18
That doesn't scale well. After a moderately long calculation you're basically left with the maximally mixed state or whatever fixed point your noise has. To scale to arbitrary long calculations you need to correct errors before they become too big.
Here's some short calculation for the intuition given above. Consider the simple white noise model (...
11
If the error rate were low enough, you could run a computation a hundred times and take the most common answer. For instance, this would work if the error rate were low enough that the expected number of errors per computation was something very small — which means that how well this strategy works would depend on how long and complicated a computation ...
11
As an addition to Nat's answer, it's worth mentioning that 'noise' is a specific concept1 in quantum computing. This answer will use Preskill's lecture notes as a basis.
In essence, noise is indeed considered to be something that could be described as 'thermal noise', although it should be noted that it is an interaction with a thermal environment causing ...
10
Unfortunately for analog computation it turns out that when realistic assumptions about the presence of noise in analog computers are made, their power disappears in all known instances; they cannot efficiently solve problems which are not solvable on a Turing machine.
"Noise" appears to be used in the general sense of non-idealities in a signal:
In ...
8
Throughout this answer, the norm of a matrix $A$, $\left\lVert A\right\rVert$ will be taken to be the spectral norm of $A$ (that is, the largest singular value of $A$). The solovay-Kitaev theorem states that approximating a gate to within an error $\epsilon$ requires $$\mathcal O\left(\log^c\frac 1\epsilon\right)$$ gates, for $c<4$ in any fixed number of ...
8
Now, adding to M. Stern's answer:
The primary reason as to why error correction is needed for quantum computers, is because qubits have a continuum of states (I'm considering qubit-based quantum computers only, at the moment, for sake of simplicity).
In quantum computers, unlike classical computers each bit doesn't exist in only two possible states. For ...
7
As far as I’m aware, the surface code is still regarded as the best. With an assumption of all elements failing with equal probability (and doing so in a certain way) it has a threshold of around 1%.
Note that the paper you linked to doesn’t have a 3D surface code. It is the decoding problem that is 3D, due to tracking changes to the 2D lattice over time. ...
7
The answer to noise (and any source of error, really) in quantum computations is quantum error correction: You choose an encoding such that discretized errors correspond not only to invalid encodings but also uniquely determine what kind of error must have occured. This is not possible for all errors but with reasonable error models (such as single qubit ...
6
There are several ways that you could realise the depolarising map $
\mathcal N_p(\rho) = (1\!-\!p)\:\!\rho + p \!\!\:\cdot\!\tfrac{1}{2}\mathbf 1$ map on a quantum computer — including an idealised quantum computer, in which waiting around for the noise to do the work for you would not be an available method.$\def\ket#1{\lvert#1\rangle}$
We start ...
6
How do we prevent quantum noise in a quantum computer?
Well, technically the answer is (at least for most systems): we use ridiculously low temperatures (much colder than space), we shield everything (or at least as much as possible) out, that might introduce any noise (radio waves, such as phone signals or light, magnetic fields, ...), we do everything to ...
6
The errors that are described by the Master equation are continuous errors. The action of error correction is to discretize those errors.
For example, dephasing noise can be described by the Master equation. The net effect is that an initial state $\rho$ is transformed into
$$
\rho\mapsto (1-p)\rho+pZ\rho Z,
$$
where $p$ is a function of time.
However, ...
5
A channel $\Phi$ is said to be degradable if there exists another channel $\Xi$ such that $\Xi\Phi$ is complementary to $\Phi$.
The idea here is as follows. Suppose $\Phi$ is a channel and $\Psi$ is complementary to $\Phi$. If $\Phi$ is applied to a state $\rho$, then the output of the channel is $\Phi(\rho)$ (of course), while $\Psi(\rho)$ represents ...
5
This can be done using the 'Aer' component of Qiskit. The properties information can be turned into a noise model using
from qiskit.providers.aer import noise
properties = device.properties()
noise_model = noise.device.basic_device_noise_model(properties)
basis_gates = noise_model.basis_gates
This can then be supplied to the execute() method, as is ...
5
There is no specific paper for this, though information on the model can be found in the Qiskit Aer API documentation and is based on the research of IBMQ quantum computing group. As examples you can read some of the following papers for more information about errors in IBMQ devices:
arXiv:1410.6419 -- The Methods section at the end has a summary of gate ...
5
I'm going to go for an intuitive answer here, as requested. Let's s go in steps:
Your input is (often?) classical, so up to that point we're good.
Then you start doing quantum operations and achieve, for example, quantum superpositions between different states. Here you're right, you cannot look to check if you're doing OK, and that indeed is a problem, or ...
