progress

planning/plan.typ

@@ -0,0 +1,37 @@
+== Introduction
+What I'll be making is a program that uses a Hardware discriptor 
+-like the one exported by qiskit- together with a target expressibility, 
+target entanglement, and a minimal Fidelity to produce a 
+diverse set of Parametrised Quantum Circuits. 
+The program will be using one of the Quality-Diversity Evolutionary Algorithms@qdea to achieve this,
+the quality aspect will use the Expressibility, Entanglement, and simulated Noise -or proxies for these-
+to determine the Quality, while some measure of distance between different expressible Hilbert Spaces
+will be developed to help the algorithm with the Diversity aspect.
+We opt for a modular but integrated approach where it's simple to modify, add, or remove proxies
+from the Evolutionary Algorithm, this way the system can be built upon for future research.
+In the next parts I'll list the inputs and outputs in a more structured way.
+
+= Inputs
+- Hardware description (like in Qiskit for example)
+  - Topology: Connection graph between qubits
+  - Valid gates: Valid hardware gates on each qubit/connection
+  - Fidelities of gates: Gate fidelity on a per-qubit/connection basis
+  - Decoherence times: Decoherence times of each qubit
+- Target Expressibility: Expressibility that should be searched towards, both lower and higher than the target will be penalised by the cost function
+- Target Entanglement: Entanglement that should be searched towards, also penalises both ways
+- Minimal Fidelity: Circuits with a lower output fidelity than this will get penalised
+
+= Outputs
+
+- The set of optimised PQCs that cover a diverse set of areas on the Hilbert space
+  - each circuit will balance diversity and performance (based on input targets)
+  - each circuit will have a one-to-one trivial mapping to the hardware due to hardware descriptor input
+- The final values of each used proxy per circuit
+  - Expressibility
+  - Entanglement
+  - Fidelity
+
+
+== References
+
+#bibliography("references.bib")

presentations/progress.typ

@@ -64,7 +64,7 @@ High level protocol:
 1. Generate a random population
 2. Evaluate the fitness
 3. Select the better individuals
-4. Produce offsprint
+4. Produce offspring
 5. Repeat until goal reached at 2
 
 Generally Genetic Algorithms but alternatives exist
@@ -106,9 +106,7 @@ However:
 
 == What I will be doing
 
-1. Reproduce parts of the paper@genetic-expressibility mentioned before
+1. Implement Quality-Diversity evolutionary Algorithm that does sampling of the gate space
-   to have a baseline and something to benchmark against.
-   As it is closer to what we discussed as TF-QAS@training-free is.
 2. Hardware constraints
     - Qubit connectivity
     - Per-qubit gate types (for NV-centers etc.)

presentations/tf-qas.typ

@@ -16,7 +16,7 @@
 
 #show: university-theme.with(
   config-info(
-    title: "Implementation Specific QAS", // Required
+    title: "Training-Free QAS", // Required
     date: datetime.today().display(),
     authors: ("Noa Aarts"),
   

