Minimal physical resources for the realisation of measurementbased quantum computation
Abstract
In measurementbased quantum computation (MBQC), a special highlyentangled state (called a resource state) allows for universal quantum computation driven by singlequbit measurements and postmeasurement corrections. Physical realisations of this model have been achieved in various physical systems for low numbers of qubits. The large number of qubits necessary to construct the resource state constitutes one of the main down sides to MBQC. However, in some instances it is possible to extend the resource state on the fly, meaning that not every qubit must be realised in the devices simultaneously. We consider the question of the minimal number of physical qubits that must be present in a system to directly implement a given measurement pattern. For measurement patterns with inputs, outputs and total qubits which have flow, we show that only qubits are required, while the number of required qubits can be as high as for measurement patterns with only gflow. We discuss the implications of removing the Clifford part of a measurement pattern, using wellestablished transformation rules for Pauli measurements, for the presence of flow versus gflow, and hence the effect on the minimum number of physical qubits required to directly realise the measurement pattern.
pacs:
Valid PACS appear hereThe circuit model of quantum computation deutsch1989quantum provides a direct analogue to the common classical computational model based on networks of logic gates. On the other hand, measurementbased quantum computation (MBQC), first proposed by Raussendorf and Briegel in 2001 raussendorf2001one, provides a conceptually and practically different model. This model harnesses unique features of quantum mechanics related to entanglement and measurement, and hence does not have a direct classical counterpart. In MBQC, computation is performed by making single qubit measurements on a special resource state, consisting of qubits prepared in a specific entangled state. As each measurement result is obtained, it is used to compute corrections to be taken into account in the bases of subsequent measurements. Due to the irreversible nature of the measurement process, the model is frequently referred to as oneway. Although twodimensional cluster states were first suggested as the resource for universal quantum computation in the MBQC model raussendorf2001one, it was later shown that more general graph states could also be used hein2004multiparty; danos2004robust.
A measurementbased computation can be represented by a measurement pattern, which captures the structure of the resource state, the measurement angles assuming all nonoutput measurement results are zero, and a dependency structure used to adapt measurement angles based on nonzero measurement outcomes. The entanglement operations in a measurement pattern can be represented by a graph, where each vertex corresponds to a qubit and each edge corresponds to an entangling operation performed between the qubits indicated by the vertices it connects. This graph together with identified sets of input and output qubits is known as the open graph corresponding to the computation ^{1}^{1}1In the rest of the paper, qubits and vertices in open graphs will be used interchangeably.. Since the measurements underlying such computations do not have predetermined outcomes, it is necessary to have some dependency structure in order to guarantee determinism. The existence of such a structure for arbitrary choices of measurement angles is determined fully by the open graph. For open graphs the presence of flow danos2006determinism is a sufficient condition, and generalized flow (gflow) browne2007generalized is a sufficient and necessary condition, for the existence of an appropriate dependency structure to ensure determinism. As gflow is more general than flow, in the rest of the paper we will use gflow only in reference to open graphs which do not have flow.
The unique features of MBQC have made it a natural choice in many quantum computer architectures nielsen2004optical; browne2005resource; benjamin2006brokered; friesen2008one; blythe2006cavity. It has also emerged as a useful theoretical tool, particularly in relation to the design of secure computing protocols as blind quantum computing (BQC) broadbent2009; fitzsimons2012unconditionally; morimae2013blind. Several other tasks such as entanglement purification in the presence of noise and imperfections zwerger2013universal and quantum error correction (QEC) zwerger2014hybrid can be achieved very efficiently in a measurementbased way, i.e., with resource states of minimal size. Furthermore, MBQC allows for topological faulttolerance to be realised in a very direct and beautiful way raussendorf2007topological.
However, despite the advantages of the MBQC model, its realisation is often expensive in terms of physical qubits, as the number of qubits in a measurement pattern is usually much more than the number of logical qubits in the computation. This stems from the fact one qubit is required for each (nonClifford) single qubit gate in the computation. For example, an instance of the threequbit quantum Fourier transform (QFT), considered in Ref. hein2004multiparty, has a realisation requiring 33 qubits in a graph state. Moreover, some applications, such as verification in blind quantum computation protocols fitzsimons2012unconditionally; kashefi2015optimised; morimae2016measurement, may significantly increase the number of required qubits. MBQC has been demonstrated experimentally using various discretevariable (qubit) systems walther2005experimental; chen2007experimental; prevedel2007high; vallone2008active; tokunaga2008generation; yao2012experimental; lanyon2013measurement and continuous variable systems miwa2009demonstration; ukai2011demonstration; pooser2014continuous. However, experiments for qubit systems have generally been restricted to low numbers of qubits and scaling them up is an important challenge walther2005experimental; pooser2014continuous.
Here we examine the number of physical qubits required to realise a measurement pattern, when entanglement operations and measurements can be reordered. We consider the question of whether the whole resource state has to be constructed at the beginning, or whether it is possible to add qubits on an as needed basis. In the latter case, we consider the minimal number of necessary physical qubits at any time, which we denote . We show that is different for open graphs with flow versus those with only gflow, and in some instances this difference can be dramatic. There is a well established method for translating from quantum circuits to measurement patterns through the use of gate teleportation childs2005unified. Our results can be thought of as providing a sensible method for doing the reverse translation, from measurement pattern to circuit model, even for patterns which may have been created without reference to circuits. This is particularly in the case of blind and verifiable quantum computing protocols naturally constructed in the measurement based model broadbent2009; fitzsimons2012unconditionally; morimae2013blind, providing a way to implement such protocols in devices which directly implement the circuit model with far lower qubit resources. It also provides a mechanism to take advantage of phenomena such as flow ambiguity mantri2016 directly in the circuit model augmented with individual gate teleportations.
The remainder of the paper is structured as follows. We begin by introducing needed definitions and background. We then derive the required physical qubit resources for measurementbased computations for the cases of flow and gflow. We conclude with the examination of the effect of removing Pauli measurements, which implement Clifford group gates, in terms of its effect on the presence of flow.
Following the notation of danos2007measurement, a measurementbased computation can be represented by a measurement pattern, or simply pattern. A pattern is defined as P = (V, I, O, A), where V is the set of qubits, I V and O V are two possibly overlapping sets representing the inputs and outputs of the computation respectively and A is a finite set of operations which act on V as defined in the following:

