When maintaining a quantum memory, you measure parity checks of the quantum error correcting code. These parity checks don't contain any information about the logical state, just (partial) information about the error, so the logical quantum information remains coherent through the process (i.e. the logical part of the state is not collapsed).
These measurements are classical data, and a computation is required in order to infer the most likely error that led to the measured syndrome. This process is known as decoding.
This work is a model that acts as a decoding algorithm for a very common quantum code -- the surface code. The surface code is somewhat like the quantum analog of a repetition code in a sense.
I often wondered why MIMO was such an investigated topic. It would make sense if the Shannon limit is higher for this channel. Is there a foundational paper or review that shows this?
I believe that a classical radio receiver is measuring a coherent state. This is a much lower level notion than people normally think about in QEC since the physical DoF are usually already fixed (and assumed to be a qubit!) in QEC. The closest analogue might be different choices of qubit encodings in a bosonic code.
In general, I'm not sure that the classical information theory toolkit allows us to compare a coherent state with some average occupation number N to say, M (not necessarily coherent) states with average occupation number N' such that N' * M = N. For example, you could use a state that is definitely not "classical" / a coherent state or you could use photon number resolving measurements.
A tangential remark: The classical information theory field uses this notion of "energy per bit" to be able to compare more universally between information transmission schemes. So they would ask something like "How many bits can I transmit with X bandwidth and Y transmission power?"
A few things come to mind from more modern aircraft like the 787/A350:
Fly by wire (flight envelope protection), electronically actuated control surfaces (less hydraulics / reduced complexity), bleedless engines (greater efficiency), greater use of composites (less weight), modern wing design using computational modeling/optimization (possibly more efficient), and essentially as large of a turbofan fan as you would like
I want to point out that the experiment was at Harvard in the Lukin group. There is a proposal for constant-rate encodings using large quantum low-density parity check codes via atom rearrangement which could in principle achieve such high encoding rate. That said, it's certainly not mainstream yet. https://arxiv.org/abs/2308.08648
This is pretty neat! I think it's worth doing more analysis on the entropy cells separately from the entropy extractor. For example, the von Neumann extractor requires exchangeability of the input bits. Does this hold? In the setting where there is no phase noise from the entropy cells (and hence no entropy), does the TRNG give any output? Assuming some basic model (maybe measure this?) about gate delays, how much entropy should be generated by each entropy cell?
Is there some good intuition why P-complete problems are difficult to parallelize? This is the first I've heard of it (but then again, I'm usually interested in more obscure complexity classes)
This is not quite true. You only need to keep the qubits at a fixed temperature as you scale the system, so the resources required to add additional qubits grow only polynomially with the system size. Once you have many qubits with a sufficiently low (but constant) error rate, you can do quantum error correction which also only has polynomial overhead.
What experimental evidence of quantum gravity is there? I like to argue for theoretical evidence of quantum gravity, but afaik there hasn’t been any experiments to rule out classical GR coupled to a quantum matter sector
It is worth noting that all known classical algorithms to solve this problem scale exponentially with the problem size, and fundamentally there is no reason why putting 60 or 70 qubits in the fridge is particularly more difficult than 53. The point is that while classical algorithm advances can take off orders of magnitude off the runtime, this has to be done _every_ time there is a small increase in the size of the quantum computing device in order to kill a supremacy claim.
(To clarify, I personally think optimization is not a remotely near-term application)
I once ran across an application that had a heap footprint for which the size was fixed at runtime. The data locality loss from the indirection killed the performance. In principle, JIT could fairly transparently unbox this and have no heap footprint
More or less, we're just before the finish line. About 20% lower noise and we'll be there. For every little bit past that, the overhead gets reduced immensely
Quantum error correction is a key ingredient for future realizations of fault-tolerant quantum computers.
Physics is local, so errors in far separated regions of the device are only slightly correlated. In this setting, threshold theorems can be proved to show that as long as the device is below some constant amount of noise at the physical level, the noise can be suppressed arbitrarily (exponentially) at the logical level.
These measurements are classical data, and a computation is required in order to infer the most likely error that led to the measured syndrome. This process is known as decoding.
This work is a model that acts as a decoding algorithm for a very common quantum code -- the surface code. The surface code is somewhat like the quantum analog of a repetition code in a sense.