Alexey Melnikov

Terra Quantum AG

Coherent controlization, i.e., coherent conditioning of arbitrary single- or multi-qubit operations on the state of one or more control qubits, is an important ingredient for the flexible implementation of many algorithms in quantum computation. This is of particular significance when certain subroutines are changing over time or when they are freq...

Quantum walks have been employed widely to develop new tools for quantum information processing recently. A natural quantum walk dynamics of interacting particles can be used to implement efficiently the universal quantum computation. In this work quantum walks of electrons on a graph are studied. The graph is composed of semiconductor quantum dots...

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. 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 cha...

Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enablin...

Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speed-up in deliberation time. PS agents have been...

Can future robots and artificial-intelligence (AI) systems have consciousness and genuinely human intelligence -- or even superhuman intelligence? Is it possible for them to behave ethically? Here we look at these questions from the point of view of philosophy and AI, and argue that these questions are related: their answer hinges on the fulfillmen...

We scrutinize publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causatio...

Modern integrated photonic quantum technologies are based on optical waveguides. The propagation of light in optical waveguides allows one to implement quantum computation and bosonic quantum simulation. Nevertheless, to further develop photonic quantum devices, one needs a precise mathematical description of quantum dynamics in waveguides. In this...

Оптические волноводы являются основой современных интегрированных фотонных квантовых технологий. Процессы распространения света в оптических волноводах позволяют реализовывать квантовые вычисления и бозонные квантовые симуляции. Тем не менее для дальнейшего усовершенствования фотонных квантовых приборов необходимо точное математическое описание ква...

The quantum walk process represents a basic subroutine in many quantum algorithms and plays an important role in studying physical phenomena. Quantum particles, photons and electrons, are naturally suited for simulating quantum walks in systems of photonic waveguides and quantum dots. With an increasing improvement in qubits fidelity and qubits num...

Finding optical setups producing measurement results with a targeted probability distribution is hard, as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabl...

Machine learning can help us in solving problems in the context of big-data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum...

We study hitting times of quantum walks on graphs from a machine learning perspective. Given a graph it is difficult to decide if quantum walks give an advantage relative to classical random walks. It was shown that machine learning can help to detect quantum advantage even though quantum walk and random walk dynamics on the graph has never been si...

Finding optical setups producing measurement results with a targeted probability distribution is hard as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices. To tackle this complexity, we introduce a method combining reinforcement learning and simulated annealing enabli...

Quantum walks is a tool for studying various phenomena in quantum systems, including quantum transport in complex networks. In article number 1900115, Alexey A. Melnikov and co‐workers suggest how to improve our understanding of noisy quantum walks in networks with computer vision. The cover gives a pictorial view of a unique eye that learns to obs...

Quantum effects are known to provide an advantage in particle transfer across networks. In order to achieve this advantage, requirements on both a graph type and a quantum system coherence must be found. Here, it is shown that the process of finding these requirements can be automated by learning from simulated examples. The automation is done by u...

Quantum effects are known to provide an advantage in particle transfer across networks. In order to achieve this advantage, requirements on both a graph type and a quantum system coherence must be found. Here we show that the process of finding these requirements can be automated by learning from simulated examples. The automation is done by using...

Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum pr...

Quantum particles are known to be faster than classical when they propagate stochastically on certain graphs. A time needed for a particle to reach a target node on a distance, the hitting time, can be exponentially less for quantum walks than for classical random walks. It is however not known how fast would interacting quantum particles propagate...

Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enablin...

Quantum particles are known to be faster than classical when they propagate stochastically on certain graphs. A time needed for a particle to reach a target node on a distance, the hitting time, can be exponentially less for quantum walks than for classical random walks. It is however not known how fast would interacting quantum particles propagate...

Quantum walks are fundamentally different from random walks due to the quantum superposition property of quantum objects. Quantum walk process was found to be very useful for quantum information and quantum computation applications. In this paper we demonstrate how to use quantum walks as a tool to generate high-dimensional two-particle fermionic e...

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous ma...

We study a continuous-time quantum walk of interacting fermions on a cycle graph. By finding analytical solutions and simulating the dynamics of two fermions we observe a diverse structure of entangled states of indistinguishable fermions. The relation between entanglement of distinguishable qutrits and indistinguishable electrons is observed. Rest...

Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice, different task environments are best handled by different learning models, rather than a single universal approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often do...

We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more compli...

We study the entanglement structure dynamics of multipartite system experiencing a dissipative evolution. We characterize processes leading to a particular form of output system entanglement and provide a recipe for their identification via concatenations of peculiar linear maps with entanglement-breaking operations. We illustrate the applicability...

In this paper we study quantum walks of electrons on a graph. This graph is composed of Si quantum dots arranged in a circle. Electrons can tunnel between neighbouring dots and interact via Coulomb interaction. We show that this mutual repulsion leads to entanglement. Fermionic entanglement dynamics is evaluated by several measures. Current detecto...

The interaction of the quantum register with a noisy environment that leads to phase and bit errors is considered. Modeling of 5-qubit and 9-qubit error-correction algorithms for various environments is performed. It is shown that the use of the quantum correction leads to a quadratic decrease in the error probability. The efficiency of applying th...

We considered the interaction of semiconductor quantum register with noisy environment leading to various types of qubit errors. We analysed both phase and amplitude decays during the process of electron-phonon interaction. The performance of quantum error correction codes (QECC) which will be inevitably used in full scale quantum information proce...