1qubit auxiliary preparation prepares a qubit v V in the state ,

2qubit entanglement operation performs a CZ operation on qubits u,v V,

1qubit correction operations and apply Pauli X and Z corrections on qubit v, and

1qubit measurement operation measures the qubit v in the orthonormal basis of , where is called the angle of measurement.
For a graph , denotes the set of its vertices and is the set of its edges. An open graph is a triplet , where is an undirected graph and are respectively the sets of input and output vertices. The size of is its number of vertices is denoted by . Noninput vertices are denoted by (the complement of in the graph) and nonoutput vertices are denoted by (the complement of in the graph).
Flow and gflow on open graphs, as defined in the following, determine an ordering of measurements which guarantees that measurement angles can always be adapted based on previous results to implement a unitary transformation deterministically, for any choice of measurement angles.
Definition 1 (Danos & Kashefi danos2006determinism).
An open graph has flow if and only if there exists a map and a strict partial order over such that all of the following conditions hold for all .

,

if , then or , where contains adjacent vertices of in ,

.
In this case, () is called a flow on .
To aid clarity, we will make use of the notation , if and , if where .
Definition 2 (Browne et al. browne2007generalized).
An open graph has generalised flow (gflow) if and only if there exists a map (the set of all subsets of vertices in ) and a strict partial order over such that all of the following conditions hold for all .

if then ,

if , then or , where ,

.
In this case, is called a gflow on .
Let be an open graph with flow. Then, a structure called path cover de2008finding is induced in as defined in the following. A collection of directed paths in is called a path cover of if (i) each is included in exactly one path, in other words paths are vertexdisjoint and they cover , (ii) each path in is either disjoint from or intersects only at its initial vertex, and (iii) each path in intersects only at its final vertex. In this paper, we assume that (corresponding to patterns performing unitary transformations). In this case, for , there are paths, each starting from an input vertex, , and ending at an output vertex, (possibly overlapping), such that . The path to which qubit belongs is denoted by .
Now, we consider the reordering of the entanglement and measurement operations such that the number of physical qubits necessary at any one time is minimised. The idea is based on postponing each entangling operation as long as possible. Suppose it is the turn of a qubit to be measured with respect to an ordering of measurements induced by flow. We will denote the set of unmeasured qubits at this stage, excluding , as and the set of measured qubits as . The measurement on a particular qubit, commutes with entangling operations between and when neither nor is equal to , but does not commute with entanglement operations between and its unmeasured neighbours nikahd2015one. Therefore, these operations have to be performed first before the measurement. The set of unmeasured neighbours of is denoted by , which is equal to . The measurement of the qubit affects the state of qubits in . As no operation acts on a previously measured qubit danos2007measurement, is not required beyond this point during the realisation of a pattern.
Now, we investigate the minimal set of qubits which must simultaneously exist prior to the measurement of , excluding itself, which we label . This set is the union of two subsets of vertices: (i) the subset that is required for performing the measurement on , , and (ii) the subset of qubits which have been affected by previous operations and which have not been measured, and hence must be retained until measurement (if they do not belong to ) or until the end of computation. We now characterise this latter subset.
At the beginning of a measurementbased computation, the qubits in are provided or prepared in some joint input state and must be retained until they are measured (if they do not belong to O), or until the end of computation. When it is the turn of a qubit to be measured, the set of all unmeasured input qubits excluding is denoted . During the computation, measurements cannot be commuted past entangling operations involving the same qubit, and hence the neighbours of any measured qubits must either be measured or retained. We will denote by the subset of qubits in with measured neighbours. More formally, , where is the empty set. Therefore, we have .
Suppose it is the turn of a qubit to be measured with respect to an ordering of measurements induced by flow. Then, the following statement holds.
Lemma 3.
Let be an open graph with flow. There exists exactly one member of in each path of .
Proof.
We first prove that in each there exists at least one member of , and then we prove that this lower bound must be saturated. We will use to label this unique vertex for a particular path.
Tackling the upper bound first, for a given , one of the following two cases will happen:

