# An introduction to graph states

#### What is a graph state?

One especially useful class of quantum states is graph states, also known as cluster states. As the name suggests, a graph state |G\rangle is associated with a graph,

G=(V,E),

where the vertices (v\in V) represent qubits initialised into the

|+\rangle = (|0\rangle+|1\rangle)/\sqrt{2}

state, and edges (e\in E) represent the application of controlled-phase (CZ) gates between respective vertices, where,

\mathrm{CZ}=\mathrm{diag}(1,1,1,-1).

A graph state can therefore be expressed as,

|G\rangle = \left[ \prod_{e\in E} \mathrm{CZ}_e \right] |+\rangle^{\otimes |V|}.

Since CZ gates are diagonal and therefore commute with one another, the order in which they are applied is irrelevant, meaning there is great flexibility in the preparation of graph states and room for parallelisation in the application of the required CZ gates.

An important special case is the two-qubit graph state, which is locally equivalent to a maximally-entangled Bell pair. From the definition, a two-qubit graph state can be written,

\mathrm{CZ}|+\rangle|+\rangle = \frac{1}{2}(|0,0\rangle + |0,1\rangle + |1,0\rangle - |1,1\rangle).

Applying a Hadamard gate to either qubit we obtain,

H\cdot \mathrm{CZ}|+\rangle|+\rangle = \frac{1}{\sqrt{2}}(|0,0\rangle + |1,1\rangle),

the |\Phi^+\rangle Bell pair.

Note that while a two-qubit graph state is maximally entangled, graph states, in general, are not, whose entanglement structure is defined over graph neighbourhoods, which in general are not global. The exceptions are star and fully connected graphs, both locally equivalent to GHZ states, where the entire graph forms a single neighbourhood.

#### Measurement-based quantum computing

Graph states are incredibly useful as they enable measurement-based quantum computing (MBQC) [Raussendorf, Browne & Briegel (2003)], an alternative way of thinking about quantum computation to the regular circuit model. In MBQC, computation proceeds only by measuring qubits from a graph state of some topology (usually a lattice), where the order and basis in which qubits are measured dictates the implemented computation. This model for computation has no classical analogue and is uniquely quantum.

The universality of MBQC using graph states is easiest to demonstrate by considering its equivalence with the circuit model. Following the narrative of Nielsen (2005), this can be seen by noting that the following circuit for single-qubit quantum state teleportation relies on a resource of a |+\rangle state and a CZ gate.

Note that the local operation accumulated by the second qubit depends on the measurement outcome, m, of the first. This is a general feature of MBQC; feedforward is required to apply subsequent measurement-outcome-dependent local corrections whenever a measurement is performed.

Combining single-qubit teleporters in a linear chain enables a qubit to be successively teleported, each time accumulating local operations. It can be seen that the state acting as a resource for this succession of teleporters is identically a linear graph state.

This provides a means for building up single-qubit operations using a linear graph state. We must also introduce an entangling two-qubit gate to enable universality, which we will choose to be the CZ gate.

Now consider two linear graphs that teleport two input qubits, accumulating single-qubit operations along the way. At some point, we encounter a vertical bridge between these two linear graphs. As the qubits teleport past this bridge, they accumulate a CZ operation between them, as this is identically what the bridge represents.

Visually, we can see a direct connection between the circuit model and the MBQC model, where rows equate to logical qubits and columns to circuit depth. As columns of qubits are measured out from left to right, qubits are successively teleported from one column to the next, accumulating single-qubit operations along the way. When they encounter vertical bridges, the respective qubits acquire CZ operations, providing everything necessary for a universal quantum computation.

Measurement-based quantum computing could have equally been referred to as teleportation-based quantum computing.

#### Stabiliser representation

Graph states are stabiliser states, meaning that an N-qubit state can be fully specified by N stabilisers, each of which is an N-fold tensor product of Pauli operators up to a factor of \{\pm 1,\pm i\}. A stabiliser is an operator under which the stabilised state is invariant, and the state is uniquely specified as the simultaneous +1 eigenstate of all N stabilisers,

S_i|\psi\rangle = |\psi\rangle,\,\,\forall\, i\in \{1,\dots,N\}.

Graph states have a stabiliser of the form,

S_i = X_i \bigotimes_{j\in n_i} Z_j

associated with every vertex i, where n_i denotes the neighbourhood of i, the set of vertices connected to i by an edge.

