14
It's just a coincidence.
I can speak from personal recollection on the Google side. Google originally intended to use a 72 qubit chip (Bristlecone) where qubits were essentially directly connected to each other. They then switched to an architecture where qubits were connected indirectly via a coupler. The coupler requires a control line, so this increased ...
8
They have different error rates because they are two different physical devices! This relates to the manufacturing processes of these chips. Every device is unique and will have its own fingerprint meaning its own error rate. Of course this is not something that manufactures do on purpose, but a side effect of making these qubit devices. It’s very difficult ...
7
In the classical case, there is a pretty big difference between digital computers and analogue ones. The methodology and hardware is very much distinct (in all cases I know of, at least).
The divide is still there in the quantum case, but it doesn't run quite as deep. The hardware can be similar, but requirements on how it behaves and how to manipulate it ...
6
It depends on what you mean by "able to handle". You mention a circuit depth of 99, which might be possible, but what will be the fidelity of the final state with respect to the one it's supposed to be (assuming no decoherence)?
If your fidelity requirement is close to 100%, the maximum circuit depth that the IBM machines can handle, is zero (try just ...
4
The choice of gates is entirely dependent on the types of interactions that occur in the different architectures. The cross resonance gate used by IBM generates the ZX interaction you want (plus other stuff) that leads to a CNOT. Trapped ions have XX type interactions that give rise to Molmer Sorensen gates. For single qubit gates it depends on what driving ...
4
I am going to try to give guesses that can make sense:
More qubits does not mean better machines. They may be less noise-tolerant and with less connectivity between qubits. That is why, when you benchmark them (with or without error-correction), you look first at the simplest implementations of state of art algorithms. Plus, you may change some calibrations ...
4
IBMQ.load_accounts() was deprecated and removed in Qiskit 0.14. Please use IBMQ.load_account().
3
No output shows for your code as you have a line underneath the call to plot_histogram(). This should be the last line of the section in the Jupyter notebook if you would like the image to be displayed.
I was able to run your code by removing the final line (print(counts)) and it displayed the histogram below.
3
When referring to the commercial quantum computers of both parties, it is that both are based on a different quantum principles.
The D-Wave machine works via quantum annealing and is suited for optimization problems. The machine by IBM is a gate-based quantum computer, similar to how digital computers work at the elementary level.
As the two quantum ...
3
The Q-Object not valid error you received is caused by the amount of shots you set. The max shots allowed is 8192. Since the amount of shots you set (16384) is greater than the max amount of shots allowed, you get that error.
The TranspilerError is caused by the second format for layout. When I tested your code with the second layout, I received this error ...
3
There is a link on the qiskit website to the public Slack. Here you can find channels for talking the various elements of Qiskit and also the IBM Q Experience.
3
So, to begin, I would point out that the 500 micosec T1 time is for a single qubit in isolation, while the GHZ results are on a 20 qubit device. This device has an avg T1 time of around ~75 microsec.
The GHZ results were done by Ken Wei from IBM, and will be published shortly.
In short, the circuit is a standard GHZ building circuit, with a hadamard ...
3
I was recently looking for a similar solution. Hope this helps.
job = execute(qc, backend=backend, shots=1024)
results = job.result()
print(results.time_taken)
You can also check all the values stored in result as it is a dictionary by printing it:
print(results)
Here you can check for all the information that is available within the dictionary and you ...
3
results.get_data() was replaced by results.data(). The new function returns (almost) the same information, but the runtime attribute was removed. As far as I know, there is no official way to get the runtime of your job on the computer.
There are some ways to get it through your own code, but keep in mind these are not official methods of doing so, and the ...
3
From the public data given by IBM about IBM Q16 Melbourne (14 qubits available):
Mean gate error: $2.14 \times 10^{-3}$ (probably higher for CX and lower for 1-qubit gates, but this information seems to be no longer available).
Mean measure error: $2.68\times 10^{-2}$.
If your circuit contains $300$ gates then the probability that at least one gate fails ...
2
My guess is that this is an example of co-opetition, i.e. collaborative competition. Number of qubits is just a single characteristic of a quantum processor, but there are a lot more, like tolerance, topology, etc. Also this characteristic is the only one that most people understand. Thus it's not reasonable to put all the resources on the increasing just ...
2
To simulate a 3D material, the material's structure will need to be somewhat understood. That way the structure can be mapped to the qubit connectivity. Notice in this tutorial the qubits and their connections to each other are represented in graphs. The 3D material to be simulated can be put into a graph that will then be mapped to the qubit graph and the ...
2
I would add that thermic noise, radiocative background (mainly cosmic rays) can play role in different error rate as those noise sources are different for each quantum processor.
Moreover, as a user of IBM Q, you probably know that quantum processors are sometimes under maintenance. Since each processor is maintained in different time, their runtime is ...
2
Well actually when looking at the source code, the construct_circuit method:
quantum register where the sequential QFT is performed
self._up_qreg = QuantumRegister(2 * self._n, name='up')
# quantum register where the multiplications are made
self._down_qreg = QuantumRegister(self._n, name='down')
# auxiliary quantum register used in ...
1
I'm not sure what had caused the problem but I was able to solve it and most likely know what the problem was.
Consider these two lines from my code above:
job_exp = execute(qc, backend = backend, shots = 8192)
exp_result = job_exp.result()
Problem with the above lines is that we are not waiting for the actual quantum device to compute and send over the ...
1
EDIT: I believe this is solved in @IEIrodov's answer below.
I'm not sure what's causing the issue, but based on similar issues on the qiskit slack channel, I don't think it's something you're doing.
As a workaround, try running:
exp_result = job_exp.result()
exp_measurement_result = exp_result.get_counts()
print(exp_measurement_result)
plot_histogram(...
1
The could be a problem, but it depends on how you're realising your qubits. Some realisations are configured so that $E_0=E_1$, and then there's no problem.
There is (at least from the theoretical perspective) a simple fix: if you're supposed to be waiting a time $t$, then, instead:
wait time $t/2$
apply bit flip
wait time $t/2$
apply bit flip.
This ...
1
I also came across with the same issue. However when I added my account token as follows, it worked.
token = 'Your token'
IBMQ.save_account(token)
provider = IBMQ.load_account()
device = provider.get_backend('ibmq_16_melbourne')
You can get your IBM Q token via : https://quantum-computing.ibm.com/account
Hope this helped, let me know if it didn't.
1
Both IBM and Google unveiled 53-qubit processors. At this time, only Google published performance metrics such as 1- and 2-qubit gate errors. Until IBM publishes similar metrics we simply cannot even tell whether Google's processor outperforms IBM's.
What we can tell is that the connectivity of the two processors is different - Google's Sycamore processor ...
1
I'm sure that this has something to do with quantum decoherence or "noise" which is caused when more qubits are added. It's likely that they are both at the frontlines of research so 53 qubits are the best that they can do given the hardware that they have access to. As they add more qubits it gets tougher to compute and prompts them to find some suitable ...
1
When you wrote your Qiskit program you had to specify how large your quantum register was or you put qubits in your quantum register as you wrote the program in your code. That's how many qubits you are using. You can also use the len function on the Quantum Register to check it's size. Another way is to print the quantum circuit or call the draw() function ...
1
Easiest thing talk about the algorithms for each architecture and the difference between physical and logical qubits. As far as I know we do not know yet how to perform quantum error correction efficiently on an adiabatic machine. Most computations on these devices are just repeated lots and lots of times without much error correction. For the gate model ...
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