Ttrenty/fix/ci test

.github/workflows/main_ci.yml

@@ -11,6 +11,9 @@ jobs:
   build-and-test:
     runs-on: ubuntu-latest
 
+    env:
+      CONDA_OVERRIDE_CUDA: "12.0" # Mock CUDA version 12.0
+
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -20,7 +23,7 @@ jobs:
         with:
           pixi-version: latest
           cache: true
-          cache-key: ${{ runner.os }}-pixi-v1-${{ hashFiles('**/pixi.lock') }}
+          cache-key: ${{ runner.os }}-pixi-${{ hashFiles('**/pixi.lock') }}
 
       - name: Check formatting
         run: pixi run lint
@@ -32,5 +35,4 @@ jobs:
         run: pixi run main
 
       - name: Run tests
-        run: pixi run test
-
+        run: pixi run test

.gitignore

@@ -6,6 +6,7 @@
 # Binary files
 build/
 build/*
+data/
 main
 *.mojopkg
 

Makefile

@@ -1,4 +1,11 @@
 # Makefile that forward every target to `pixi run`
+
+.PHONY: all
+all:
+	# @pixi run test
+	@pixi run main
+	@pixi run bench
+
 .PHONY: %
 %:
 	@pixi run $@ $(ARGS)

README.md

@@ -2,43 +2,49 @@
 
 **A Quantum Circuit Composer & Simulator in Mojo** 🔥⚛️
 
+
+## Education 
+
 This project reimplements and extends the ideas from the following tutorial paper:
 
 > **How to Write a Simulator for Quantum Circuits from Scratch: A Tutorial**  
 > *Michael J. McGuffin, Jean-Marc Robert, and Kazuki Ikeda*  
 > Published: 2025-06-09 on [arXiv:2506.08142v1](https://arxiv.org/abs/2506.08142v1) (last accessed: 2025-06-12)
 
-
-## 🎯 Project Objectives
+###  🎯 Project Objectives
 
 * **Mojo Implementation:** Re-implement the approach from the paper in Mojo for more Pythonic synthax and better readability.
 * **Learning by Doing:** Gain hands-on experience with quantum circuit simulation to better understand the capabilities and limitations of classical simulation.
 * **Performance & Safety:** Leverage Mojo's strong static typing and compilation for blazing-fast and safe operations.
 * **Hardware Acceleration:** Utilize Mojo’s universal GPU programming support to accelerate simulations.
 
+### 🔥 Current Implementation
 
-## ⚙️ Environment Setup
+The current implementation uses a State Vector approach, which is an efficient method for simulating small-scale quantum circuits (20–30 qubits) with high precision. This approach also enables relatively straightforward exact gradient computations.
 
-Follow these steps to set up your environment and build the binary:
+An alternative implementation for the futur could be using the Tensor Network approach. This method is more suitable for larger circuits but offers lower precision and would involves more computationally expensive gradient calculations.
 
+## Usage
+
+### ⚙️ Environment Setup
+
+Follow these steps to set up your environment, build the library and run some examples:
+
+If you don't have Pixi installed yet:
 ```bash
-# If you don't have Pixi installed yet:
 curl -sSf https://pixi.sh/install.sh | bash
-
-# Install all project dependencies:
+```
+Install all project dependencies:
+```
 pixi install
+```
 
-# Build and run examples of the simulator:
+Build and run examples of the simulator:
+```bash
 pixi run main
 ```
 
 
-## 🔥 Current Implementation
-
-The current implementation uses a State Vector approach, which is an efficient method for simulating small-scale quantum circuits (20–30 qubits) with high precision. This approach also enables relatively straightforward exact gradient computations.
-
-An alternative implementation for the futur could be using the Tensor Network approach. This method is more suitable for larger circuits but offers lower precision and would involves more computationally expensive gradient calculations.
-
 ## 📄 License
 
 This project is open-source and licensed under Apache License 2.0.

