Physical processes in the quantum regime possess non-classical properties of quantum mechanics. However, methods for quantitatively identifying such processes are still lacking. Accordingly, in this study, we develop a framework for characterizing and quantifying the ability of processes to cause quantum-mechanical effects on physical systems. We start by introducing a new concept, referred to as quantum process capability, to evaluate the effects of an experimental process upon a prescribed quantum specification. Various methods are then introduced for measuring such a capability. It is shown that the methods are adapted to quantum process tomography for implementation of process capability measure and applicable to all physical processes that can be described using the general theory of quantum operations. The utility of the proposed framework is demonstrated through several examples, including processes of entanglement, coherence, and superposition. The formalism proposed in this study provides a generic approach for the identification of dynamical processes in quantum mechanics and facilitates the general classification of quantum-information processing.
Physical processes in the quantum regime possess non-classical properties of quantum mechanics. However, methods for quantitatively identifying such processes are still lacking. Accordingly, in this study, we develop a framework for characterizing and quantifying the ability of processes to cause quantum-mechanical effects on physical systems. We start by introducing a new concept, referred to as quantum process capability, to evaluate the effects of an experimental process upon a prescribed quantum specification. Various methods are then introduced for measuring such a capability. It is shown that the methods are adapted to quantum process tomography for implementation of process capability measure and applicable to all physical processes that can be described using the general theory of quantum operations. The utility of the proposed framework is demonstrated through several examples, including processes of entanglement, coherence, and superposition. The formalism proposed in this study provides a generic approach for the identification of dynamical processes in quantum mechanics and facilitates the general classification of quantum-information processing.
The act of describing how a physical process changes a system is the basis for understanding observed phenomena. For quantum-mechanical processes in particular, the affect of processes on quantum states profoundly advances our knowledge of the natural world, from understanding counter-intuitive concepts to the development of wholly quantum-mechanical technology. Here, we show that quantum-mechanical processes can be quantified using a generic classical-process model through which any classical strategies of mimicry can be ruled out. We demonstrate the success of this formalism using fundamental processes postulated in quantum mechanics, the dynamics of open quantum systems, quantum-information processing, the fusion of entangled photon pairs, and the energy transfer in a photosynthetic pigment-protein complex. Since our framework does not depend on any specifics of the states being processed, it reveals a new class of correlations in the hierarchy between entanglement and Einstein-Podolsky-Rosen steering and paves the way for the elaboration of a generic method for quantifying physical processes.
Abstract Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system’s environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.
We introduce two quantitative measures of the strength of causal relations in quantum theory and more general physical theories. These two measures, called the maximum and minimum causal effect, quantify the maximum and minimum changes in the output of a quantum process induced by changes in its input. The maximum and minimum causal effect possesses useful properties, such as continuity and data-processing inequality. In quantum theory, they have close connections with quantum information tasks. The maximum quantum causal effect can be used to detect quantum channels with nonzero capacity for transmitting classical information. The minimum causal effect can be used to guarantee the recoverability of quantum information: every quantum process with a high value of the minimum quantum causal effect can be approximately inverted. Moreover, we show that the quantum causal effects satisfy a duality relation: if the minimum causal effect of a quantum system A on another quantum system B is high, then A must have a low value of the maximum causal effect on any other quantum system B ′ that is spacelike separated with B . This duality implies a monogamy relation for quantum causal effects, and represents a fundamental difference between quantum and classical causal relations. We illustrate the application of the maximum causal effect to the analysis of two paradigmatic examples, the first involving a coherent superposition of direct cause and common cause, and the second involving a coherent superposition of multiple quantum processes.
Abstract A Markovian quantum process can be arbitrarily divided into two or more legitimate completely‐positive (CP) subprocesses. When at least one non‐CP process exists among the divided processes, the dynamics is considered non‐Markovian. However, how to utilize minimum experimental efforts, without examining all process input states and using entanglement resources, to identify or measure non‐Markovianity is still being determined. Herein, a method is proposed to quantify non‐CP processes for identifying and measuring non‐Markovianity without the burden of state optimization and entanglement. This relies on the non‐CP processes as new quantum process capabilities and can be systematically implemented by quantum process tomography. Additionally, an approach for witnessing non‐Markovianity by analyzing at least four system states without process tomography is provided. It is faithfully demonstrated that this method can be explicitly implemented using all‐optical setups and applied to identify the non‐Markovianity of single‐photon and two‐photon dynamics in birefringent crystals. The results also can be used to explore non‐Markovianity in other dynamical systems where process or state tomography is implementable.
