `optimalSuccessProb_eq`

iii. Table of contents

  • A. Success probability of a fixed test
  • B. Optimal success probability
  • C. The abstract Helstrom formula

A. Success probability of a fixed test

B. Optimal success probability

C. The abstract Helstrom formula

23 declarations

definition

Success probability p0ω0(e)+p1ω1(1−e)p_0 \omega_0(e) + p_1 \omega_1(1 - e) for state discrimination

The success probability of discriminating between two states ω0\omega_0 and ω1\omega_1 with respective prior probabilities p0p_0 and p1p_1 using a binary test ee is defined as p0ω0(e)+p1ω1(1−e)p_0 \omega_0(e) + p_1 \omega_1(1 - e) where ee is an effect (an element such that 0≤e≤10 \le e \le 1) and 1−e1 - e is its complement. This value represents the probability of correctly identifying the state by guessing 00 when the effect ee occurs and guessing 11 when its complement occurs.

theorem

Success Probability Equals Prior plus Discrimination Advantage

Let EE be an ordered additive commutative group with a unit element 11. For two states ω0,ω1:E→R\omega_0, \omega_1 : E \to \mathbb{R} (represented as unital positive linear maps) and prior probabilities p0,p1∈Rp_0, p_1 \in \mathbb{R}, the success probability of a binary test e∈Ee \in E (where 0≤e≤10 \le e \le 1) is given by: successProb(ω0,ω1,p0,p1,e)=p1+(p0ω0(e)−p1ω1(e))\text{successProb}(\omega_0, \omega_1, p_0, p_1, e) = p_1 + (p_0 \omega_0(e) - p_1 \omega_1(e)) This formula expresses the success probability as the prior probability of guessing state 11 (p1p_1) plus the discrimination advantage p0ω0(e)−p1ω1(e)p_0 \omega_0(e) - p_1 \omega_1(e) provided by the test ee.

definition

Optimal success probability sup⁡e(p0ω0(e)+p1ω1(1−e))\sup_e (p_0 \omega_0(e) + p_1 \omega_1(1 - e))

Given two states ω0,ω1\omega_0, \omega_1 (represented as unital positive linear maps from an ordered additive commutative group EE with a unit 11 to R\mathbb{R}) and their respective prior probabilities p0,p1∈Rp_0, p_1 \in \mathbb{R}, the **optimal success probability** is defined as the supremum of the success probabilities over all possible effects e∈Ee \in E (0≤e≤10 \le e \le 1): sup⁡0≤e≤1(p0ω0(e)+p1ω1(1−e))\sup_{0 \le e \le 1} (p_0 \omega_0(e) + p_1 \omega_1(1 - e)) This value represents the maximum possible probability of correctly identifying whether the system is in state ω0\omega_0 or ω1\omega_1 based on a single binary test.

theorem

The discrimination advantage p0ω0(e)−p1ω1(e)p_0 \omega_0(e) - p_1 \omega_1(e) is bounded by p0p_0

Let EE be an ordered additive commutative group with a unit element 11. For any unital positive linear maps ω0,ω1:E→R\omega_0, \omega_1: E \to \mathbb{R}, non-negative real numbers p0,p1≥0p_0, p_1 \ge 0, and any effect e∈Ee \in E (defined such that 0≤e≤10 \le e \le 1), the discrimination advantage satisfies the inequality: p0ω0(e)−p1ω1(e)≤p0p_0 \omega_0(e) - p_1 \omega_1(e) \le p_0

theorem

The advantage p0ω0(e)−p1ω1(e)p_0 \omega_0(e) - p_1 \omega_1(e) is bounded above

Let EE be a type equipped with an additive commutative group structure, a partial order, and a unit element 11. Let ω0\omega_0 and ω1\omega_1 be states (unital positive linear maps from EE to R\mathbb{R}), and let p0p_0 and p1p_1 be non-negative real prior probabilities. Then the set of values {p0ω0(e)−p1ω1(e)∣0≤e≤1}\{p_0 \omega_0(e) - p_1 \omega_1(e) \mid 0 \le e \le 1\}, representing the "advantage" or distinguishing power of an effect ee, is bounded above in R\mathbb{R}.

theorem

The abstract Helstrom bound: optimalSuccessProb=p1+sup⁡e(p0ω0(e)−p1ω1(e))\text{optimalSuccessProb} = p_1 + \sup_e (p_0 \omega_0(e) - p_1 \omega_1(e))

