add to tf-qas presentation

presentations/tf-qas.typ

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+
+#import "@preview/touying:0.6.1": *
+#import "@preview/physica:0.9.5": *
+#import "@preview/cetz:0.3.4"
+#import "@preview/typsium:0.2.0": ce
+#import "@preview/numbly:0.1.0": numbly
+#import "./theme.typ": *
+
+#set heading(numbering: numbly("{1}.", default: "1.1"))
+#show ref: set text(size:0.5em, baseline: -0.75em)
+
+#let cetz-canvas = touying-reducer.with(reduce: cetz.canvas, cover: cetz.draw.hide.with(bounds: true))
+
+
+#show: university-theme.with(
+  config-info(
+    title: "Implementation Specific QAS", // Required
+    date: datetime.today().display(),
+    authors: ("Noa Aarts"),
+  
+    // Optional Styling (for more / explanation see in the typst universe)
+    // ignore how bad the images look i'll adjust it until Monday
+    title-color: blue.darken(10%),
+  ),
+  config-common(
+    // handout: true, // enable this for a version without animations
+  ),
+  aspect-ratio: "16-9",
+  config-colors(
+   primary: rgb("#00a6d6"),
+   secondary: rgb("#00b3dc"),
+   tertiary: rgb("#b8cbde"),
+   neutral-lightest: rgb("#ffffff"),
+   neutral-darkest: rgb("#000000"),
+ ),
+)
+
+#show outline.entry: it => link(
+  it.element.location(),
+   text(fill: rgb("#00b3dc"), size: 1.3em)[#it.indented(it.prefix(), it.body())],
+)
+
+
+#slide[
+- #text(fill: purple)[Purple text is a question I have]
+- #text(fill: red)[Red text is something I think they did not do well]
+- #text(fill: orange)[Orange text is something I would have preferred a reference for]
+]
+
+#title-slide()
+
+#outline(depth: 1, title: text(fill: rgb("#00a6d6"))[Content])
+
+= Introduction
+
+== Variational Quantum Algorithms
+ 
+- Classical optimisation
+
+- Parametrized Quantum Circuit
+
+- Very structure dependent
+
+== Quantum Architecture Search
+
+- Automated Design
+
+- New Problems
+  - Exponential search space
+  - Ranking circuits during search
+
+- Parallels with Neural Architecture Search
+  - Differentiable QAS
+  - Reinforcement-learning QAS
+  - Predictor-based QAS
+  - Weight-sharing QAS
+
+== Training Free Proxies
+
+- No need to train parametrized quantum circuit
+  \ $->$ Faster searching
+
+- No objective functions
+  - Possibility for easier transfer
+
+- Need to prove correlation with ground-truth
+  #text(fill: red)[- not done in paper]
+
+= Method
+
+== Overview
+
+#align(horizon)[
+1. Sample circuits from search space
+
+2. Filter using Path proxy
+
+3. Rank on Expressibility
+]
+
+== Search Space
+
+Following Neural Predictor based QAS@npqas
+- Native gate set ($cal(A) = {R_x, R_y, R_z, X X, Y Y, Z Z}$)
+- Layer based sampling
+  - Layers of $n/2$ gates
+- Gate based sampling
+  - placing 1 gate at a time
+
+- Why not fully random circuits?
+  - Mitigating barren plateaus
+  - Mitigating high circuit depth
+  #text(fill:purple)[- What is the difference with gate-based?]
+
+== Path Proxy
+
+#slide(composer: (auto, auto))[
+- 'zero-cost'
+  #text(fill:orange)[- best case: $O("Operations" times "Qubits"^2)$] 
+  // I think it'd scale like this, but am uncertain since they didn't explain it anywhere
+  - below $7.8 times 10^(-4)$s
+
+1. Represent as Directed acyclic graph
+
+2. Count distinct paths from input-to-output
+
+3. Top-R highest path count circuits
+][
+  #image("tf-qas/circuit.png", height: 40%)
+  #image("tf-qas/dag.png")
+  #text(size: 0.6em)[#align(right)[from Training-Free QAS@training-free]]
+]
+
+== Expressibility Proxy
+
+#text(fill: red)[- Performance hinges on Expressibility]
+- Particularly valueable without prior knowledge
+
+#block(fill: blue.lighten(85%), inset: 12pt, radius: 6pt, stroke: 2pt + blue)[
+  *Expressibility:* \ 
+  The capability to uniformly reach the entire Hilbert space.
+]
+
+1. Calculate expressibility:
+#align(center)[$cal(E)(cal(C)) = -D_"KL" (P(cal(C),F) || P_"Haar" (F)$]
+
+2. Top expressibility circuits
+
+= Results
+
+== Evaluation
+
+- Three variational quantum eigensolver tasks
+  - Transverse field Ising model
+  - Heisenberg model
+  - $"Be"space.hair"H"_2$ molecule
+
+- Compared to
+  - Network-Predictive QAS@npqas
+  #text(fill: red)[- Hardware-efficient ansatz@hea-kandala] // but like, which one
+  - Random sampling
+
+- Implementation details
+  - TensorCircuit python package@tensorcircuit
+  #text(fill: red)[- No code included anywhere]
+
+
+= Conclusion
+
+==
+
+- Combining proxies can improve on either
+
+
+
+#slide[
+  == References <touying:unoutlined>
+  #bibliography("references.bib", title: [])
+]
+