Towards optimal experimental tests on the reality of the quantum state
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NiederspannungsnetzFeldquantGleitlagerVideotechnikBlackbox <Bordinstrument>ModellbauerKnopfComputeranimationBesprechung/Interview
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Indirekte MessungLambda-HyperonMessgerätComputeranimation
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NiederspannungsnetzBesprechung/Interview
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MessungNiederspannungsnetzPatrone <Munition>ModellbauerKompendium <Photographie>Satz <Drucktechnik>Blatt <Papier>GleichstromFeldquantProof <Graphische Technik>EinbandmaterialZwangsbedingungBesprechung/Interview
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MessungBuntheitZwangsbedingungKompendium <Photographie>MessungKlangeffektLunkerFeldquantScheinbare HelligkeitPhase <Thermodynamik>AmplitudeBesprechung/Interview
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Indirekte MessungScheinbare HelligkeitPhase <Thermodynamik>AmplitudeBesprechung/Interview
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Indirekte MessungVeränderlicher SternPatrone <Munition>Besprechung/Interview
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VollholzAnschlussbezeichnungFertigpackungZentralsternRastersondenmikroskopieBlatt <Papier>GasdichteSchubvektorsteuerungVeränderlicher SternHerbstVorlesung/Konferenz
Transkript: Englisch(automatisch erzeugt)
00:03
Usually, we think of the quantum state as representing maximal information about a physical system, but it need not be this way. Could it be that pure states are actually more like mixed states in that they represent ignorance or lack of knowledge about an underlying reality?
00:20
To answer this question, we'll adopt the ontological models formalism. In this picture, preparation of a quantum state is associated with a black box procedure. So when we press the button on our black box that says prepare psi, what actually happens is the box prepares our physical system in a physical state or ontic state labeled lambda.
00:42
Now this lambda encodes all physical properties of that system and is the only thing that a measurement device can respond to. It is only the distribution of ontic states that can be produced that is set by the quantum state and each quantum state has its own distribution. Whether the quantum state is to be considered real or not, really comes down to whether these preparation distributions overlap or not.
01:05
If the distributions are completely disjoint, that means there's no overlap. The wave function is said to be completely real because for every physical state of the world, there is at most one quantum state that's compatible with it. If the preparations overlap more, we say that the quantum state becomes more epistemic and less real.
01:24
In this case, some of the characteristic features of quantum theory, such as the indistinguishability of non-orthogonal quantum states, have an appealing explanation. Recently, many papers have argued so as to constrain the degree of overlaps that are possible in an ontological model of quantum theory.
01:43
The most convincing one is due to Barrett, Cavalcanti, Lal and Moroni. These authors derive an experimentally testable constraint that provides an upper bound on the amount of overlap that is possible. To conduct the experiment, we require the following list of ingredients. We need a reference state, c, and we also need a set of test states, psi i.
02:06
Furthermore, for each pair of test states, we're going to need a distinct quantum measurement. Each quantum measurement is used to experimentally constrain the three-way overlap between the reference state and that particular pair of test states. And using the inclusion-exclusion principle, or Bonferroni inequality,
02:24
one can derive a constraint on the two-way overlaps. The exact experimental recipe, that is, the reference state, the test states, and the measurements, is important in achieving the most effective laboratory test. If we consider doing an experiment on a Q-TRIT or three-level quantum system,
02:42
just by counting the parameters in our list of ingredients, we can see that this is a 72-dimensional optimization problem. I've represented that here as the height and color of 36 bars, each representing magnitude and phase of a complex amplitude. As you can see, randomly changing these variables is not much use, because we end up with bounds that are all above 100%, in most cases.
03:06
If we fix 70 of the 72 variables and scan across the other two, we can get an idea of just how difficult the optimization landscape is to move around in. You can see there that it's very patchy. I discovered that this is the kind of optimization problem that's best analyzed by representing the states as density matrices rather than vectors.
03:26
When the problem is recast in this form, it's more amenable to study with semi-definite programming methods. I've designed an algorithm that exploits these methods to find good experimental recipes. The algorithm uses the CVX package from MATLAB, and I've included the code with the paper so that you can download it and try it yourself.
03:44
As you can see, the experimental recipes that my algorithm finds, for example the light blue bars here, are superior to the state-of-the-art recipes that have been known up to now. Those are shown with black stars. I hope you enjoy reading the paper, and if you're a theorist or an experimentalist, I'd love to hear from you, so please drop me an email.