Let EE be an ordered additive commutative group with a unit element 11. Let ω0,ω1:E→R\omega_0, \omega_1: E \to \mathbb{R} be states (unital positive linear maps) and p0,p1≥0p_0, p_1 \ge 0 be their respective prior probabilities. The optimal success probability for distinguishing ω0\omega_0 and ω1\omega_1 is given by optimalSuccessProb(ω0,ω1,p0,p1)=p1+sup⁡0≤e≤1(p0ω0(e)−p1ω1(e))\text{optimalSuccessProb}(\omega_0, \omega_1, p_0, p_1) = p_1 + \sup_{0 \le e \le 1} (p_0 \omega_0(e) - p_1 \omega_1(e)) where the supremum is taken over all effects e∈Ee \in E (elements satisfying 0≤e≤10 \le e \le 1). This formula, known as the abstract Helstrom bound, expresses the maximum probability of correctly identifying a state as p1p_1 plus the maximum "advantage" provided by a single binary test.

definition

Base norm of the weighted difference ∥p0ω0−p1ω1∥\|p_0 \omega_0 - p_1 \omega_1\|

Let EE be an ordered additive commutative group with a unit element 11. Given two states ω0,ω1:E→R\omega_0, \omega_1 : E \to \mathbb{R} (unital positive linear maps) and real weights p0,p1p_0, p_1, the `weightedStateBaseNorm` is the base norm of the signed functional p0ω0−p1ω1p_0 \omega_0 - p_1 \omega_1. Operationally, it is defined as ∥p0ω0−p1ω1∥=2(sup⁡0≤e≤1(p0ω0(e)−p1ω1(e)))−(p0−p1)\|p_0 \omega_0 - p_1 \omega_1\| = 2 \left( \sup_{0 \le e \le 1} (p_0 \omega_0(e) - p_1 \omega_1(e)) \right) - (p_0 - p_1) where the supremum is taken over all effects e∈Ee \in E (elements satisfying 0≤e≤10 \le e \le 1). This quantity is normalized such that if p0+p1=1p_0 + p_1 = 1, the optimal success probability for distinguishing the states is given by the abstract Helstrom formula 1+∥p0ω0−p1ω1∥2\frac{1 + \|p_0 \omega_0 - p_1 \omega_1\|}{2}.

theorem

∥p0ω0−p1ω1∥≤p0+p1\|p_0 \omega_0 - p_1 \omega_1\| \le p_0 + p_1 for non-negative weights p0,p1p_0, p_1

Let EE be an ordered additive commutative group with a unit element 11. Let ω0,ω1:E→R\omega_0, \omega_1: E \to \mathbb{R} be two states, defined as unital positive linear maps. For any non-negative weights p0,p1≥0p_0, p_1 \ge 0, the operational base norm of the weighted difference satisfies: ∥p0ω0−p1ω1∥≤p0+p1\|p_0 \omega_0 - p_1 \omega_1\| \le p_0 + p_1 where the base norm is defined as ∥p0ω0−p1ω1∥=2(sup⁡0≤e≤1(p0ω0(e)−p1ω1(e)))−(p0−p1)\|p_0 \omega_0 - p_1 \omega_1\| = 2 \left( \sup_{0 \le e \le 1} (p_0 \omega_0(e) - p_1 \omega_1(e)) \right) - (p_0 - p_1), with the supremum taken over all effects e∈Ee \in E.

theorem

The operational base norm dominates the weight difference ∣p0−p1∣≤∥p0ω0−p1ω1∥|p_0 - p_1| \le \|p_0 \omega_0 - p_1 \omega_1\|

Let EE be an ordered additive commutative group with a unit element 11. Let ω0\omega_0 and ω1\omega_1 be two states on EE (represented as unital positive linear maps to R\mathbb{R}). For any non-negative real weights p0,p1≥0p_0, p_1 \ge 0, the absolute difference between the weights is bounded by the operational base norm of the weighted difference of the states: ∣p0−p1∣≤∥p0ω0−p1ω1∥|p_0 - p_1| \le \|p_0 \omega_0 - p_1 \omega_1\| where the operational base norm is defined as ∥p0ω0−p1ω1∥=2(sup⁡0≤e≤1(p0ω0(e)−p1ω1(e)))−(p0−p1)\|p_0 \omega_0 - p_1 \omega_1\| = 2 \left( \sup_{0 \le e \le 1} (p_0 \omega_0(e) - p_1 \omega_1(e)) \right) - (p_0 - p_1) and the supremum is taken over all effects e∈Ee \in E satisfying 0≤e≤10 \le e \le 1.

