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arXiv:2011.01987 · Journal of Physics A Mathematical and Theoretical

Constructing a qubit POVM from quantum data

Mark Hillery

Published Aug 18, 2020• 1 citation
arXivHTMLPDF

Abstract

Abstract Given an ensemble of qubits, which we are told consists of a mixture of two pure states, one with probability η 0 and one with probability η 1 , we want to find a POVM that will discriminate between the two states by measuring the qubits. We do not know the states, and for any given qubit, we do not know which of the two states it is in. This can be viewed as learning a POVM from quantum data. Once found, the POVM can be used to separate the remaining qubits in the ensemble into two groups, corresponding to the two states present in the ensemble. In order to find the POVM, we need more information about the possible states. We examine several cases. First, we suppose that we know that the Bloch vectors of the states lie in the x – z plane and their a priori probabilities are equal. We next keep the restriction to the x – z plane, but allow the a priori probabilities to be different. Finally, we consider the case in which the Bloch vectors of the states have the same z component.

DOI
10.1088/1751-8121/abb043

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Abstract Given an ensemble of qubits, which we are told consists of a mixture of two pure states, one with probability η 0 and one with probability η 1 , we want to find a POVM that will discriminate between the two states by measuring the qubits. We do not know the states, and for any given qubit, we do not know which of the two states it is in. This can be viewed as learning a POVM from quantum data. Once found, the POVM can be used to separate the remaining qubits in the ensemble into two groups, corresponding to the two states present in the ensemble. In order to find the POVM, we need more information about the possible states. We examine several cases. First, we suppose that we know that the Bloch vectors of the states lie in the x – z plane and their a priori probabilities are equal. We next keep the restriction to the x – z plane, but allow the a priori probabilities to be different. Finally, we consider the case in which the Bloch vectors of the states have the same z component.
Suppose you receive a sequence of qubits where each qubit is guaranteed to be in one of two pure states, but you do not know what those states are. Your task is to determine the states. This can be viewed as a kind of quantum state learning -- or quantum state estimation. A problem is that, without more information, all that can be determined is the density matrix of the sequence and, in general, density matrices can be decomposed into pure states in many different ways. To solve the problem, additional information, either classical or quantum, is required. We show that if an additional copy of each qubit is supplied -- that is, one receives pairs of qubits, both in the same state, rather than single qubits -- the task can be accomplished. This is possible because the mixed two-qubit state has a unique decomposition into pure product states. For illustration, we simulate numerically the symmetric, informationally complete measurement of a sequence of qubit pairs and show that the unknown states and their respective probabilities of occurrence can be inferred from the data with high accuracy. Finally, we propose an experiment that employs a product measurement and can be realized with existing technology, and we demonstrate how the data tell us the states and their probabilities. We find that it is enough to detect a few thousand qubit pairs.
Tomography allows one to estimate the density matrix describing the state an ensemble of quantum systems are prepared in (for example, polarization tomography determines the polarization state of a beam of identically prepared photons). In general, it is not possible to uniquely decompose the density matrix into its pure state components. Agarwal et al. proposed a protocol which, for a mixture composed of any two pure states of a qubit (with arbitrary probabilities), allows an observer to infer not only the density matrix but the identity of those specific pure states and their weights - the additional requirement being that the qubits arrive in pairs, where both qubits in each pair are in the same state. We experimentally demonstrate this learning-from-pairs concept using photons in the polarization degree of freedom. We use tomography to measure a sequence of single photons and make use of their time-of-arrival information to 'pair up' the photons after the measurement. From here we are able to infer the photons' polarization states and their respective probabilities, and we demonstrate this for various different choices of polarization states and ratios. Finally, we investigate our ability to discriminate between two equal mixtures of distinct pairs of orthogonal polarization states. We find that on the order of approx. 10e4 photons is typically enough to achieve tomography fidelities of approximately 0.9999. This is sufficient to discriminate between two different preparations of the same mixed state, differing by angles of less than 5 degrees between the pure states used in the two preparations.
We determine the optimal measurement that maximizes the average information gain about the state of a qubit system. The qubit is prepared in one of two known states with known prior probabilities. To treat the problem analytically we employ the formalism developed for the maximum confidence quantum state discrimination strategy and obtain the POVM which optimizes the information gain for the entire parameter space of the system. We show that the optimal measurement coincides exactly with the minimum-error quantum measurement only for two pure states, or when the two states have the same Bloch radius or they are on the same diagonal of the Bloch disk.
Beyond state vectors We cannot always assign a definite state vector |ψ〉 to a quantum system Q. It may be that Q is part of a composite system RQ that is in an entangled state |Ψ (RQ) 〉. Or it may be that our knowledge of the preparation of Q is insufficient to determine a particular state |ψ〉. Consider, for instance, a qubit sent from Alice to Bob during the BB84 key distribution protocol from Section 4.4. The state of this qubit could be |0〉, |1〉, |+〉 or |−〉, each with equal likelihood. In either case – whether Q is a subsystem of an entangled system, or the state of Q is determined by a probabilistic process – we cannot specify a quantum state vector |ψ〉 for Q. Nevertheless, in either case we are in a position to make statistical predictions about the outcomes of measurements on Q. In this chapter we describe the mathematical machinery for doing this. Mixtures of states Suppose the state of Q arises by a random process, so that the state |ψ α 〉 is prepared with probability p α . The possible states |ψ α 〉 need not be orthogonal (as you can see in the BB84 example above). We call this situation a mixture of the states |ψ α 〉, or a mixed state for short. One way to interpret a mixed state is to return to the idea of an ensemble of systems, which we introduced in Section 3.3.
A qubit pure state can be specified by a point on the Bloch sphere, and similarly certain quantum measurements can be specified by M points on a sphere which we refer to as an Etro sphere. A key objective of this paper is to compare the performance for discriminating between two qubit pure states using POVM designs based on the distribution of M points on the Etro sphere. We specifically address the case where the alignment of the M points relative to the coordinates of the sphere is unknown. Of particular interest is the insensitivity of the POVM designs as measured by the difference between the maximum and minimum probabilities of error over all alignments. We consider distributions of the M points corresponding to Platonic solids as well as optimal distributions with respect to several criteria including, for example, maximum nearest neighbor distance and minimum Riesz s-energy. We provide evidence through simulation of various performance tradeoffs such as the tradeoff between stability and best case performance.
Abstract We investigate minimum-error (ME) discrimination for mixed qubit states using a geometric approach. By analyzing positive operator-valued measure (POVM) solutions and introducing Lagrange operator Γ\Gamma Γ Γ , we develop a four-step structured instruction to find Γ\Gamma Γ Γ for N mixed qubit states. Our method covers optimal solutions for two, three, and four mixed qubit states, including a novel result for four qubit states. We introduce geometric-based POVM classes and non-decomposable subsets for constructing optimal solutions, enabling us to find all possible answers for the general problem of minimum-error discrimination for N mixed qubit states with arbitrary a priori probabilities.