The structure of these stabilisers can be understood by noting that the |+\rangle state is stabilised by the X operator, X|+\rangle = |+\rangle. Upon applying a CZ gate between this qubit and a neighbour, the commutation relation,

\mathrm{CZ}_{i,j}\cdot X_i = X_iZ_j\cdot\mathrm{CZ}_{i,j},

implies the X_i stabiliser for qubit i gains an additional Z_j operator for every neighbour j connected by a CZ gate.

Equivalently, stabiliser states are the class of states obtained by evolving computational input states under Clifford operations (i.e. CNOT, CZ, Hadamard, Paulis & phase gates). Note that Clifford circuits map stabiliser states to stabiliser states, which is always classically efficient and insufficient for universal quantum computing [Gottesman (1998)]. However, with the addition of the single-qubit, non-Clifford T gate we obtain a universal gate set.

As an example, consider the following linear graph.

This graph has the stabilisers,

S_1 = X_1 \otimes Z_2
S_2 = Z_1 \otimes X_2 \otimes Z_3
S_3 = Z_2 \otimes X_3

If we were to apply a CZ gate to create a third edge, we would obtain a cyclic graph.

This would then have the stabilisers,

S_1 = X_1 \otimes Z_2 \otimes Z_3
S_2 = Z_1 \otimes X_2 \otimes Z_3
S_3 = Z_1 \otimes Z_2 \otimes X_3

The stabilisers associated with graph states can be represented as binary matrices, also referred to as a tableau representation or generator matrix, comprising an N\times N block representing the location of X operators and another for the location of Z operators,

[\mathbf{X} | \mathbf{Z}].

Noting that rows in this matrix can be arbitrarily permuted, the structure of graph state stabilisers, with exactly one X operator associated with each vertex, implies the X block can be expressed as the identity matrix with appropriate reordering, in which case the Z block corresponds to the adjacency matrix of the graph, noting that the rows and columns of an adjacency matrix capture respective vertex neighbourhoods, the Z operators from each stabiliser,

[\mathbf{X} | \mathbf{Z}] \to[I_N | A_G].

Using the previous example of a three-qubit linear graph, the stabilisers,

S_1 = X_1 \otimes Z_2
S_2 = Z_1 \otimes X_2 \otimes Z_3
S_3 = Z_2 \otimes X_3

can be expressed via the binary matrix,

\left[\begin{array}{ccc|ccc} 1 & 0 & 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 1 \\ 0 & 0 & 1 & 0 & 1 & 0\end{array}\right],

and it can be seen that the Z block corresponds to the expected adjacency matrix for a linear graph.

#### All stabiliser states are locally equivalent to graph states

In addition to graph states being stabiliser states, all stabiliser states are locally equivalent to graph states. An efficient classical algorithm with O(N^3) runtime exists for transforming arbitrary stabiliser states into graph states using local operations [Vijayan et al. (2022)]. Using stabiliser transformation rules in the binary representation, the goal is to diagonalise the X block, achieved using a variant of Gaussian elimination.

The concept is best illustrated by example. Consider the 3-qubit GHZ state, which may be represented using the stabilisers,

S_1 = X_1\otimes X_2\otimes X_3
S_2 = Z_1\otimes Z_2
S_3 = Z_2\otimes Z_3

with the binary matrix representation,

\left[\begin{array}{ccc|ccc} 1 & 1 & 1 & 0 & 0 & 0 \\ 0 & 0 & 0 & 1 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 & 1\end{array}\right]

Let us first apply Hadamard gates to qubits 2 and 3. Hadamard gates interchange X and Z operators, since HXH=Z and HZH=X, and stabilisers evolve under conjugation. In the binary matrix representation, this swaps the respective columns from the X and Z blocks,

\left[\begin{array}{ccc|ccc} 1 & 0 & 0 & 0 & 1 & 1 \\ 0 & 1 & 0 & 1 & 0 & 0 \\ 0 & 1 & 1 & 0 & 0 & 0\end{array}\right].

Since the product of two stabilisers is also a stabiliser, arbitrarily multiplying them together, equivalent to bitwise XORing the respective matrix rows, does not change the description of the state. Applying S_3\to S_2 S_3, we obtain,

\left[\begin{array}{ccc|ccc} 1 & 0 & 0 & 0 & 1 & 1 \\ 0 & 1 & 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 1 & 0 & 0\end{array}\right].

Our matrix is now in the required form, and the Z block corresponds to the adjacency matrix of the 3-qubit star graph. In general, an N-qubit star graph is locally equivalent to an N-qubit GHZ state, as is the fully connected graph, K_N.