theorem

Abstract Helstrom Formula: optimalSuccessProb=1+∥p0ω0−p1ω1∥2\text{optimalSuccessProb} = \frac{1 + \|p_0 \omega_0 - p_1 \omega_1\|}{2}

Let EE be an ordered additive commutative group with a unit element 11. Let ω0,ω1\omega_0, \omega_1 be two states on EE, represented as unital positive linear maps to R\mathbb{R}. For any non-negative prior probabilities p0,p1≥0p_0, p_1 \ge 0 such that p0+p1=1p_0 + p_1 = 1, the optimal success probability for discriminating between the two states is given by the abstract Helstrom formula: optimalSuccessProb(ω0,ω1,p0,p1)=1+∥p0ω0−p1ω1∥2\text{optimalSuccessProb}(\omega_0, \omega_1, p_0, p_1) = \frac{1 + \|p_0 \omega_0 - p_1 \omega_1\|}{2} where ∥p0ω0−p1ω1∥\|p_0 \omega_0 - p_1 \omega_1\| is the operational base norm of the weighted difference between the states.

theorem

Data processing inequality for maximal effect advantage: sup⁡e∈[0,1]E(p0(ω0∘φ)(e)−p1(ω1∘φ)(e))≤sup⁡e∈[0,1]F(p0ω0(e)−p1ω1(e))\sup_{e \in [0, 1]_E} (p_0 (\omega_0 \circ \varphi)(e) - p_1 (\omega_1 \circ \varphi)(e)) \le \sup_{e \in [0, 1]_F} (p_0 \omega_0(e) - p_1 \omega_1(e))

Let φ:E→F\varphi : E \to F be a unital positive linear map between spaces with ordered structures and unit elements. Let ω0,ω1\omega_0, \omega_1 be states on FF and p0,p1p_0, p_1 be non-negative real weights. The maximal weighted advantage obtained by choosing an effect e∈Ee \in E (where 0≤e≤10 \le e \le 1) for the pullback states ω0∘φ\omega_0 \circ \varphi and ω1∘φ\omega_1 \circ \varphi is less than or equal to the maximal weighted advantage obtained by choosing an effect in FF: sup⁡e∈[0,1]E(p0(ω0∘φ)(e)−p1(ω1∘φ)(e))≤sup⁡e∈[0,1]F(p0ω0(e)−p1ω1(e))\sup_{e \in [0, 1]_E} \left( p_0 (\omega_0 \circ \varphi)(e) - p_1 (\omega_1 \circ \varphi)(e) \right) \le \sup_{e \in [0, 1]_F} \left( p_0 \omega_0(e) - p_1 \omega_1(e) \right) where the pullback state ω∘φ\omega \circ \varphi maps x∈Ex \in E to ω(φ(x))\omega(\varphi(x)).

theorem

Data processing inequality for weighted base norm: ∥p0(ω0∘φ)−p1(ω1∘φ)∥≤∥p0ω0−p1ω1∥\|p_0 (\omega_0 \circ \varphi) - p_1 (\omega_1 \circ \varphi)\| \le \|p_0 \omega_0 - p_1 \omega_1\|

Let φ:E→F\varphi : E \to F be a unital positive linear map between spaces with an ordered structure and a unit element. Let ω0,ω1\omega_0, \omega_1 be states on FF (i.e., unital positive linear maps F→RF \to \mathbb{R}) and p0,p1≥0p_0, p_1 \ge 0 be non-negative real weights. Let ω0∘φ\omega_0 \circ \varphi and ω1∘φ\omega_1 \circ \varphi denote the pullback states on EE. Then the base norm of the weighted difference of the pullback states is less than or equal to the base norm of the weighted difference of the original states: ∥p0(ω0∘φ)−p1(ω1∘φ)∥≤∥p0ω0−p1ω1∥\|p_0 (\omega_0 \circ \varphi) - p_1 (\omega_1 \circ \varphi)\| \le \|p_0 \omega_0 - p_1 \omega_1\| where the weighted base norm is defined as ∥p0ω0−p1ω1∥=2(sup⁡0≤e≤1(p0ω0(e)−p1ω1(e)))−(p0−p1)\|p_0 \omega_0 - p_1 \omega_1\| = 2 \left( \sup_{0 \le e \le 1} (p_0 \omega_0(e) - p_1 \omega_1(e)) \right) - (p_0 - p_1).

