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AN ALGORITHM TO PERFORM POVMS THROUGH NEUMARK THEOREM: APPLICATION TO THE DISCRIMINATION OF NON-ORTHOGONAL PURE QUANTUM STATES

WILSON Ricardo Matos Rabelo, ALEXANDRE G. RODRIGUES, REINALDO O. VIANNA

Published Sep 27, 2005• 4 citations
OpenAlex

Abstract

We consider a protocol to perform the optimal quantum state discrimination of $N$ linearly independent non-orthogonal pure quantum states and present a computational code. Through the extension of the original Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination without ambiguity by means of von Neumann measurements. Our goal is to introduce a detailed general mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection probability amplitude for each state of the ensemble. The latter determines the matrix which leads the states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of two-level rotation matrices.

Related papers

AN ALGORITHM TO PERFORM POVMS THROUGH NEUMARK THEOREM: APPLICATION TO THE DISCRIMINATION OF NON-ORTHOGONAL PURE QUANTUM STATES

WILSON R. M. RABELO, ALEXANDRE G. RODRIGUES, Reinaldo O. Vianna

We consider a protocol to perform the optimal quantum state discrimination of N linearly independent non-orthogonal pure quantum states and present a computational code. Through the extension of the original Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination without ambiguity by means of von Neumann measurements. Our goal is to introduce a detailed general mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection probability amplitude for each state of the ensemble. The latter determines the matrix which leads the states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of two-level rotation matrices.

arXiv:quant-ph/0509191•International Journal of Modern Physics C•Aug 1, 2006•2 citations

Algoritmos para a informação quântica: discriminação de estados quânticos e modelo híbrido

WILSON Ricardo Matos Rabelo

In this thesis we consider a protocol to perform the optimal quantum state discrimination of N linearly independent non-orthogonal pure quantum states. This protocol obtains the best set of generalized measurements for the problem of the unambiguous state discrimination. Through the extension of the Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination. We introduce a detailed mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection conclusive probability amplitude for each quantum state of the ensemble. The latter determines the matrix which leads the input states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of 1-qubit rotation matrices. Subsequently, we show applications of the protocol in the quantum cryptography and state filtering, and present a computational code in MATLAB. Our additional result deals with the computational hybrid models. In particular, we study the Semi-quantum Computer. We discuss the performance of the Search and Fourier Transform algorithms on a hybrid architecture constituted of classical and quantum processors working together. We show that the semi-quantum computer can reach an improvement over a pure classical architecture, no matter how few qubits are available and, therefore, it suggests an easier implementable technology.

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A Semidefinite Programming Approach to Optimal Unambiguous Discrimination of Quantum States

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Abstract—In this paper, we consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform (GU) state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound GU state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint. Index Terms—Compound geometrically uniform (CGU) quantum states, equal-probability measurement (EPM), geometrically uniform (GU) quantum states, quantum detection, semidefinite programming, unambiguous discrimination. I.

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A semidefinite programming approach to optimal unambiguous discrimination of quantum states

Yonina C. Eldar

We consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform (GU) state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound GU state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint.

arXiv:quant-ph/0206093•IEEE Transactions on Information Theory•Feb 1, 2003•120 citations

von Neumann measurement is optimal for detecting linearly independent mixed quantum states

Yonina C. Eldar

We consider the problem of designing a measurement to minimize the probability of a detection error when distinguishing between a collection of possibly nonorthogonal mixed quantum states. We show that if the quantum state ensemble consists of linearly independent density operators, then the optimal measurement is an orthogonal von Neumann measurement consisting of mutually orthogonal projection operators, and not a more general positive operator-valued measure.