#### Manipulating graph states

From the definition of a graph state, we know that CZ gates toggle the existence of edges between the vertices upon which they act. Graph states additionally exhibit elegant properties under the action of some gates and measurements, providing valuable tools for directly manipulating the graph structure associated with a state [Hein, Eisert & Briegel (2004), Hein et al. (2006)].

##### Local complementation

One important graph operation that will appear is local complementation (LC), represented by the graph operator \tau_i(G). Local complementation inverts (or complements) the edges in the subgraph induced by the neighbourhood of i.

Applying the square root of the graph stabiliser associated with vertex i,

\sqrt{S_i} = \sqrt{X_i} \bigotimes_{j\in n_i} \sqrt{Z_j},

directly implements the local complementation \tau_i,

\sqrt{S_i}|G\rangle \equiv |\tau_i(G)\rangle,

an isomorphism between the LC graph operator, \tau_i, and the state operator, \sqrt{S_i},

\sqrt{S_i} \cong \tau_i.

Note that \sqrt{S_i} comprises only local operations and hence does not affect entanglement. Nonetheless, we observe that its application generally changes the number of edges in the graph. This leads to the interesting observation that while edges in a graph state represent the application of maximally entangling CZ gates, the amount of entanglement in a graph state is not directly related to the number of edges. Locally equivalent graph states exhibiting identical entanglement structure can significantly differ in their edge count.

The extreme case is seen in the above example of the star and fully connected graphs, related by local complementation around the central vertex. Both of these states are locally equivalent to the maximally-entangled GHZ state, yet one contains the smallest number of edges a connected graph can have, O(N), while the other is maximally connected with O(N^2) edges.

While these are locally equivalent states with identical entanglement structure and computational characteristics, they differ enormously from an architectural implementation perspective. Were these two LC-equivalent states to be physically prepared using CZ gates, the star graph would clearly be preferable owing to its quadratically lower CZ count.

Single-qubit gates are generally far easier to implement than entangling ones, and theoreticians often assume they come for free. From the perspective of minimising CZ gate usage during state preparation, the local complementation orbit of a graph provides an avenue for optimisation.

##### Local complementation orbits

Since local complementation comprises only local operations, all graphs related by LC are locally equivalent. The entire class of graphs related by LC is referred to as the graph’s (local complementation) orbit, whose size, in general, grows exponentially with the number of vertices, |V|.

Note that local complementations about different vertices do not commute in general,

\tau_i\circ\tau_j(G) \neq \tau_j\circ\tau_i(G),\,\,\forall\, G,i,j.

Simultaneously, the LC operation associated with a given vertex is its own inverse, an involution,

\tau_i =\tau_i^{-1},
\tau_i^2 = I.

The LC orbit of a graph admits a non-Abelian group structure, where the LC operators \tau_i act as the group generators, and the graphs in the orbit constitute the elements of the group. The orbit may itself be represented as a graph, the group’s Cayley graph. Here, vertices represent individual graphs in the orbit, and edges, labelled by group generators, relate them via LC operators. Since the LC operators are involutions, the Cayley graph of a graph orbit is undirected.

Graph orbits are hard to explore, given their in-general exponential scaling and the complexity of navigating them. Finding the LC-equivalence between two graphs is NP-complete in general, while the problem of counting the orbits of a graph is in general #P-complete [Dahlberg, Helsen & Wehner (2019); Adcock et al. (2020)].

A sequence of LC operations,

\tau_{\vec{u}}(G) = \tau_{u_{|u|}} \circ \dots \circ \tau_{u_1}(G)

may be represented by an ordered (since LC operators are non-commutative) list of vertex labels where complementations took place,

\vec{u} = \{u_1,\dots,u_{|u|}\},\,\,u_i\in V\,\,\forall\,i,

which may be of any length and have repeat entries.

A graph G’ is said to be a vertex-minor of G if it can be reached from G via a sequence of local complementations, \vec{u}, and vertex deletions, for which we use the notation,

G’<G.

The vertex-minor problem is the decision problem of finding a sequence of local complementations, \vec{u}, such that \tau_{\vec{u}}(G) = G’, up to vertex deletions, which may always be performed at the end.

This problem is known to be NP-complete in general, requiring exponential classical (and quantum) runtime. While complexity proofs are onerous, the intuition behind the NP-completeness of the vertex-minor problem can be seen by framing it as a satisfiability problem.