An optimal quantum algorithm for "direct characterization of quantum dynamics" (DCQD) is introduced. The DCQD method can be utilized to completely determine the quantum dynamical superoperator acting on an open quantum system. In contrast to all other known quantum process tomography (QPT) schemes, this scheme relies on error-detection techniques and does not require any quantum state tomography. By analysis of the number of required experimental configurations and quantum operations in a given Hilbert space, it is demonstrated that this approach is more efficient than all other known QPT schemes. DCQD can be applied, by construction, to the task of partial characterization of quantum dynamics. Specifically, it is demonstrated that the DCQD algorithm can be efficiently used for Hamiltonian identification, and also for simultaneous determination of both the relaxation time T1 and dephasing time T2. Furthermore, it is argued that the DCQD scheme is experimentally implementable in a variety of prominent quantum information processing systems, and it is explicitly shown how it can be physically realized in photonic systems with present day technology. The ability of a quantum system to perform arbitrary or "universal" quantum operations is restricted to its naturally available/controllable interactions. It is shown that quantum systems with an effective exchange interaction can be made operationally universal by utilizing a subspace or a subsystem of the combined principal system and an auxiliary system. Moreover, it is demonstrated that universal fault-tolerant quantum computation can be performed on these systems without utilizing any form of single-qubit control. Characterization and control of open quantum systems are among the central and fundamental problems in quantum physics. A crucial primitive is the characterization of the dynamics of a quantum system that has an unknown interaction with its embedding environment. Another essential task is to control the decoherence of an open quantum system by using naturally available interactions. This thesis addresses the above fundamental problems in two separate parts. In the first part, a general theory for direct and complete characterization of quantum dynamics is introduced. In the second part, a scheme for performing arbitrary quantum operations on spin-based quantum systems is presented. This scheme can be implemented in the presence of environmental noise and faulty quantum operations without need to control the state of spins individually.
This chapter delves deeply into quantum process tomography (QPT) and regression: two critical approaches in the field of processing quantum information. The process of characterizing and estimating unknown quantum processes executed on quantum systems is known as quantum process tomography, while regression aims to find optimal parameters for input states to achieve desired unitary transformations. The chapter begins by introducing the concept of channel-state duality, which establishes a fundamental connection between quantum channels and quantum states. This duality enables the analysis of quantum processes by examining the input and output states, providing insights into their properties and behavior. Quantum process tomography is then discussed in detail, covering various tomographic methods and strategies used to reconstruct unknown quantum processes. Then the mathematical foundations, experimental techniques, and statistical analysis involved in quantum process tomography are explored to provide a comprehensive understanding of the field. Next, the chapter delves into the role of groups, the unitary group, and compact lie groups in the study of quantum processes. These mathematical concepts provide a framework for understanding the structure and symmetries of quantum processes, and representation theory is introduced as a powerful tool for decomposing and analyzing quantum processes into irreducible representations. The concept of storage of unitary and parallel application operations is crucial for the practical implementation of quantum processes insights about techniques such as parallelization and matrix factorization, which optimize the computational resources required for large-scale quantum systems, enabling efficient and scalable execution of quantum processes. The chapter also addresses the concept of finding optimal input states for learning quantum processes, adaptive measurement, and statistical optimization techniques, which enables researchers to identify the input states that provide the most informative data for accurate process estimation. Finally, the chapter concentrates on establishing the unitary operation and defining the parameterization for the initial state to achieve desired transformations. The effective learning criteria and quantum machine learning (QML) for leveraging quantum states provides advantages of QML over classical methods. In summary, this chapter provides a comprehensive exploration of quantum process tomography and regression. By leveraging channel-state duality, representation theory, and optimization techniques, researchers and practitioners can accurately characterize and estimate quantum processes. It also offers insights into the mathematical foundations, experimental methodologies, and practical applications of these techniques, contributing to the advancement of quantum information processing and technology.