Python is not the problem. The compile gap is.

Описание: Most people talk about quantum as if the hard part is the qubits.
In my interview with Jonathon Riddell, CEO of Kothar Computing, the bottleneck looked different: the classical layer that has to run the science.
Because real quantum workflows are hybrid. Quantum plus classical.
And hybrid workflows live or die on orchestration, reproducibility, performance, and deployment.
Here is the uncomfortable truth.
Python is perfect for exploration. It is the front door. Python has great compiler-adjacent tools, but the workflow is still fragmented and hard to make robust end-to-end.
The pain starts when you move from notebooks to real workloads and you need predictable execution, repeatable builds, and optimized, validated runs across heterogeneous hardware.
That is the compile gap.
The jump from Python-first workflows to a reliable compilation and transpilation pipeline that targets CPUs, GPUs, and QPUs. And it shows up everywhere you care about in physics and quantum:
1. Speed
You are looping, sampling, optimizing, fitting, and dispatching to accelerators. At scale, performance depends on the lowering path: memory layout, scheduling, and how well your code maps onto the backend.
2. Reproducibility
Scientific code has to be repeatable and inspectable across machines and over time. Dynamic glue is great for discovery, but fragile when you want stable pipelines, controlled dependencies, and consistent results.
3. Correctness and safety
In physics and quantum, constraints like units, tensor shapes, and operator structure are part of the problem. Dynamic code is fast to write, but harder to fully validate end to end.
dynamic languages make certain classes of errors harder to catch early, and large scientific stacks accumulate risk through runtime shape/type/unit mismatches.As systems grow, you want more errors caught before runtime, and failures that are loud and actionable.


This is why I find Aleph so interesting.
Aleph is Kothar’s attempt to raise the ceiling for scientific and quantum computing: a language designed to feel natural for researchers, while still being built for compilation and performance.
The idea is simple but powerful.
Keep the ergonomics scientists love.
Add the compiler backbone production systems require.
Make hybrid workflows feel normal, not fragile.
If you are building or investing in quantum, I think this framing matters.
The winners will not just have better qubits.
They will have better tooling that turns quantum into a usable accelerator inside a larger scientific workflow.
Part 1 of the deep dive is out now.
Link:    • Python is not the problem. The compile gap...  

Also curious: where do you feel the pain most today, compilation, debugging, or reproducibility?

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#DeveloperTools #ProgrammingLanguages #Compilers #Compilation #Reproducibility
#PerformanceEngineering #SoftwareEngineering #ComputeAcceleration #DeepTech
#KotharComputing #physics #deeptech #BeyondTheQubit #FutureOfCompute @Kotharcomputing @JonathonRiddell

📌 Disclaimer: This post is shared on a personal basis and I do not represent any company