quantum | Quantum computer simulation in python

Since you are already using SAAJ for your call, why not use the same API to read the response?

Because Qiskit has multiple drawers. Those are:

• text
• mpl
• latex
• latex_source.

The drawer you see in the IBM Quantum Lab is the one based on Matplotlib. You can get the same output by qc.draw('mpl').

To set a default, you can change (or create if does not exist) the file ~/.qiskit/settings.conf) with the entry circuit_drawer = mpl.

The problem is resolved when blit=False, though it may slow down your animation.

Just quoting from a previous answer:

"Possible solutions are:

Put the title inside the axes.

Don't use blitting"

See: How to update plot title with matplotlib using animation?

You also need ffmpeg installed. There are other answers on stackoverflow that help you through that installation. But for this script, here are my recommended new lines you need to add, assuming you're using Windows:

Computing transpositions efficiently is hard. This primitive is not compute-bound but memory-bound. This is especially true for big matrices stored in the RAM (and not CPU caches).

Numpy use a view-based approach which is great when only a slice of the array is needed and quite slow the computation is done eagerly (eg. when a copy is performed). The way Numpy is implemented results in a lot of cache misses strongly decreasing performance when a copy is performed in this case.

I found this function virtually always run in single-threaded, whatever how large the transposed matrix/array is.

This is clearly dependant of the Numpy implementation used. AFAIK, some optimized packages like the one of Intel are more efficient and take more often advantage of multithreading.

I think a parallelized transpose function should be a resolution. The problem is how to implement it.

Yes and no. It may not be necessary faster to use more threads. At least not much more, and not on all platforms. The algorithm used is far more important than using multiple threads.

On modern desktop x86-64 processors, each core can be bounded by its cache hierarchy. But this limit is quite high. Thus, two cores are often enough to nearly saturate the RAM throughput. For example, on my (4-core) machine, a sequential copy can reach 20.4 GiB/s (Numpy succeed to reach this limit), while my (practical) memory throughput is close to 35 GiB/s. Copying A takes 72 ms while the naive Numpy transposition A takes 700 ms. Even using all my cores, a parallel implementation would not be faster than 175 ms while the optimal theoretical time is 42 ms. Actually, a naive parallel implementation would be much slower than 175 ms because of caches-misses and the saturation of my L3 cache.

Naive transposition implementations do not write/read data contiguously. The memory access pattern is strided and most cache-lines are wasted. Because of this, data are read/written multiple time from memory on huge matrices (typically 8 times on current x86-64 platforms using double-precision). Tile-based transposition algorithm is an efficient way to prevent this issue. It also strongly reduces cache misses. Ideally, hierarchical tiles should be used or the cache-oblivious Z-tiling pattern although this is hard to implement.

Here is a Numba-based implementation based on the previous informations:

The resource /sample/klantPagina.fxml might not have been found and give a NullPointerException on load.

@Joab.Ai, thank you for posting this issue! We've identified this to be specific to the latest version of qsharp (0.16.2104.138035). While we are looking into a fix, a workaround will be to downgrade your qsharp package version:

Edit: we have fixed this issue in our latest release! Update to the latest version with this command:

If you are familiar with the physics of the Ising model (e.g. just look it up on wikipedia), you will find out that the term "linear bias" h is used instead of the physics term external constant magnetic field and the term "quadratic bias" J is used instead of the physics term of interaction between a pair of (neighbouring in the case of the Ising model) spins. My guess is that the h and J coefficients must be learned from some given data. Your job is to cast (interpret) the data available to you into an Ising model configuration (state) and then use some sort of optimization with unknown h and J that minimizes the difference between the model's solutions (theoretical Ising model configuration) and the observed data.

@Column(name = "observaciones,", length = 100, nullable = true, unique = false) you have a comma at the end of the name that is wreaking havoc with the SQL. Remove the comma! Same for fecha,.

call Platform.exit() instead of System.exit();