Defining the polynomial-time function,

f(\vec{u},G,G’)\to \begin{cases} 1, & \tau_{\vec{u}}(G) = G’ \\ 0, & \tau_{\vec{u}}(G) \neq G’ \end{cases}

solving vertex-minor is now equivalent to finding a satisfying input string, \vec{u}, such that f(\vec{u},G,G’)=1. Unstructured satisfiability (or SAT) problems of this form are NP-complete in general, noting there is more nuance than this as this is not an unstructured satisfiability problem.

Counting the orbits of the N-qubit GHZ state is definitely not #P-complete, as there are always exactly two. It therefore exhibits O(1) time-complexity, where 1=2.

##### Pauli measurements

Up to measurement-outcome-dependent local corrections, Pauli measurements implement simple graph transformation rules as follows:

• Pauli-Z: Delete vertex i from G,

G\to G-i.
• Pauli-Y: Locally complement the neighbourhood of i and delete vertex i,

G\to \tau_i(G)-i.
• Pauli-X: For any qubit b neighbouring i, locally complement b, apply rule for Y, then locally complement b again,

G\to \tau_b(\tau_i \circ \tau_b(G)-i).

Since Pauli measurements induce simple graph transformation rules, this immediately implies that graph states combined with Pauli measurements are classically efficient to simulate; simultaneously, Pauli measurements alone are insufficient for universal MBQC, which cannot be efficiently classically simulated. Furthermore, any local Clifford operation may be expressed in terms of efficient graph transformation rules [Van den Nest, Dehaene & De Moor (2004)]. This observation is analogous to the Gottesman-Knill theorem [Gottesman (1998)], that stabiliser states in combination with Clifford evolution and Pauli measurements are classically efficient to simulate, recalling that all stabiliser states are locally equivalent to graph states.

#### Graph surgery

These measurement properties facilitate convenient surgical graph operations. Suppose we became aware that we lost a qubit from a graph state. Rather than discard the entire state and start from scratch, we can measure out the neighbouring qubits in the Z-basis, thereby detaching the lost qubit from the graph, after which the damaged section can be reconstructed by replacing the measured and lost qubits with fresh ones and reconnecting them using CZ gates.

Pauli measurements also provide the tools for contracting larger graphs with redundant qubits down to ones with the required substructure, thereby etching the structure into the graph. Any subgraph of G induced by vertex set S, G[S], may be obtained by measuring out all vertices V(G-S) in the Z basis, while Y measurements may be employed to perform contractions. This provides an elegant means for arguing that lattices of various topologies are universal for MBQC — if a lattice can be reduced to substructures reflecting arbitrary quantum circuits, it can be considered a substrate for universal MBQC.

#### Graph state compilation

Quantum algorithms are often designed in the circuit model. While etching circuits into a corresponding graph structure works by equivalence, it doesn’t exploit the more general graph-theoretic structure of graph states, rather imposing an existing way of thinking onto a different paradigm in a highly constrained way.

A more direct and resource-efficient approach for the compilation of quantum circuits into graph states was presented by Vijayan et al. (2022), known as algorithm-specific graph states, distinguishing them from universal ones like lattices. Conceptually, the approach is to structure quantum algorithms into a form where all Clifford circuitry — the bulk of most quantum algorithms — acts on the computational input state, thereby preparing a stabiliser state, while non-Clifford gates are performed at the end and may be absorbed into non-Clifford measurements.

Considering a universal gate set comprising Clifford and T gates, it is only the non-Clifford, single-qubit T gates that present an obstacle to graph state representation. One well-known technique for converting Clifford + T circuits into Clifford-only circuits is using magic state injection. Here, performing single-qubit teleportation using a,

|A\rangle=T|+\rangle=(|0\rangle+e^{i\pi/4}|1\rangle)/\sqrt{2}

resource state, known as a magic state, implements quantum gate teleportation of a T gate. Since the teleportation circuit itself is Clifford-only, this allows any circuit to be expressed in a form comprising only Clifford operations, where some inputs are non-stabiliser states.

The T gate teleportation protocol can also be inverted, relying on measurement of the non-Clifford

A(\pi/4)=T^\dag X T

observable instead of magic states. This inverted form allows Clifford + T circuits to be expressed as stabiliser states followed by non-Clifford measurements.

Since stabiliser states are locally equivalent to graph states, this decomposition allows universal quantum circuits to be directly compiled to graph states, where computation proceeds via non-Clifford measurements.

Algorithm-specific graph states capture the entanglement structure of algorithms much more naturally than an etched circuit would. However, the resultant graphs are not unique, as their orbits are computationally equivalent.