4
I believe that the Centre for Engineered Quantum Systems, School of Physics, The University of Sydney and the Center for Theoretical Physics, Massachusetts Institute of Technology use of a tensor network decoder of Bravyi, Suchara and Vargo (BSV), to achieve the highest error correction threshold to date.
In their whitepaper from last December, "Ultrahigh ...
4
One simple way to do it is by defining a composite gate, like this:
class MyGateThenDepolarize(cirq.SingleQubitGate):
def _decompose_(self, qubits):
q = qubits[0]
return [MyGate.on(q), cirq.depolarize(p).on(q)]
If you want a depolarizing gate on every qubit at the end of every moment, you can do a noisy simulation:
cirq.sample(circuit, ...
4
Yes you can: When you build a noise model the noise is defined with respect to the name or label of gate objects, so you can use the labelto create multiple versions of a single gate but reference different errors in a noise model (NB: the transpiler strips away label information when it unrolls a gate not in the basis gates set).
Selective noise on ...
4
frequency (GHz): The frequency(energy) associated with the transition between the qubit's ground state ($|0\rangle$) and first excited state ($|1\rangle$).
readout error: The probability of preparing a $|0\rangle$($|1\rangle$) and measuring a $|1\rangle$($|0\rangle$), ie., of having an error in your readout
single qubit U2 error rate: The average error per ...
3
For amplitude damping, $\gamma$ is something like $e^{-\Delta t/T_1}$ where $\Delta t$ is how long the Kraus operator is supposed to act. But be very careful, Kraus evolution assumes your system has no initial correlations, that every qubit interacts with identical baths and that every qubit is identical. All the assumptions are most likely violated and so ...
2
In the dim and distant past (I.e. I don’t remember the details any more), I tried to calculate an upper bound on a fault tolerant threshold. I suspect the assumptions that I made to get there wouldn’t apply to every possible scenario, but I came up with an answer of 5.3% (non-paywall version).
The idea was roughly to make use of a well-known connection ...
2
Asking the author to clarify would give you the exact answer you are looking for. However, based upon the context provided I believe this may be related to the problem quantum noise spectroscopy attempts to solve.
Noise
According to a team of Dartmouth researchers led by Professor Lorenza Viola,
These quantum properties are essential for quantum ...
2
noise should average itself out.
Noise doesn't perfectly average itself out. That's the Gambler's Fallacy. Even though noise tends to meander back and forth, it still accumulates over time.
For example, if you generate N fair coin flips and sum them up, the expected magnitude of the difference from exactly $N/2$ heads grows like $O(\sqrt N)$. That's ...
2
Why do you need error correction? My understanding is that error correction removes errors from noise, but noise should average itself out.
If you built a house or a road and noise was a variance, a difference, with respect to straightness, to direction, it's not solely / simply: "How would it look", but "How would it be?" - a superposition of both ...
2
It is important to realize that the depolarizing and dephasing channel (and pretty much any other noise model for that matter) do not represent unitary operations. This means there is no unitary operation (that takes qubit states to qubit states) corresponding to these channels.
Rather, channels are more general: they map density operators to density ...
2
I am by no means an expert in this sort of calculation, but I think I (mostly) agree with you.
I divided the calculation up slightly differently, which simplified things notationally. Firstly, I considered the input $|0\rangle_A|0\rangle_B$. Clearly, $H$ acting on this is just 0, so this state doesn't evolve. So, the lop-left element of $E_0$ is 1, and that ...
2
I'm not sure what was the expected solution, but this also works.
First of all, note that $$ I = |\phi^+\rangle\langle\phi^+| + |\phi^-\rangle\langle\phi^-| + |\psi^+\rangle\langle\psi^+| + |\psi^-\rangle\langle\psi^-|,$$
where $|\phi^+\rangle, |\phi^-\rangle, |\psi^+\rangle, |\psi^-\rangle$ are Bell states and $I$ is 4-dimensional identity operator.
...
2
If you call execute with the parameter optimization_level set to 0, qiskit will not perform any circuit optimizations.
Your call should therefore be
job = execute(circ, simulator,
optimization_level=0,
noise_model = noise_bit_flip)
The optimization level can be 0-3 inclusive depending on how much optimization you would like ...
2
I read the article and it seems too rudimentary to provide a manual how to build a quantum computer home. It also contain some "half-truth" about quantum computers, e.g.:
Quantum computers take advantage of strange properties from quantum mechanics to filter through possible solutions much more quickly than conventional computers. When someone asks about ...
2
You can take snapshots of the statevector of the circuit when you use the 'qasm_simulator'. You simply append snapshot instructions into your circuit where you would like to see the statevector, and then can see the values in the result object that is returned. You add a snapshot instruction using
from qiskit.extensions.simulator import snapshot
qc....
Only top voted, non community-wiki answers of a minimum length are eligible
