Transformations between symmetric sets of quantum states

We investigate probabilistic transformations of quantum states from a `source' set to a `target' set of states. Such transforms have many applications. They can be used for tasks which include state-dependent cloning or quantum state discrimination, and as interfaces between systems whose information encodings are not related by a unitary transform, such as continuous-variable systems and finite-dimensional systems. In a probabilistic transform, information may be lost or leaked, and we explain the concepts of leak and redundancy. Following this, we show how the analysis of probabilistic transforms significantly simplifies for symmetric source and target sets of states. In particular, we give a simple linear program which solves the task of finding optimal transforms, and a method of characterizing the introduced leak and redundancy in information-theoretic terms. Using the developed techniques, we analyse a class of transforms which convert coherent states with information encoded in their relative phase to symmetric qubit states. Each of these sets of states on their own appears in many well studied quantum information protocols. Finally, we suggest an asymptotic realization based on quantum scissors.Comment: 10 pages; 5 figure

Ideal quantum protocols in the non-ideal physical world

The development of quantum protocols from conception to experimental realizations is one of the main sources of the stimulating exchange between fundamental and experimental research characteristic to quantum information processing. In this thesis we contribute to the development of two recent quantum protocols, Universal Blind Quantum Computation (UBQC) and Quantum Digital Signatures (QDS). UBQC allows a client to delegate a quantum computation to a more powerful quantum server while keeping the input and computation private. We analyse the resilience of the privacy of UBQC under imperfections. Then, we introduce approximate blindness quantifying any compromise to privacy, and propose a protocol which enables arbitrary levels of security despite imperfections. Subsequently, we investigate the adaptability of UBQC to alternative implementations with practical advantages. QDS allow a party to send a message to other parties which cannot be forged, modified or repudiated. We analyse the security properties of a first proof-of-principle experiment of QDS, implemented in an optical system. We estimate the security failure probabilities of our system as a function of protocol parameters, under all but the most general types of attacks. Additionally, we develop new techniques for analysing transformations between symmetric sets of states, utilized not only in the security proofs of QDS but in other applications as well

Quantum-enhanced Secure Delegated Classical Computing

We present a quantumly-enhanced protocol to achieve unconditionally secure delegated classical computation where the client and the server have both limited classical and quantum computing capacity. We prove the same task cannot be achieved using only classical protocols. This extends the work of Anders and Browne on the computational power of correlations to a security setting. Concretely, we present how a client with access to a non-universal classical gate such as a parity gate could achieve unconditionally secure delegated universal classical computation by exploiting minimal quantum gadgets. In particular, unlike the universal blind quantum computing protocols, the restriction of the task to classical computing removes the need for a full universal quantum machine on the side of the server and makes these new protocols readily implementable with the currently available quantum technology in the lab

Neural Network Operations and Susuki-Trotter evolution of Neural Network States

It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [Science, 355(6325), 2017]. States represented in this way, called Neural Network States (NNSs), were shown to display interesting properties like the ability to efficiently capture long-range quantum correlations. However, identifying an optimal neural network representation of a given state might be challenging, and so far this problem has been addressed with stochastic optimization techniques. In this work we explore a different direction. We study how the action of elementary quantum operations modifies NNSs. We parametrize a family of many body quantum operations that can be directly applied to states represented by Unrestricted Boltzmann Machines, by just adding hidden nodes and updating the network parameters. We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit. This is a powerful representation theorem (which was recently obtained with different methods) but that is not directly useful, since there is no general and efficient way to extract information from this unrestricted description of quantum states. To circumvent this problem, we propose a step-wise procedure based on the projection of Unrestricted quantum states to Restricted quantum states. In turn, two approximate methods to perform this projection are discussed. In this way, we show that it is in principle possible to approximately optimize or evolve Neural Network States without relying on stochastic methods such as Variational Monte Carlo, which are computationally expensive