Erratum: Quantum detector based on a superposition of macroscopic states in a phase qubit [Low Temp. Phys. 33, 15–22 (2007)]

2007 ◽  
Vol 33 (5) ◽  
pp. 484-484
Author(s):  
V. N. Shnyrkov ◽  
S. I. Melnik
Keyword(s):  
Macroscopic States ◽  
Quantum Detector ◽  
Phase Qubit

Receiving phase-keyed optical signals via quantum detector

1999 ◽  
Vol 5 (5-6) ◽  
pp. 71-76
Author(s):  
A.S. Mazmanishvili ◽  
◽  
Keyword(s):  
Optical Signals ◽  
Quantum Detector

scholarly journals Distinguishing quantum from classical oscillations in a driven phase qubit

2008 ◽  
Vol 10 (7) ◽  
pp. 073026 ◽  
Author(s):  
S N Shevchenko ◽  
A N Omelyanchouk ◽  
A M Zagoskin ◽  
S Savel'ev ◽  
Franco Nori
Keyword(s):  
Phase Qubit

scholarly journals SELF-ORGANIZATION IN COMPLEX SYSTEMS AS DECISION MAKING

2014 ◽  
Vol 17 (03n04) ◽  
pp. 1450016 ◽  
Author(s):  
V. I. YUKALOV ◽  
D. SORNETTE
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Mathematical Formulation ◽  
Conditional Entropy ◽  
Self Organization ◽  
Entropy Maximization ◽  
Natural Systems ◽  
Macroscopic States ◽  
System States ◽  
Statistical Systems

The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.

scholarly journals Superconductor qubits hamiltonian approximations effect on quantum state evolution and control

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Javad Sharifi
Keyword(s):  
Quantum State ◽  
Charge Qubit ◽  
Gate Operation ◽  
Goal State ◽  
Expected Time ◽  
State Evolution ◽  
Angle Of Deviation ◽  
Hadamard Gate ◽  
Phase Qubit ◽  
And Control

AbstractMicrowave IQ-mixer controllers are designed for the three approximated Hamiltonians of charge, phase and flux qubits and the controllers are exerted both on approximate and precise quantum system models. The controlled qubits are for the implementation of the two quantum-gates with these three fundamental types of qubits, Quantum NOT-gate and Hadamard-gate. In the charge-qubit, for implementation of both gates, in the approximated and precise model, we observed different controlled trajectories. But fortunately, applying the controller designed for the approximated system over the precise system leads to the passing of the quantum state from the desired state sooner that the expected time. Phase-qubit and flux qubit have similar behaviour under the control system action. In both of them, the implementation of NOT-gate operation led to same trajectories which arrive at final goal state at different times. But in both of those two qubits for implementation of Hadamard-gate, desired trajectory and precise trajectory have some angle of deviation, then by exerting the approximated design controller to precise system, it caused the quantum state to approach the goal state for Hadamard gate implementation, and since the quantum state does not completely reach the goal state, we can not obtain very high gate fidelity.

scholarly journals Quantum behavior of a dc SQUID phase qubit

2008 ◽  
Vol 77 (21) ◽  
Author(s):  
Kaushik Mitra ◽  
F. W. Strauch ◽  
C. J. Lobb ◽  
J. R. Anderson ◽  
F. C. Wellstood ◽  
...  
Keyword(s):  
Quantum Behavior ◽  
Phase Qubit

scholarly journals By-passing fluctuation theorems

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 231
Author(s):  
Paul Boes ◽  
Rodrigo Gallego ◽  
Nelly H. Y. Ng ◽  
Jens Eisert ◽  
Henrik Wilming
Keyword(s):  
Degrees Of Freedom ◽  
Quantum Thermodynamics ◽  
Many Body ◽  
Fluctuation Theorems ◽  
Macroscopic States ◽  
Many Body Systems ◽  
Highly Correlated ◽  
Positive Work ◽  
Thermodynamical Processes ◽  
Reduced State

Fluctuation theorems impose constraints on possible work extraction probabilities in thermodynamical processes. These constraints are stronger than the usual second law, which is concerned only with average values. Here, we show that such constraints, expressed in the form of the Jarzysnki equality, can be by-passed if one allows for the use of catalysts---additional degrees of freedom that may become correlated with the system from which work is extracted, but whose reduced state remains unchanged so that they can be re-used. This violation can be achieved both for small systems but also for macroscopic many-body systems, and leads to positive work extraction per particle with finite probability from macroscopic states in equilibrium. In addition to studying such violations for a single system, we also discuss the scenario in which many parties use the same catalyst to induce local transitions. We show that there exist catalytic processes that lead to highly correlated work distributions, expected to have implications for stochastic and quantum thermodynamics.

Quantum Detector Tomography

2013 ◽  
pp. 283-313
Author(s):  
Lijian Zhang ◽  
Hendrik Coldenstrodt-Ronge ◽  
Animesh Datta ◽  
Ian A. Walmsley
Keyword(s):  
Quantum Detector
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