Quantum Physics

It is commonplace that quantum theory can be viewed as a ``non-classical" probability calculus. This observation has inspired the study of more general non-classical probabilistic theories modeled on QM, the so-called generalized probabilistic theories or GPTs. However, the boundary between these putatively non-classical probabilistic theories and classical probability theory is somewhat blurry, and perhaps even conventional. This is because, as is well known, any probabilistic model can be understood in classical terms if we are willing to embrace some form of contextuality. In this talk, I want to stress that this can often be done functorially: given a category $\Cat$ of probabilistic models, there are functors $F : \Cat \rightarrow \Set_{\Delta}$ where $\Set_{\Delta}$ is the category of sets and stochastic maps. In addition to the familiar Beltrametti-Bugajski representation, I'll exhibit two others that are less well known, one involving the ``semi-classical cover" and another, slightly more special, that allows one to represent a probabilistic model with sufficiently strong symmetry properties by a model having a completely classical probabilistic structure, in which any ``non-classicality" is moved into the dynamics, in roughly the spirit of Bohmian mechanics. 

(Based on http://philsci-archive.pitt.edu/16721/)

Zoom Link: https://pitp.zoom.us/j/91838172434?pwd=SUltOGlURWI5MDN6Qk45dnVRelBOQT09

We study the problem of learning a Hamiltonian given copies of its Gibbs state at a known inverse temperature. Anshu et al. recently studied the sample complexity (number of copies of the Gibbs state needed) of this problem for geometrically local Hamiltonians. In the high-temperature regime, their algorithm has sample complexity polynomial in the system size, temperature, and accuracy. Their algorithm can also be implemented with polynomial, but suboptimal, time complexity. Here, we study the same question for a more general class of Hamiltonians and present an algorithm that solves this problem with improved sample complexity and time complexity linear in the sample size. Furthermore, we prove a matching lower bound showing that our algorithm's sample complexity is optimal, and hence our time complexity is also optimal. Joint work with Robin Kothari and Ewin Tang.

The standard operational probabilistic framework (within which Quantum Theory can be formulated) is time asymmetric.  This is clear because the conditions on allowed operations include a time asymmetric causality condition.  This causality condition enforces that future choices do not influence past events.  To formulate operational theories in a time symmetric way I modify the basic notion of an operation allowing classical incomes as well as classical outcomes.  I provide a new time symmetric causality condition which I call double causality.  I apply these ideas to Quantum Theory proving, along the way, a time symmetric version of the Stinespring extension theorem using double causality.  I also propose the idea of a conditional frame of reference.  We can transform from the time symmetric frame of reference to a forward or a backward frame of reference.  This talk is based on arXiv:2104.00071.

We give a complete characterization of the (non)classicality of all stabilizer subtheories. First, we prove that there is a unique nonnegative and diagram-preserving quasiprobability representation of the stabilizer subtheory in all odd dimensions, namely Gross’s discrete Wigner function. This representation is equivalent to Spekkens’ epistemically restricted toy theory, which is consequently singled out as the unique noncontextual ontological model for the stabilizer subtheory. Strikingly, the principle of noncontextuality is powerful enough (at least in this setting) to single out one particular classical realist interpretation. Our result explains the practical utility of Gross’s representation, e.g. why (in the setting of the stabilizer subtheory) negativity in this particular representation implies generalized contextuality, and hence sheds light on why negativity of this particular representation is a necessary resource for universal quantum computation in the state injection model. This last fact, together with our result, implies that generalized contextuality is also a necessary resource for universal quantum computation in this model. In all even dimensions, we prove that there does not exist any nonnegative and diagram-preserving quasiprobability representation of the stabilizer subtheory, and, hence, that the stabilizer subtheory is contextual in all even dimensions.

Ontic structural realism is a form of scientific realism based on quantum mechanics in two ways:
(i) particles are not taken to be individual entities because they are not distinguishable; and, (ii) entanglement is taken to be relational structure that does not reduce to the state of parts and their causal interactions.

Furthermore, the idea of modal structure in OSR is exemplified by the way quantum mechanics sits between Bell-inequality violation and no-superluminal signalling. However, what should the advocate of OSR say about the measurement problem and decoherence? Wallace and Saunders combines OSR with Everettianism and argue that they need each other. Are Ladyman and Ross justified in their reluctance to agree with him?

With the race for quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit. While such "supervised quantum models" are sometimes called "quantum neural networks", their mathematical structure reveals that they are in fact kernel methods with kernels that measure the distance between data embedded into quantum states. This talk gives an informal overview of the link, and discusses the far-reaching consequences for quantum machine learning.

In recent years, random quantum circuits have played a central role in the theory of quantum computation. Much of this prominence is due to recent random quantum circuit sampling experiments which have constituted the first claims of "quantum supremacy".   While random quantum circuits enjoy certain advantages that make them ideal for implementation by near-term quantum experiments, it is unclear a priori why they should be difficult to simulate classically. While we know several examples of quantum algorithms which attain exponential speedups over classical computation, they all seem to rely on highly structured circuits (such as quantum Fourier transforms) which are far from typical. Why then should we expect a generic quantum circuit to realize a large computational advantage?

In this talk we will explain the complexity theoretic basis for the classical hardness of random circuit sampling.

This talk will be based on joint work with Adam Bouland, Chinmay Nirkhe, and Umesh Vazirani (https://arxiv.org/abs/1803.04402), as well as Adam Bouland, Yunchao Liu and Zeph Landau (https://arxiv.org/abs/2102.01738).

Zoom Link: https://pitp.zoom.us/j/92408058752?pwd=b1JoeTVISlpjaXk3ZjBoSi9pYjNUZz09