theorem

Data processing inequality: Popt(ω0∘φ,ω1∘φ)≤Popt(ω0,ω1)P_{\text{opt}}(\omega_0 \circ \varphi, \omega_1 \circ \varphi) \le P_{\text{opt}}(\omega_0, \omega_1)

Let EE and FF be ordered additive commutative groups with unit elements. Let φ:E→F\varphi : E \to F be a unital positive linear map (representing a channel in the Heisenberg picture). For any two states ω0,ω1\omega_0, \omega_1 on FF and non-negative prior probabilities p0,p1≥0p_0, p_1 \ge 0, the optimal success probability for discriminating the states after the action of the channel (the pullback states ω0∘φ\omega_0 \circ \varphi and ω1∘φ\omega_1 \circ \varphi) is less than or equal to the optimal success probability for discriminating the original states: optimalSuccessProb(ω0∘φ,ω1∘φ,p0,p1)≤optimalSuccessProb(ω0,ω1,p0,p1)\text{optimalSuccessProb}(\omega_0 \circ \varphi, \omega_1 \circ \varphi, p_0, p_1) \le \text{optimalSuccessProb}(\omega_0, \omega_1, p_0, p_1) where the optimal success probability is defined as the supremum over all effects ee (0≤e≤10 \le e \le 1): optimalSuccessProb(ω,ω′,p,p′)=sup⁡0≤e≤1(pω(e)+p′ω′(1−e)).\text{optimalSuccessProb}(\omega, \omega', p, p') = \sup_{0 \le e \le 1} (p \omega(e) + p' \omega'(1 - e)).

definition

Operational distance between states ω\omega and ϕ\phi

The operational distance between two states ω,ϕ∈S(R,E)\omega, \phi \in \mathcal{S}(\mathbb{R}, E) is defined as the supremum of the absolute difference between the probabilities they assign to the same effect, expressed as sup⁡e∣ω(e)−ϕ(e)∣\sup_{e} |\omega(e) - \phi(e)|, where the supremum is taken over all effects e∈Ee \in E such that 0≤e≤10 \le e \le 1.

theorem

The probability gap ∣ω(e)−ϕ(e)∣≤1|\omega(e) - \phi(e)| \le 1 for any two states and an effect ee

Let EE be an ordered additive commutative group with a unit element 11. For any two states ω,ϕ∈S[R,E]\omega, \phi \in \mathcal{S}[\mathbb{R}, E] (unital positive linear maps from EE to R\mathbb{R}) and any effect e∈Ee \in E (where 0≤e≤10 \le e \le 1), the absolute difference between the values assigned to the effect by the two states satisfies ∣ω(e)−ϕ(e)∣≤1.|\omega(e) - \phi(e)| \le 1.

theorem

The set of effect probability gaps {∣ω(e)−φ(e)∣}\{|\omega(e) - \varphi(e)|\} is bounded above

Let EE be a type equipped with an additive commutative group structure, a partial order, and a unit element 11. Let ω\omega and φ\varphi be unital positive linear maps from EE to R\mathbb{R}. An effect ee is defined as an element of EE such that 0≤e≤10 \le e \le 1. Then the set of values {∣ω(e)−φ(e)∣:0≤e≤1}\{ |\omega(e) - \varphi(e)| : 0 \le e \le 1 \} is bounded above.

theorem

The operational distance between states is non-negative (0≤operationalDistance(ω,ϕ)0 \le \text{operationalDistance}(\omega, \phi))

Let EE be an ordered additive commutative group with a unit element. For any two states ω,ϕ∈S(R,E)\omega, \phi \in \mathcal{S}(\mathbb{R}, E) (unital positive linear maps from EE to R\mathbb{R}), the operational distance between them is non-negative: 0≤operationalDistance(ω,ϕ).0 \le \text{operationalDistance}(\omega, \phi). The operational distance is defined as the supremum of the absolute difference between the probabilities the states assign to the same effect ee, expressed as sup⁡e∣ω(e)−ϕ(e)∣\sup_{e} |\omega(e) - \phi(e)|, where the supremum is taken over all effects e∈Ee \in E such that 0≤e≤10 \le e \le 1.

