Jayne Thompson

How discord underlies the noise resilience of quantum illumination

The benefits of entanglement can outlast entanglement itself. In quantum illumination, entanglement is employed to better detect reflecting objects in environments so noisy that all entanglement is destroyed. Here, we show that quantum discord—a more resilient form of quantum correlations—explains the resilience of quantum illumination. We introduce a quantitative relation between the performance gain in quantum illumination and the amount of discord used to encode information about the presence or absence of a reflecting object. This highlights discords role preserving the benefits of entanglement in entanglement breaking noise.

Unified approach to contextuality, nonlocality, and temporal correlations

We highlight the existence of a joint probability distribution as the common underpinning assumption behind Bell-type, contextuality, and Leggett-Garg-type tests. We then present a procedure to translate contextual scenarios into temporal Leggett-Garg-type and spatial Bell-type ones. To demonstrate the generality of this approach we construct a family of spatial Bell-type inequalities. We show that in the Leggett-Garg scenario a necessary condition for contextuality in time is given by a violation of consistency conditions in the consistent histories approach to quantum mechanics.

Towards quantifying complexity with quantum mechanics

Abstract.While we have intuitive notions of structure and complexity, the formalization of this intuition is non-trivial. The statistical complexity is a popular candidate. It is based on the idea that the complexity of a process can be quantified by the complexity of its simplest mathematical model —the model that requires the least past information for optimal future prediction. Here we review how such models, known as

The classical-quantum divergence of complexity in modelling spin chains

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Replicating the benefits of Deutschian closed timelike curves without breaking causality

-machines can be further simplified through quantum logic, and explore the resulting consequences for understanding complexity. In particular, we propose a new measure of complexity based on quantum

Using quantum theory to simplify input–output processes

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An experimental proposal for revealing contextuality in almost all qutrit states

-machines. We apply this to a simple system undergoing constant thermalization. The resulting quantum measure of complexity aligns more closely with our intuition of how complexity should behave.

Provably unbounded memory advantage in stochastic simulation using quantum mechanics

The minimal memory required to model a given stochastic process - known as the statistical complexity - is a widely adopted quantifier of structure in complexity science. Here, we ask if quantum mechanics can fundamentally change the qualitative behaviour of this measure. We study this question in the context of the classical Ising spin chain. In this system, the statistical complexity is known to grow monotonically with temperature. We evaluate the spin chains quantum mechanical statistical complexity by explicitly constructing its provably simplest quantum model, and demonstrate that this measure exhibits drastically different behaviour: it rises to a maximum at some finite temperature then tends back towards zero for higher temperatures. This demonstrates how complexity, as captured by the amount of memory required to model a process, can exhibit radically different behaviour when quantum processing is allowed.

Power of one qumode for quantum computation

Sending messages back in time can be remarkably powerful, even if no one ever reads them, says an international research team. Peculiarities of general relativity called ‘closed timelike curves’ (CTCs) effectively allow particles to travel backwards in time, and are consistent with current quantum theory. However, CTCs break causality, the fundamental notion that cause must precede effect, and thus their existence remains highly controversial. Mile Gu at Tsinghua University in China and colleagues in Australia, Singapore, the UK and Canada investigated ‘open timelike curves’ (OTCs), which keep all time-travelling particles isolated from the past and thus respect causality. The researchers showed that despite such restrictions, OTCs allow quantum computers to clone quantum states, defy Heisenberg’s uncertainty principle, and efficiently solve previously intractable mathematical problems. This greatly improves prospects for relativistically enhanced quantum computation.

Contextuality in bosonic bunching.

All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems—algorithmic abstractions of their input–output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency—storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input–output processes depends fundamentally on what sort of information theory we use to describe them.Responding to the environment is easier with quantum mechanicsCan a quantum goldfish exhibit more complex behaviour than a classical dolphin? In complexity science, the complexity of an input-output process – a system that reacts differently when supplied with different environmental stimuli – can be quantified by the minimal memory needed to reproduce the process’s observed behaviour. This reflects the intuition that a goldfish – that remembers very little – can only exhibit fairly simple input-output behaviour. Here we show how these ideas can radically change when generalized to the quantum domain. A quantum system may exhibit behaviour that appears considerably more complex than a classical system that has significantly more memory.