Related papers: Investigation of bi-particle states in gate-array-…
Silicon quantum computing has the potential to revolutionize technology with capabilities to solve real-life problems that are computationally complex or even intractable for modern computers [1] by offering sufficient high quality qubits…
We study coherent oscillations in double quantum dots tunnel-coupled to metallic leads by means of full counting statistics of electron transport. If two such systems are coupled by Coulomb interaction, there are in total six (instead of…
Achieving control over the electron spin in quantum dots (artificial atoms) or real atoms promises access to new technologies in conventional and in quantum information processing. Here we review our proposal for quantum computing with…
Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits. Current physical realizations of qubits require a careful calibration, composed by different experiments, due to noise and…
Recent demonstrations using electron spins stored in quantum dots array as qubits are promising for developing a scalable quantum computing platform. An ongoing effort is therefore aiming at the precise control of the quantum dots…
In double quantum dot singlet-triplet qubits, the exchange interaction is used in both quantum gate operation and the measurement of the state of the qubit. The exchange can be controlled electronically by applying gate voltage pulses. We…
We study a system of two symmetrical capacitively coupled quantum dots, each coupled to its own metallic lead, focusing on its evolution as a function of the gate voltage applied to each dot. Using the numerical renormalization group and…
Quantum embedding theories are promising approaches to investigate strongly-correlated electronic states of active regions of large-scale molecular or condensed systems. Notable examples are spin defects in semiconductors and insulators. We…
Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to…
We investigate the spin of the ground state of a geometrically confined many-electron system. For atoms, shell structure simplifies this problem-- the spin is prescribed by the well-known Hund's rule. In contrast, quantum dots provide a…
Observing quantum phase transitions in mesoscopic systems is a daunting task, thwarted by the difficulty of experimentally varying the magnetic interactions, the typical driving force behind these phase transitions. Here we demonstrate that…
Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and…
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the {\it in situ}…
Machine learning has emerged as a promising approach to study the properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods$-$such as Monte Carlo$-$which…
The hybrid approach to quantum computation simultaneously utilizes both discrete and continuous variables which offers the advantage of higher density encoding and processing powers for the same physical resources. Trapped ions, with…
We study magnetism in magnetically doped quantum dots as a function of confining potential, particle numbers, temperature, and strength of Coulomb interactions. We explore possibility of tailoring magnetism by controlling the…
The non-linear response of a beam splitter to the coincident arrival of interacting particles enables numerous applications in quantum engineering and metrology yet poses considerable challenge to achieve focused interactions on the…
We introduce a new approach for quantum linear algebra based on quantum subspace states and present three new quantum machine learning algorithms. The first is a quantum determinant sampling algorithm that samples from the distribution…
Neural networks have emerged as a promising paradigm for quantum information processing, yet they confront the challenge of generating training datasets with sufficient size and rich diversity, which is particularly acute when dealing with…
A system consisting of two independently contacted quantum dots with strong electrostatic interaction shows interdot Coulomb blockade when the dots are weakly tunnel coupled to their leads. It is studied experimentally how the blockade can…