theorem

operationalDistance(ω,ϕ)≤1\text{operationalDistance}(\omega, \phi) \le 1

Let EE be an ordered additive commutative group with a unit element 11. For any two states ω,ϕ∈S(R,E)\omega, \phi \in \mathcal{S}(\mathbb{R}, E) (defined as unital positive linear maps from EE to R\mathbb{R}), the operational distance between them is at most 11: operationalDistance(ω,ϕ)≤1.\text{operationalDistance}(\omega, \phi) \le 1. The operational distance is defined as the supremum of the absolute difference between the values assigned by the states to the same effect ee, expressed as sup⁡e∣ω(e)−ϕ(e)∣\sup_{e} |\omega(e) - \phi(e)|, where ee ranges over all effects in EE such that 0≤e≤10 \le e \le 1.

theorem

operationalDistance(ω,ω)=0\text{operationalDistance}(\omega, \omega) = 0

For any state ω∈S(R,E)\omega \in \mathcal{S}(\mathbb{R}, E) (a unital positive linear map from EE to R\mathbb{R}), the operational distance from the state to itself is zero: operationalDistance(ω,ω)=0.\text{operationalDistance}(\omega, \omega) = 0.

theorem

Symmetry of the Operational Distance: operationalDistance(ω,ϕ)=operationalDistance(ϕ,ω)\text{operationalDistance}(\omega, \phi) = \text{operationalDistance}(\phi, \omega)

For any two states ω,ϕ∈S(R,E)\omega, \phi \in \mathcal{S}(\mathbb{R}, E), the operational distance between ω\omega and ϕ\phi is equal to the operational distance between ϕ\phi and ω\omega: operationalDistance(ω,ϕ)=operationalDistance(ϕ,ω)\text{operationalDistance}(\omega, \phi) = \text{operationalDistance}(\phi, \omega) where the operational distance is defined as the supremum of the absolute difference between the probabilities assigned to an effect ee by the two states, sup⁡e∣ω(e)−ϕ(e)∣\sup_{e} |\omega(e) - \phi(e)|.

theorem

The operational distance between states ω\omega and ϕ\phi is 00 if and only if ω=ϕ\omega = \phi

For any two states ω,ϕ∈S(R,E)\omega, \phi \in \mathcal{S}(\mathbb{R}, E), the operational distance between them is zero if and only if the states are identical (ω=ϕ\omega = \phi). Here, the operational distance is defined as dop(ω,ϕ)=sup⁡0≤e≤1∣ω(e)−ϕ(e)∣d_{\text{op}}(\omega, \phi) = \sup_{0 \le e \le 1} |\omega(e) - \phi(e)|, where the supremum is taken over all effects ee.

theorem

Triangle Inequality for Operational Distance between States

For any states ω,ϕ,ψ∈S(R,E)\omega, \phi, \psi \in \mathcal{S}(\mathbb{R}, E), the operational distance satisfies the triangle inequality: operationalDistance(ω,ψ)≤operationalDistance(ω,ϕ)+operationalDistance(ϕ,ψ)\text{operationalDistance}(\omega, \psi) \le \text{operationalDistance}(\omega, \phi) + \text{operationalDistance}(\phi, \psi) where the operational distance between two states is defined as the supremum of the absolute difference between the probabilities they assign to the same effect ee, i.e., sup⁡e∣ω(e)−ϕ(e)∣\sup_{e} |\omega(e) - \phi(e)|.

theorem

Data Processing Inequality for Operational Distance

Let EE and FF be spaces and φ:E→F\varphi : E \to F be a unital positive linear map (representing a quantum channel in the Heisenberg picture). For any two states ω,ψ\omega, \psi on FF, the operational distance between their pullback states ω∘φ\omega \circ \varphi and ψ∘φ\psi \circ \varphi on EE is less than or equal to the operational distance between ω\omega and ψ\psi: dop(ω∘φ,ψ∘φ)≤dop(ω,ψ)d_{\text{op}}(\omega \circ \varphi, \psi \circ \varphi) \le d_{\text{op}}(\omega, \psi) where the operational distance is defined as dop(ω,ψ)=sup⁡0≤e≤1∣ω(e)−ψ(e)∣d_{\text{op}}(\omega, \psi) = \sup_{0 \le e \le 1} |\omega(e) - \psi(e)|, with the supremum taken over all effects ee.