: With respect to the flow definition, there is given by such that .

: In this situation, there are only two possible cases:

None of the qubits in have been measured previously. Therefore, there exists in this path.

At least one of the qubits in has been measured previously. Let be the last qubit which has been measured in this path. Therefore, we have .

This guarantees that at least one qubit in each path must be in , when the input state is left unspecified.
We now show that if , and , then . The proof is done by contradiction. Suppose and without loss of generality, suppose . In such a situation, it must be the case that . Therefore, one of the following two cases will occur:

: Based on the flow definition, has to be measured before which belongs to . Therefore, .

: Based on the flow definition, has to be measured before all of the neighbours of , but since , a neighbour of has been previously measured. Therefore, .
This leads directly to the conclusion that in each , is the unique member of . ∎
In Theorem 4, is determined for open graphs with flow.
Theorem 4.
Let be an open graph with flow, with the same number of inputs and outputs, . To realise patterns with the underlying open graph, is , where is the whole number of qubits in the pattern.
Proof.
First, consider the case that . In this case, is trivially equal to . Now, suppose that , and in this case, according to Lemma 3, the size of is equal to the number of paths in the graph, trivially equal to , and therefore by including the presence of , we have .∎
Although we have shown that for open graphs with flow on inputs is , it is not the case for open graphs with gflow. This is demonstrated by constructing a family of open graphs which require large numbers of qubits to be present as a counterexample. We will consider open graphs with inputs, , outputs, , and intermediate qubits, , where . Rather than specifying the edges of directly, we instead specify the edges of the graph obtained by edge complement of . This is for simplicity since will be highly connected. The graph , shown in Fig. 1, has the following edges: for , for , and .
A gflow on can be found by applying the algorithm proposed in Ref. mhalla2008finding, which yields the following: for , for , and . Since from Fig. 1 the maximum degree of can easily be seen to be , the minimal degree of must be equal to . Starting from a qubit in a partial order induced by a gflow on this open graph, we have . Therefore .
We conclude by examining the effect of measurement of Pauli operators on graphs with flow and those with gflow, since this can alter the presence of flow. Unitary operators which map Pauli group operators to the Pauli group under conjugation are known as Clifford group operations. Any of these operators can be implemented by patterns with Pauli measurements and only browne2006one. Due to the nature of corrections made during an MBQC, measurements of Pauli operators are unaffected and can be shifted to the start of the computation. In Ref. hein2004multiparty, general transformation rules for graphs are described when Pauli measurements are performed on qubits. This allows for Pauli measurements to be eliminated by modifying the graph state to be prepared and updating the other measurement bases. For example, in the case of a measurement on qubit , the graph corresponding to the resulting state is obtained by replacing the subgraph consisting of neighbours of by its complement, and removing and any incident edges. Measurement bases of qubits neighbouring also need to be updated.
Consider an open graph where is a graph consisting of (shown in Fig. 1) and another vertex, which is connected to all of the vertices of . has a flow as follows: for , , , for , and . Thus, . It can be readily verified that when is measured in the basis, will be transformed to , which has been previously shown that has gflow, with . On the other hand, when any vertex in is measured in the basis, will lead to an open graph which has gflow but not flow. In Fig. 2, further examples are given where measurement maintains flow and where Pauli measurement introduces flow to an open graph that previously had only gflow. This highlights the fact that when certain measurements are fixed to a Pauli basis in measurement pattern, their removal can have either a positive or negative effect on the minimal physical qubit resources necessary to implement the pattern.
Acknowledgements – The authors thank Tommaso Demarie, Yingkai Ouyang, and Atul Mantri for useful comments on an earlier version of this paper. The second author is grateful to Eesa Nikahd for helpful discussions. The authors acknowledge support from Singapore’s Ministry of Education and National Research Foundation, and the Air Force Office of Scientific Research under AOARD grant FA23861514082. This material is based on research funded in part by the Singapore National Research Foundation under NRF Award NRFNRFF201301.