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Minimum Error Discrimination of Linearly Independent Pure States: Analytic Properties of POVM

Tanmay Singal, Sibasish Ghosh A

The optimization conditions for minimum error discrimination of linearly independent pure states comprise of two kinds: stationary conditions over the space of rank one projective measurements and the global maximization conditions. A discrete number of projective measurments will solve th former of which a unique one will solve the latter. In the case of three real linearly independent pure states we show that the stationary conditions translate to a system of simultaneous polynomial (non linear) equations in three variabes thus explaining why it's so difficult to obtain a closed-form solution for the optimal POVM. Additionally, our method suggests that as an ensemble of LI pure states is varied as a smooth function of some independent parameters, the optimal POVM will also vary smoothly as a function of the same parameters. By employing the implicit functions theorem we exploit this fact to obtain a technique to find the solution of MED of LI pure states by dragging the solution from a known example (say, pure orthogonal states) to any general linearly indepenent ensemble of pure states in the same Hilbert space. By employing RK4 to solve the first order coupled non-linear differential equations find that the resulting error is within the RK4 error performance.

arXiv:1402.4553•Feb 19, 2014•1 citation
We consider a protocol to perform the optimal quantum state discrimination of NNN linearly independent non-orthogonal pure quantum states and present a computational code. Through the extension of the original Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination without ambiguity by means of von Neumann measurements. Our goal is to introduce a detailed general mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection probability amplitude for each state of the ensemble. The latter determines the matrix which leads the states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of two-level rotation matrices.
We consider a protocol to perform the optimal quantum state discrimination of N linearly independent non-orthogonal pure quantum states and present a computational code. Through the extension of the original Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination without ambiguity by means of von Neumann measurements. Our goal is to introduce a detailed general mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection probability amplitude for each state of the ensemble. The latter determines the matrix which leads the states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of two-level rotation matrices.
In this thesis we consider a protocol to perform the optimal quantum state discrimination of N linearly independent non-orthogonal pure quantum states. This protocol obtains the best set of generalized measurements for the problem of the unambiguous state discrimination. Through the extension of the Hilbert space, it is possible to perform an unitary operation yielding a final configuration, which gives the best discrimination. We introduce a detailed mathematical procedure to realize this task by means of semidefinite programming and norm minimization. The former is used to fix which is the best detection conclusive probability amplitude for each quantum state of the ensemble. The latter determines the matrix which leads the input states to the final configuration. In a final step, we decompose the unitary transformation in a sequence of 1-qubit rotation matrices. Subsequently, we show applications of the protocol in the quantum cryptography and state filtering, and present a computational code in MATLAB. Our additional result deals with the computational hybrid models. In particular, we study the Semi-quantum Computer. We discuss the performance of the Search and Fourier Transform algorithms on a hybrid architecture constituted of classical and quantum processors working together. We show that the semi-quantum computer can reach an improvement over a pure classical architecture, no matter how few qubits are available and, therefore, it suggests an easier implementable technology.
Abstract—In this paper, we consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform (GU) state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound GU state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint. Index Terms—Compound geometrically uniform (CGU) quantum states, equal-probability measurement (EPM), geometrically uniform (GU) quantum states, quantum detection, semidefinite programming, unambiguous discrimination. I.
We consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform (GU) state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound GU state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint.
The optimization conditions for minimum error discrimination of linearly independent pure states comprise of two kinds: stationary conditions over the space of rank one projective measurements and the global maximization conditions. A discrete number of projective measurments will solve th former of which a unique one will solve the latter. In the case of three real linearly independent pure states we show that the stationary conditions translate to a system of simultaneous polynomial (non linear) equations in three variabes thus explaining why it's so difficult to obtain a closed-form solution for the optimal POVM. Additionally, our method suggests that as an ensemble of LI pure states is varied as a smooth function of some independent parameters, the optimal POVM will also vary smoothly as a function of the same parameters. By employing the implicit functions theorem we exploit this fact to obtain a technique to find the solution of MED of LI pure states by dragging the solution from a known example (say, pure orthogonal states) to any general linearly indepenent ensemble of pure states in the same Hilbert space. By employing RK4 to solve the first order coupled non-linear differential equations find that the resulting error is within the RK4 error performance.