Toronto, Canada — 1st June 2026
Try The Benchmarking Yourself at GitHub: https://github.com/paktronsimulatorpakistan/QFT-Qiskit-Benchmarking-
The Centre of Excellence for Technology, Quantum & AI Canada/Pakistan (CETQAC) has released a reproducible benchmark indicating that PKTron v6.1.6 executes an 8-qubit Quantum Fourier Transform (QFT) statevector simulation substantially faster than Qiskit 2.4.1 with Aer under the tested conditions.
Across 50 consecutive benchmark runs, PKTron recorded an average execution time of 0.786 milliseconds, while Qiskit Aer averaged 2.599 milliseconds for the same circuit and benchmark environment. This corresponds to an observed performance ratio of approximately 3.3× in favor of PKTron.
Why the Quantum Fourier Transform Matters
The Quantum Fourier Transform is one of the foundational building blocks of quantum computing. It serves as a core component in algorithms such as:
Shor’s integer-factorization algorithm
Quantum Phase Estimation
Quantum chemistry simulations
Quantum signal processing
Various quantum machine learning techniques
Because QFT appears in many higher-level quantum algorithms, improvements in its execution efficiency can contribute to faster simulation workflows in related research domains.
Benchmark Configuration
The benchmark used an 8-qubit Quantum Fourier Transform circuit consisting of:
Testing methodology:
10 warmup executions (excluded from results)
50 timed executions
Alternating execution order between frameworks
Timing performed using Python’s time.perf_counter()
Qiskit’s one-time transpilation stage was excluded from measurements to focus solely on execution performance
Results
| Metric | PKTron v6.1.6 | Qiskit 2.4.1 + Aer |
|---|
| Mean | 0.786 ms | 2.599 ms |
| Median | 0.767 ms | 2.562 ms |
| Minimum | 0.674 ms | 2.242 ms |
| Maximum | 1.245 ms | 3.798 ms |
| Standard Deviation | 0.098 ms | 0.294 ms |
| 25th Percentile | 0.737 ms | 2.397 ms |
| 75th Percentile | 0.814 ms | 2.681 ms |
Notably, PKTron recorded faster execution times in all 50 measured runs within this benchmark environment.
Technical Factors Behind the Observed Performance
According to CETQAC researchers, several engineering choices contribute to the measured performance difference:
Native SIMD Acceleration
PKTron’s simulation backend is compiled using:
These optimizations allow the simulator to leverage AVX2 vector instructions, enabling multiple floating-point operations to be processed simultaneously.
Lightweight Execution Path
PKTron’s simulator executes through a minimal dispatch path with reduced Python-layer overhead, while Qiskit’s execution pipeline involves additional backend abstraction layers and result-handling structures.
Focused Statevector Optimization
The benchmark specifically evaluates exact statevector simulation (shots=0) on a single CPU core, a scenario where PKTron’s optimized native backend appears particularly effective.
Scope and Limitations
CETQAC emphasizes that the benchmark should be interpreted within its tested scope.
The reported results apply to:
✓ Single-core CPU execution
✓ Exact statevector simulation
✓ Small-to-medium quantum circuits
✓ Structured circuits such as the Quantum Fourier Transform
The benchmark does not establish superiority across all simulation workloads.
The results do not necessarily extend to:
✗ Large-scale simulations involving significantly more qubits
✗ GPU-accelerated workloads
✗ Deep random circuits benefiting from advanced transpilation optimizations
✗ Multi-node or distributed simulation environments
As with all benchmarking studies, performance outcomes may vary depending on hardware configuration, compiler settings, operating system, simulator architecture, and workload characteristics.
Reproducibility
CETQAC has publicly released the complete benchmark code and methodology, allowing independent researchers to reproduce the results on their own systems.
The benchmark can be executed using:
pip install pktron qiskit qiskit-aer
All reported measurements were obtained using:
About PKTron
PKTron is an open-source quantum computing framework developed by CETQAC. Version 6.1.6 includes:
Native C-extension simulation backends
AVX2 SIMD acceleration
OpenMP parallel execution support
Statevector and density-matrix simulators
Quantum chemistry modules
Quantum machine learning capabilities
Error correction and mitigation tools
Research modules for finance, optimization, and scientific computing
The project aims to expand access to advanced quantum computing research tools for scientists, educators, and developers worldwide.
About CETQAC
The Centre of Excellence for Technology, Quantum & AI Canada/Pakistan (CETQAC) is a research organization focused on quantum computing education, framework development, artificial intelligence research, and emerging technology innovation.
Benchmark Summary
Observed Result: PKTron v6.1.6 averaged 0.786 ms versus 2.599 ms for Qiskit Aer on an 8-qubit Quantum Fourier Transform statevector simulation under the disclosed test conditions, representing an observed 3.3× performance advantage within the benchmark’s stated scope.
