Vedran Dunjko

Quantum digital signatures without quantum memory.

Quantum digital signatures (QDSs) allow the sending of messages from one sender to multiple recipients, with the guarantee that messages cannot be forged or tampered with. Additionally, messages cannot be repudiated--if one recipient accepts a message, she is guaranteed that others will accept the same message as well. While messaging with these types of security guarantees are routinely performed in the modern digital world, current technologies only offer security under computational assumptions. QDSs, on the other hand, offer security guaranteed by quantum mechanics. All thus far proposed variants of QDSs require long-term, high quality quantum memory, making them unfeasible in the foreseeable future. Here, we present a QDS scheme where no quantum memory is required, which also needs just linear optics. This makes QDSs feasible with current technology.

Realization of quantum digital signatures without the requirement of quantum memory

Digital signatures are widely used to provide security for electronic communications, for example, in financial transactions and electronic mail. Currently used classical digital signature schemes, however, only offer security relying on unproven computational assumptions. In contrast, quantum digital signatures offer information-theoretic security based on laws of quantum mechanics. Here, security against forging relies on the impossibility of perfectly distinguishing between nonorthogonal quantum states. A serious drawback of previous quantum digital signature schemes is that they require long-term quantum memory, making them impractical at present. We present the first realization of a scheme that does not need quantum memory and which also uses only standard linear optical components and photodetectors. In our realization, the recipients measure the distributed quantum signature states using a new type of quantum measurement, quantum state elimination. This significantly advances quantum digital signatures as a quantum technology with potential for real applications.

Quantum Speedup for Active Learning Agents

Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in reallife situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

Quantum digital signatures with quantum-key-distribution components

Digital signatures guarantee the authenticity and transferability of messages and are widely used in modern communication. The security of currently used classical digital signature schemes, however, relies on computational assumptions. In contrast, quantum digital signature (QDS) schemes offer information- theoretic security guaranteed by the laws of quantum mechanics. We present two QDS protocols which have the same experimental requirements as quantum key distribution, which is already commercially available. We also give a security proof for the presented QDS schemes against coherent forging attacks.

Composable Security of Delegated Quantum Computation

Delegating difficult computations to remote large computation facilities, with appropriate security guarantees, is a possible solution for the ever/growing needs of personal computing power. For delegated computation protocols to be usable in a larger context – or simply to securely run two protocols in parallel – the security definitions need to be composable. Here, we define composable security for delegated quantum computation. We distinguish between protocols which provide only blindness – the computation is hidden from the server – and those that are also verifiable – the client can check that it has received the correct result. We show that the composable security definition capturing both these notions can be reduced to a combination of several distinct “trace/distance/type” criteria – which are, individually, non/composable security definitions.

Phylostratigraphic profiles reveal a deep evolutionary history of the vertebrate head sensory systems

BackgroundThe vertebrate head is a highly derived trait with a heavy concentration of sophisticated sensory organs that allow complex behaviour in this lineage. The head sensory structures arise during vertebrate development from cranial placodes and the neural crest. It is generally thought that derivatives of these ectodermal embryonic tissues played a central role in the evolutionary transition at the onset of vertebrates. Despite the obvious importance of head sensory organs for vertebrate biology, their evolutionary history is still uncertain.ResultsTo give a fresh perspective on the adaptive history of the vertebrate head sensory organs, we applied genomic phylostratigraphy to large-scale in situ expression data of the developing zebrafish Danio rerio. Contrary to traditional predictions, we found that dominant adaptive signals in the analyzed sensory structures largely precede the evolutionary advent of vertebrates. The leading adaptive signals at the bilaterian-chordate transition suggested that the visual system was the first sensory structure to evolve. The olfactory, vestibuloauditory, and lateral line sensory organs displayed a strong link with the urochordate-vertebrate ancestor. The only structures that qualified as genuine vertebrate innovations were the neural crest derivatives, trigeminal ganglion and adenohypophysis. We also found evidence that the cranial placodes evolved before the neural crest despite their proposed embryological relatedness.ConclusionsTaken together, our findings reveal pre-vertebrate roots and a stepwise adaptive history of the vertebrate sensory systems. This study also underscores that large genomic and expression datasets are rich sources of macroevolutionary information that can be recovered by phylostratigraphic mining.

Experimental demonstration of kilometer-range quantum digital signatures

We present an experimental realization of a quantum digital signature protocol which, together with a standard quantum key distribution link, increases transmission distance to kilometer ranges, three orders of magnitude larger than in previous realizations. The bit rate is also significantly increased compared with previous quantum signature demonstrations. This work illustrates that quantum digital signatures can be realized with optical components similar to those used for quantum key distribution and could be implemented in existing quantum optical fiber networks.

Active learning machine learns to create new quantum experiments

Significance Quantum experiments push the envelope of our understanding of fundamental concepts in quantum physics. Modern experiments have exhaustively probed the basic notions of quantum theory. Arguably, further breakthroughs require the tackling of complex quantum phenomena and consequently require complex experiments and involved techniques. The designing of such complex experiments is difficult and often clashes with human intuition. We present an autonomous learning model which learns to design such complex experiments, without relying on previous knowledge or often flawed intuition. Our system not only learns how to design desired experiments more efficiently than the best previous approaches, but in the process also discovers nontrivial experimental techniques. Our work demonstrates that learning machines can offer dramatic advances in how experiments are generated. How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments—a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Quantum-enhanced deliberation of learning agents using trapped ions

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are ‘upgraded’ to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.

Implementing quantum control for unknown subroutines

We present setups for the practical realization of adding control to unknown subroutines, supplementing the existing quantum optical scheme for black-box control with a counterpart for the quantum control of the ordering of sequences of operations. We also provide schemes to realize either task using trapped ions. These practical circumventions of recent no-go theorems are based on existing technologies. We argue that the possibility to add control to unknown operations in practice is a common feature of many physical systems. Based on the proposed implementations we discuss the apparent contradictions between theory and practice.