infinitewalk

Main reason is that the quantum computing/scientific research user base is still very Python entrenched

> Neat. XLA predates MLIR. Interesting stories there. You'd have to stop by the Bay Area LLVM monthly meetup to hear them. :-X

I would love to hear those stories! Sadly I'm based in Toronto, so dropping by the Bay Area meetups isn't in the cards anytime soon.

If any of that history ever makes it into a blog post, I'd be first in line to read it.

I wonder if this is more connected to the rise of the internet and information transfer. Even with written recipes, there was still a massive amount of variation across regions and publications (see carbonara)

thank you!

Damn. That's a much better title

> Can I ask you about the calamansi? Filipinos tend to prefer them ripening (never fully ripe), but it looks like the unripe ones are more common in malay cuisine? Your recreation seem to have used store-bought juice?

I'm pretty sure I typically see them fully green when in Singapore, but not 100% sure.

I tend to alternate between using calamansi (if I manage to find them), or instead the bottled calamansi extract (which still works pretty well!).

> Did Rose's version not have the calamansi?

By Rose's version are you referring to https://www.jewishfoodsociety.org/stories/the-flavors-of-ira... ?

If I recall, it doesn't include calamansi but instead lemon juice, I'm not sure if this is an ingredient substitution or not. I've tried making it with lemon juice, but it just misses the exact flavour profile I remember from my childhood.

> The garnishing, was that cilantro?

Yep, that is cilantro (I just happened to have some on hand).

> like bamia kambing (close to the original,lamb; Indian version would add yoghurt), nonya okra stew (Chinese,adding tamarind like you mentioned, different spices)

Thanks for mentioning these! I'm not too familiar with them, but would be keen to try them.

The interesting thing is, for a long time I didn't really think about it. I never thought about why I never saw this dish outside of my families dinner table as a kid, and later when I was an adult, I never really questioned what this oddly familiar but distant stew was in middle eastern restaurants.

It was only when I moved out of home and decided to try and recreate it I realized how unique it was!

Makes me wonder how many other similar stories exist in other peoples homes.

For those interested in compilers/LLVM/MLIR, the underlying compiler (Catalyst) may also be interesting: https://github.com/pennylaneai/catalyst

For those interested in the compiler/software stack and control hardware: https://pennylane.ai/blog/2025/12/open-source-quantum-comput...

Actually, yes! ML algorithms using PennyLane have been run on the IBM Q Experience, using both our Qiskit plugin (https://github.com/carstenblank/pennylane-qiskit) and our ProjectQ plugin (https://github.com/xanaduai/pennylane-projectq).

I can't say much more at the moment, but we should have a few more plugins released in the next few weeks that targets hardware from other QC vendors.

The D-Wave question in an interesting one, though. Unlike the QC hardware available from IBM, Rigetti, Google, etc, which uses a universal circuit model, D-Wave has focused on a particular application - quantum annealing. While our theoretical quantum gradient results only apply to the qubit model, it is an interesting question whether they can be extended to the quantum annealing framework.

I'm one of the developers on PennyLane, a cross-platform Python library for quantum machine learning (QML), automatic differentiation, and optimization of hybrid quantum-classical computations.

For a while now, QML has been getting a lot of hype --- at the Quantum2Business conference the other day, a quote that made the rounds was "QML: most overhyped and underestimated field at the same time" (attributed to Iordanis Kerenidis, I believe).

However, current research has been showing a lot of promise, especially as an application for near-term quantum devices, that doesn't require an exceptionally large number of fault tolerant qubits.

At the moment, the main approach to QML has been the so-called 'variational circuit' approach, where a parameterised quantum circuit is evaluated on quantum hardware, with optimization/machine learning then performed by an external classical ML library, such as TensorFlow/PyTorch. However, this is not the most optimal approach - the most optimal approach is to take advantage of the quantum hardware to also perform the optimization.

This was our goal with PennyLane. Before we could even start designing the library, we needed to know how to analytically evaluate gradients on quantum circuits; so we performed the research, discovered some cool analytic tricks, and published this separately [1]. This forms the backbone of PennyLane - the exact same quantum circuits used in the machine learning model are also used to calculate the gradient during backpropagation. As a result, you can construct arbitrarily complex classical-quantum models, with both the quantum and classical parts natively 'backpropagation aware'.

Even more ambitiously, we wanted an environment where you can build a hybrid classical-quantum computational model, using not only different quantum hardware devices at once, but different hardware devices from different hardware vendors. By taking advantage of all near-term quantum hardware currently available - even those using fundamentally different models, such as qubits vs. photonic modes - you can build significantly more powerful computations. Currently, we have plugins available for [ProjectQ](https://projectq.ch), [Strawberry Fields](https://github.com/XanaduAI/strawberryfields), [Qiskit](https://qiskit.org/), and more to come.

Feel free to ask any questions you might have on PennyLane, the state of QML, and quantum computation in general!

[1] Evaluating analytic gradients on quantum hardware (https://arxiv.org/abs/1811.11184)

[2] Check out the PennyLane documentation for the nitty-gritty on our analytic gradient approach to QML: https://pennylane.readthedocs.io