Quantum systems engineering

Abstract:

The existence of multistability in quantum systems beyond the mean-field approximation remains an intensely debated open question. Quantum fluctuations are finite-size corrections to the mean-field as the full exact solution is unobtainable and they usually destroy the multistability present on the mean-field level. Here, by identifying and using exact modulated dynamical symmetries in a driven-dissipative fermionic chain we exactly prove multistability in the presence of quantum fluctuations. Further, unlike common cases in our model, rather than destroying multistability, the quantum fluctuations themselves exhibit multistability, which is absent on the mean-field level for our systems. Moreover, the studied model acquires additional thermodynamic dynamical symmetries that imply persistent periodic oscillations, constituting the first case of a boundary time crystal,to the best of our knowledge, a genuine extended many-body quantum system with the previous cases being only in emergent single- or few-body models. The model can be made into a dissipative time crystal in the limit of large dissipation (i.e. the persistent oscillations are stabilized by the dissipation) making it both a boundary and dissipative time crystal.

Abstract:

Contextuality is a nonclassical feature of quantum systems---exhibited by data that is produced empirically or theoretically---which in the realm of sheaf theory is characterised by local consistency but global inconsistency. A large part of this thesis is concerned with studying how this signature of nonclassicality is apparent also when measurements are embedded in some causal structure, and so motivating the study of causal contextuality. We begin with temporal correlations, which occur when measurements are performed sequentially on the system, and in which the definition of nonclassicality becomes sensitive to memory resources with which the classical system is equipped. For certain types of such memory, we show that there exists a map from the temporal setup to a (appropriately defined) contextuality setup, such that every nonclassical temporal empirical model satisfying no-signalling constraints consistent with the memory function corresponds to a contextual empirical model on this constructed scenario---one can view this also as a simulation of a subset of the temporal correlations by the contextuality setup. The existence of such a map allows us to apply a result from Vorob'ev in order to say, for any temporal setup and choice memory function, whether nonclassical correlations can arise. We then study causal setups by employing the notion of strategy from game semantics. We in particular show how `playing off' Nature strategies, corresponding to adaptive hidden variables, against Experimenter strategies, which may also be adaptive, realises the classical correlations of certain causal setups from the literature. We show that adaptivity on the side of the Experimenter, by reducing the sets of measurements empirical data is obtained over, can remove the inconsistencies that are imperative for the observation of contextuality. In the second part of the thesis, we study spin Hamiltonians on random graphs, focusing on exact descriptions in the thermodynamic limit. By utilising the graphon, which is the limit object of a dense random graphs sequence, we are able to derive analytical results for certain graphons and certain choice of Hamiltonian. Our overarching result is that the equilibrium physics in the thermodynamic limit is described by a set of coupled equations containing the graphon, and which describes product, \ie unentangled, states

Abstract:

Stark many-body localized (SMBL) systems have been shown both numerically and experimentally to have Bloch many-body oscillations, quantum many-body scars, and fragmentation in the large field tilt limit, but these observations have not been fundamentally understood. We explain and analytically prove all these observations by rigorously perturbatively showing the existence of novel algebraic structures that are exponentially stable in time, which we call dynamical l-bits. In particular, we show that many-body Bloch oscillations persist even at infinite temperature for exponentially long-times using a new type of dynamical algebra and provide a bound on the tilt strength for this non-ergodic transition. We numerically confirm our results by studying the prototypical Stark MBL model of a tilted XXZ spin chain. Our work explains why thermalization was observed in a recent 2D tilted experiment. As dynamical l-bits represent stable, localized, and quantum coherent excitations, our work opens new possibilities for quantum information processing in Stark MBL systems even at high temperature.

Abstract:

Identifying the types of algorithms and applications that can be solved on today's quantum computers is one of the fundamental goals of modern quantum algorithms research. Underpinning this effort is the potential leap in human technology should it be shown that quantum computers can perform useful classically-intractable calculations in the absence of quantum error correction. In this thesis we study the new paradigm of variational quantum algorithms (VQAs), designed specifically to solve optimisation problems on near-term hardware. We develop several novel algorithms spanning a range of application areas and provide estimates of the physical resources required to match classical methods. Thereafter, we attempt to run these algorithms on current quantum computers, probing their capabilities in the process. In this endeavour, we also develop our own algorithmic solution to device noise in the form of a new approach to approximate quantum circuit recompilation. We begin with studying the feasibility of simulating the time evolution of quantum systems on current quantum computers, an important problem due to its classical hardness. We build an algorithm specifically focused on the electron-phonon Hamiltonian and subsequently realise the first demonstration of obtaining its dynamics on real quantum hardware. Subsequently, we test whether a similar result is possible for time-evolution based VQAs. For this we construct a near-term quantum computing approach to dynamical mean-field theory and find that whilst not possible currently, such an algorithm could be realistically evaluated on the next generation of quantum computers in the immediate future. We then show how quantum circuits can be used as machine learning models and apply them to self-supervised learning, one of the most demanding tasks in deep learning. Through our experiments we observe a numerical advantage for the learning of visual representations using small-scale quantum neural networks over equivalently structured classical networks, a first step on the ladder towards general quantum advantage. Through this, we also highlight the potential of near-term quantum computing in problems with only empirically established classical complexity