This series consists of talks in the area of Foundations of Quantum Theory. Seminar and group meetings will alternate.
In this talk I will discuss recently-identified classes of quantum correlations that go beyond nonlocal classical hidden-variable models equipped with communication. First, in the bipartite scenario, I will focus on so-called instrumental causal networks, which are a primal tool in causal inference. There, I will show that it is possible to “fake” classical causal influences with quantum common causes, in a formal sense quantified by the average causal effect (ACE).
We will discuss recent trapping and cooling experiments with optically levitated nanoparticles [1]. We will report on the cooling of all translational motional degrees of freedom of a single trapped silica particle to 1mK simultaneously at vacuum of 10^{-5} mbar using a parabolic mirror. We will further report on the squeezing of a thermal motional state of the trapped particle by rapid switching of the trap frequency [2].
We discuss the role of contextuality within quantum fluctuation theorems, in the light of a recent no-go result by Perarnau et al. We show that any fluctuation theorem reproducing the two-point measurement scheme for classical states either admits a notion of work quasi-probability or fails to describe protocols exhibiting contextuality.
Conventional quantum processes are described by quantum circuits, that represent evolutions of states of systems from input to output. In this seminar we consider transformations of an input circuit to an output circuit, which then represent the transformation of quantum evolutions. At this level, all the processes complying to admissibility conditions have in principle a physical realization scheme.
Quantum gravity has many conceptual problems. Amongst the most well-known is the "Problem of Time": gravitational observables are global in time, while we would really like to obtain probabilities for processes taking us from an observable at one time to another, later one. Tackling these questions using relationalism will be the preferred strategy during this talk.
Quantum mechanics can be seen as a set of instructions how to calculate probabilities by associating mathematical objects to physical procedures, like preparation, manipulation, and measurement of a system. Quantum theory then yields probabilities which are neutral with respect to its use, e.g., in a Bayesian or a frequentistic way. We investigate a different approach to quantum theory and physical theories in general, in which we aim for subjective predictions in the Bayesian sense. This gives a structure different from the operational framework of general probabilistic theories.
Seven years ago, the first paper was published [1] on what has come to be known as the “Many Interacting Worlds” (MIW) interpretation of quantum mechanics (QM) [2,3,4]. MIW is based on a new formulation of QM [1,5,6], in which the wavefunction Ψ(t, x) is discarded entirely. Instead, the quantum state is represented as an ensemble, x(t, C), of quantum trajectories or “worlds.” Each of these worlds has well-defined real-valued particle positions and momenta, and is thereby classical-like.
The path integral formulation of quantum mechanics has been immensely influential, particularly in high energy physics. However, its applications to quantum circuits has so far been more limited. In this talk I will discuss the sum-over-paths approach to computing transition amplitudes in Clifford circuits. In such a formulation, the relative phases of different discrete-time paths through the configuration space can be defined in terms of a classical action which is provided by the discrete Wigner representation.
Device-independent self-testing allows user to identify the measured quantum states and the measurements made in a device using the observed statistics. Such verification scheme only requires assuming the validity of quantum theory and no-signalling. As such, no assumptions are required about the inner-workings of the device.
What can machine learning teach us about quantum mechanics? I will begin with a brief overview of attempts to bring together the two fields, and the insights this may yield. I will then focus on one particular framework, Projective Simulation, which describes physics-based agents that are capable of learning by themselves. These agents can serve as toy models for studying a wide variety of phenomena, as I will illustrate with examples from quantum experiments and biology.