Research Translated (RT) — Making Networks of Entangled Qubits to work together
This post is the first in a series that we are experimenting with called “Research Translated”, or RT (a pun of “retweet”) for short. Have questions, comments, concerns? Let us know in the comments or Contact Us
And without further ado…
The first paper that we are focusing on was published in Nature, titled Random access quantum information processors using multimode circuit quantum electrodynamics.
It’s a bit of a mouthful, but effectively its a paper discussing one of the limits to quantum entanglement, the fact that qubits only entangle with neighboring qubits, and (currently) cannot entangle with qubits further away.
Another way to say this is that a quantum processor cannot have random access. Random access is a computer scientist’s way of describing accessing something in a computer in a constant amount of time. So no matter how far away something is, it will always take the same amount of time to access that thing.
When you throw a baseball to someone, the time it takes for the baseball to travel from your throwing hand to the other person’s receiving glove is a function of the distance between your throwing hand and the receiving glove. The further the distance, the longer the travel time.
(Physics experts, I know that there are a million factors to consider, including wind direction and speed, gravity, spin on the ball, etc. Please bear with this simplification).
If we could somehow make throwing the baseball a “random access function”, then no matter how far away the receiving glove is from the throwing hand, the ball would take the same about of time to travel from the throwing hand to the receiving glove.
Applying the concept of random access to quantum entanglement, random access implies that two qubits would be able to interact directly with one another regardless of how far away they are from one another or how many other qubits are in the way.
This paper makes the argument that solving this problem and allowing more qubits to entangle with one another will ultimately make quantum processors a lot faster, and proposes a path forward to achieve this level of entanglement.
So there are two parts to talk about here:
- Why is this important?
- How do we do it?
Why is this important?
In this paper, Naik et al argue that solving the random access problem will allow a quantum processor to build what they call “maximally entangled states spanning several modes”. This type of quantum architecture, they argue, allows for more accurate and faster error correction.
The idea behind error correction comes from signal processing theory. Basically, there are a lot of ways to transmit information from one person to another. One could call someone on the phone, one could email, one could use a telegraph. But the actual communication channels through which these communications happen don’t always transmit information 100% accurately. When channels transmit information without losing any information, then that channel is called lossless. When some information is lost, the channel is called lossy.
So why would we ever have lossy channels? Well, sometimes there is a tradeoff between accuracy and speed. Some applications do not require 100% accurate information to be received and prefer to optimize speed.
Additionally, there are methods that electrical and systems engineers have developed over the years to mitigate the number of errors produced over a lossy channel. This is error correction.
For the purposes of this article, we will not go into examples of error correction methods, but there are many tried and tested ways.
Error correction also applies in computing. Just as there are multiple ways for two humans to send a message between one another, there are many ways that a computer can store and transmit information. Some of these methods are lossless, and some are lossy.
Error correction is particularly important in quantum computing due to the sheer volume of computations that quantum computers will be making. If a quantum computer is making a billion computations in a second, you want to ensure that the vast majority, if not all, of those computations is correct. Otherwise, one would have to go through all of those billions of computations by hand to check their accuracy, which would defeat the whole purpose of a quantum computer anyways.
Random access of qubits allows a quantum processor to optimize error correction in a few ways. Getting us into
How do we do this?
Step 1: Use a multimode quantum memory:
Ok, so qubits are logical representations of memory, right? Just as a normal bit is logically represented on paper as either a 0 or 1 but requires a physical representation to be implemented, a qubit also needs a physical representation.
One way to physically represent a qubit is to use a set of modes. For example, photons have two modes: horizontal or vertical. Using these two modes, we can represent a qubit’s quantum state in a single photon.
Multimode quantum memory is referring to using sets of modes to represent qubits.
Step 2: Create stimulated vacuum Rabi oscillations:
Vacuum Rabi oscillations happen when you have an atom and an electromagnetic resonator. A resonator is something that produces waves (also called oscillations), which can be electromagnetic (something like light waves) or mechanical (something like sound waves). Our case involves an electromagnetic resonator.
When the atom and an electromagnetic resonator are coupled and the atom get’s excited (has more energy than it normally would, above a certain threshold), the atom releases a photon into the resonator, and then later reabsorbs it.
In a “Stimulated” vacuum Rabi oscillation, the atom gets excited artificially. So the lab will cause interactions to occur between the atom and the resonator. This can be done by adding energy into the system, among other ways.
In this paper, the researchers cause vacuum Rabi oscillations to occur between a transmon (a type of quantum processor) and an individual mode that they are looking at.
The researchers start the oscillations by adding charge to the transmon and then controlling how quickly the transmon emits and reabsorbs the photon (aka, they control the frequency of the oscillation). By controlling the frequency in this way, the researchers are able to dictate what mode gets the photon that is emitted from the transmon.
This control allows the researchers to create what they call “universal quantum control”
Step 3: Universal quantum control:
The control that researchers have over the vacuum Rabi oscillations allow researchers to prepare, manipulate, and measure each mode in the quantum memory much more easily.
This section gets into the crux of why random access qubits are important. The rabi oscillations allow universal quantum control, which in turn allows for multimode entanglement (which we’ll get into later, but basically gives a huge performance boost to quantum computers and allows error correcting).
In current quantum architectures, which do not have random access to qubits but rather have qubits that can only interact with the closest qubits to them, the researchers found significant decreases in the performance of universal quantum control.
However, in simulated random access qubits, they did not experience those degradations. Hence, achieving random access is super important.
Step 4: Multimode entanglement:
Universal control allows the researchers to create entangled qubits across multiple modes. For the purposes of this article, we only have to know that that allows us to create many more quantum states then we otherwise would.
The increased number of quantum states leads to better performance and to better error correction, which is critical to the mass adoption of quantum computers.
To quote the authors, this technology is “a promising new module for quantum computation and simulation”.
