I mean, not really? Most Jax code is only marginally more restrictive than numpy, and most of the annoyances I have with Jax are shared with numpy.
And having spent too much of my life writing some extremely high performance physics code, 99% of the code complexity comes from the stupid stuff, like handling configuration and IO and restarting. Python handles that stuff great. Oh, and tests. Ever tried to write unit tests in Fortran? You'll hug python tight and promise it you'll never leave again.
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)
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.
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?
Did Rose's version not have the calamansi?
The garnishing, was that cilantro?
What choice of rice brings you back to the childhood?
Thanks! This was quite eye-opening. The tiniest details are essential!!
There are also other ethnic takes, like Kuah bamia/bamia kambing (close to the original,lamb; north Indian version would add yoghurt, South would be (fermented?)coconut milk?), nonya okra stew (Straits Chinese,adding tamarind like you mentioned, different spices)
> 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!).
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.
Yes, that book. It could be that calamansis are not common in AU at that time, that makes sense. I think I saw it referenced in your recipe. The cilantro remind one of briyani, imho, I don't know if the briyani-likeness would make a difference?
Calamansi I got with Hokkien mee was green outside and yellow/oranage-ish inside. I assumed it's ripe since it was kinda sweet too.
An aside:
I used to not use the Calamansi in Hokkien Mee because I thought we don't really do sour in our mains in my origin cuisine. After I saw a friend squeeze (over a spoon to filter out the seeds) Clamansi, I tried it too and regretted all the times I did not add it.
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!
Is there any actual QC hardware that can run these algorithms? Does it even make sense to say that you can "run" code on a quantum computer?
I don't follow this field much, but I remember there was a company called D-Wave, and people saying their product was not a "real" quantum computer. Has anything changed since?
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 looked at the intro page for Pennylane project, and it went completely over my head. I'm a ML person, can you tell me how can quantum computation help me, or why would I want to consider it? For example, would I be able to train my neural networks faster on a quantum computer? What's the point?