report/references.bib

@@ -458,17 +458,78 @@
 }
 
 @article{Zhang_2022,
-doi = {10.1088/2058-9565/ac87cd},
+    doi = {10.1088/2058-9565/ac87cd},
-url = {https://doi.org/10.1088/2058-9565/ac87cd},
+    url = {https://doi.org/10.1088/2058-9565/ac87cd},
-year = {2022},
+    year = {2022},
-month = {aug},
+    month = {aug},
-publisher = {IOP Publishing},
+    publisher = {IOP Publishing},
-volume = {7},
+    volume = {7},
-number = {4},
+    number = {4},
-pages = {045023},
+    pages = {045023},
-author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao, Hong},
+    author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao,
-title = {Differentiable quantum architecture search},
+              Hong},
-journal = {Quantum Science and Technology},
+    title = {Differentiable quantum architecture search},
-abstract = {Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS. For instance, unitary operations are decomposed into quantum gates, noisy circuits are re-designed to improve accuracy, and circuit layouts for quantum approximation optimization algorithm are automatically discovered and upgraded for combinatorial optimization problems. These results not only manifest the vast potential of DQAS being an essential tool for the NISQ application developments, but also present an interesting research topic from the theoretical perspective as it draws inspirations from the newly emerging interdisciplinary paradigms of differentiable programming, probabilistic programming, and quantum programming.}
+    journal = {Quantum Science and Technology},
+    abstract = {Quantum architecture search (QAS) is the process of automating
+                architecture engineering of quantum circuits. It has been desired
+                to construct a powerful and general QAS platform which can
+                significantly accelerate current efforts to identify quantum
+                advantages of error-prone and depth-limited quantum circuits in
+                the NISQ era. Hereby, we propose a general framework of
+                differentiable quantum architecture search (DQAS), which enables
+                automated designs of quantum circuits in an end-to-end
+                differentiable fashion. We present several examples of circuit
+                design problems to demonstrate the power of DQAS. For instance,
+                unitary operations are decomposed into quantum gates, noisy
+                circuits are re-designed to improve accuracy, and circuit layouts
+                for quantum approximation optimization algorithm are
+                automatically discovered and upgraded for combinatorial
+                optimization problems. These results not only manifest the vast
+                potential of DQAS being an essential tool for the NISQ
+                application developments, but also present an interesting
+                research topic from the theoretical perspective as it draws
+                inspirations from the newly emerging interdisciplinary paradigms
+                of differentiable programming, probabilistic programming, and
+                quantum programming.},
 }
 
+@article{qdea,
+    title = {A survey on Quality-Diversity optimization: Approaches,
+             applications, and challenges},
+    journal = {Swarm and Evolutionary Computation},
+    volume = {100},
+    pages = {102240},
+    year = {2026},
+    issn = {2210-6502},
+    doi = {https://doi.org/10.1016/j.swevo.2025.102240},
+    url = {https://www.sciencedirect.com/science/article/pii/S2210650225003979},
+    author = {Haoxiang Qin and Yi Xiang and Hainan Zhang and Yuyan Han and
+              Yuting Wang and Xinrui Tao and Yiping Liu},
+    keywords = {Quality-Diversity optimization, Evolutionary computation,
+                Feature space, MAP-Elites},
+    abstract = {Quality-Diversity (QD) optimization is a paradigm of
+                evolutionary computation (EC) that extends the classic approaches
+                , aiming to generate a collection of solutions that are both
+                diverse and high-performing. Unlike traditional evolutionary
+                algorithms (EAs), QD methods emphasize the illumination (or
+                coverage) of a user-defined feature space, while simultaneously
+                aiming for local optimization within each discovered region of
+                the feature space. Over the past decade, QD has rapidly developed
+                and proven effective in areas such as evolutionary robotics and
+                video games. However, a systematic review of this growing field
+                remains lacking. To date, the most recent review article on QD
+                was published in 2021. Therefore, to offer a more comprehensive
+                overview of the latest QD research, this paper provides a
+                thorough survey of QD optimization, covering its foundational
+                principles and representative algorithmic frameworks such as
+                Novelty Search with Local Competition (NSLC), MAP-Elites, the
+                unified modular QD framework, and RIBS. In addition, we divide
+                the algorithm improvement part into three modules for discussion:
+                containers, selection, and mutation. Then, the evaluation metrics
+                widely used in QD optimization are listed for researchers. We
+                further explore its diverse applications across domains such as
+                evolutionary robotics, video games, scheduling, software testing,
+                and engineering design. Finally, we discuss the current
+                challenges in the field and outline promising directions for
+                future research.},
+}

report/sections/2theory.typ

@@ -2,3 +2,5 @@
 #import "@preview/physica:0.9.6": *
 #import "@preview/unify:0.7.1": num, numrange, qty, qtyrange
 #import "@preview/zero:0.5.0"
+
+