TODOs.md

@@ -4,24 +4,24 @@
 
 ### Implementations
 
-- 4 / 3 : Implement measurement gates
-
-- 4 / 5 : Implement the computation of statistics (6.5 and 6.6)
-
-- 3 / 3 : Implement naive implementation of the functions to compare performances
-    - matrix multiplication (but starting from right or smart)
-    - partial trace
-
-- 5 / 5 : Start adding support for GPU in the base classes if needed (not possible to use SIMD(complexfloat64) anymore, or keep them but seperate them when moving data to GPU)
-    - struct PureBasisState
+- 5 / 5 : Start adding support for GPU in the base classes if needed (not possible to use SIMD(ComplexFloat32) anymore, or keep them but seperate them when moving data to GPU)
+    - struct StateVector
     - struct ComplexMatrix
     - struct Gate
 
 - 5 / ? : GPU implementation of:
     - qubit_wise_multiply()
     - apply_swap()
     - partial_trace()
+    - StateVector.to_density_matrix()
+
+- 4 / 3 : Export benchmark results as plots.
+
+- 2 / 4 : Efficient support for tracking a state statistic like entropy during the execution of the circuit by the simulator.
 
+- 3 / 3 : Implement naive implementation of the functions to compare performances
+    - matrix multiplication (but starting from right or smart)
+    - partial trace
 
 ### Tests
 
@@ -30,8 +30,7 @@
 - 5 / 2 : Test for everything that will be implement in GPU
     - qubit_wise_multiply()
     - apply_swap()
-    - partial_trace()
-    - struct PureBasisState's methods
+    - struct StateVector's methods
     - struct ComplexMatrix's methods
     - struct Gate's Gate
 
@@ -41,14 +40,23 @@
 
 ## Droped for now
 
+- 4 / 3 : Implement end of circuit measurement gates with some of those options:
+    - https://docs.pennylane.ai/en/stable/introduction/measurements.html
+
 - 4 / 4 : Gradient computation with Finite Difference
 
 - 3 / 2 :  Use a separate list for things that are not real gate to not slow down the main run logic
 
+- 3 / 3 : Setup automatic Doc generation with pixi but also on github.io repository page
+
 - 3 / 4 : Compile time circuit creation?
 
 - 3 / 4 : Gradient computation with Parameter-Shift
 
+- 3 / 4 : Implement mid circuit measurement gates (Section 7 of paper)
+
 - 3 / 100 : Gradient computation with Adjoint Method
 
 - 2 / 4 : qubit_wise_multiply_extended() but for gates applied to non-adjacent qubits
+
+- 2 / 3 : Implement concurence (2-qubits entanglement metric) computePairwiseQubitConcurrences()

benchmarks/all_benchmarks.mojo

@@ -0,0 +1,45 @@
+from sys import has_accelerator
+
+from bench_simulate_random_circuit import bench_simulate_random_circuit
+from bench_qubit_wise_multiply import (
+    bench_qubit_wise_multiply,
+    bench_qubit_wise_multiply_inplace,
+    bench_qubit_wise_multiply_extended,
+)
+from bench_qubit_wise_multiply_gpu import (
+    bench_qubit_wise_multiply_inplace_gpu,
+)
+
+
+def main():
+    print("Running all benchmarks...")
+    # bench_qubit_wise_multiply()
+    bench_qubit_wise_multiply_inplace[
+        min_number_qubits=5,
+        max_number_qubits=25,
+        number_qubits_step_size=2,
+        min_number_layers=5,
+        max_number_layers=4000,
+        number_layers_step_size=400,
+        fixed_number_qubits=11,
+        fixed_number_layers=20,
+    ]()
+
+    @parameter
+    if not has_accelerator():
+        print("No compatible GPU found")
+    else:
+        bench_qubit_wise_multiply_inplace[
+            min_number_qubits=5,
+            max_number_qubits=25,
+            number_qubits_step_size=2,
+            min_number_layers=5,
+            max_number_layers=4000,
+            number_layers_step_size=400,
+            fixed_number_qubits=11,
+            fixed_number_layers=20,
+        ]()
+
+    # bench_qubit_wise_multiply_extended()
+    # bench_simulate_random_circuit()
+    print("All benchmarks completed.")