Graph optimisation therefore becomes an important next stage in the compilation pipeline. The optimal graph for implementing a given algorithm has many variables and is subject to architectural constraints, but identifying them generally yields computationally complex optimisation problems associated with the complexity of exploring graph orbits.

#### Preparing graph states

The commutativity of CZ gates implies they are order-independent. In the context of graph states, this means edges needn’t be built up sequentially but can be parallelised, and divide-and-conquer strategies may be employed to build up large graph states from smaller subgraphs that may be prepared independently and in parallel.

This becomes especially useful when entangling gates are non-deterministic, as is often the case in many physical architectures and necessarily the case when dealing with photonic implementation.

Consider a linear graph state with N qubits. One might think that preparing this state requires successfully performing N-1 CZ gates in succession, meaning that if the gates are non-deterministic with success probability p_\mathrm{gate}, the overall state preparation probability would be p_\mathrm{gate}^{N-1}, which is exponentially decreasing and therefore inefficient.

However, this is not the correct intuition for graph states, which are generally not maximally entangled and from the Pauli measurement properties, enable a state to be rescued via surgical operations when local defects occur.

Rather than building the linear graph in a single shot, let us consider the following scenario, taken from Rohde & Barrett (2007). We have a main linear graph of length N and a resource of small linear graphs of length M, which are prepared offline. We are assuming the availability of quantum memory, or qubits with inherently long decoherence times, to enable this protocol.

We will employ a CZ gate that succeeds with probability p_\mathrm{gate}, and upon failure, destroys the qubits it acts upon, effectively tracing them out of the system.

Upon applying the CZ gate between the two graphs of length N and M, with probability p_\mathrm{gate}, the main graph grows in length to N+M. Upon failure, with probability 1-p_\mathrm{gate}, the end qubit is destroyed, and we recover the remainder of the graph by measuring out its neighbour in the Z basis, shrinking it in length to N-2.

Applying this process repeatedly, the length of our main graph proceeds as a random walk, probabilistically growing or shrinking. The figure of merit is the average length by which the graph grows or contracts. In our case, this can easily be calculated to be,

\langle\Delta\rangle = p_\mathrm{gate}M - 2(1-p_\mathrm{gate}).

If the graph is to grow on average, we require \langle\Delta\rangle > 0, which implies,

M>2(1-p_\mathrm{gate})/p_\mathrm{gate}.

A CZ gate operating with 50% probability would only require M=3 resource states to ensure growth on average.

So long as the resource states we employ are at least length M, our main graph is guaranteed to grow on average, with length growing linearly in the number of iterations t,

\langle N(t)\rangle = N(0) + \langle\Delta\rangle\cdot t.

While the above argument is for linear graph states, linear graphs alone are not computationally useful. For MBQC, we require at least a two-dimensional topology, such as a lattice. It was first shown by Nielsen (2004) how graph structure can be exploited to enable efficient preparation of arbitrarily large lattice graph states using non-deterministic gates by introducing the concept of micro-clusters. Micro-clusters are small subgraphs of a larger graph state with multiple dangling nodes, each providing an independent bonding opportunity. Micro-clusters, therefore, provide redundancy in bonding attempts should they sometimes fail.

How large do micro-clusters need to be? The probability of a micro-cluster failing to bond with another drops exponentially with the number of available attempts, M,

p_\mathrm{fail} = (1-p_\mathrm{gate})^M,

which implies the number of available bonds must scale as,

M>\frac{\log(p_\mathrm{fail})}{\log(1-p_\mathrm{gate})},

which is only logarithmic and highly efficient. Achieving 99% bonding probability using gates with 50% success probability requires only M=7 available bonds.

This observation affords enormous resource savings in the context of linear optics quantum computing (LOQC), where micro-clusters were first introduced. Resource overheads associated with the non-determinism of entangling gates in LOQC using the initial circuit-based construction described by Knill, Laflamme & Milburn (2001) (aka KLM) were ‘efficient’ in the computer scientist’s sense of exhibiting polynomial scaling but not in a practical sense as the polynomials were unpleasant ones.

#### Percolation theory

Percolation theory studies the connectedness of percolated graphs (ones with random defects) as a function of defect rate, p_\mathrm{delete}. Percolations could apply to either edges or vertices, and the measure of connectedness could be defined in many ways, commonly whether routes exist spanning the graph.

Taking an edge- or vertex-percolated lattice in the limit of large lattice dimension, L, and considering the probability of connectedness as a function of percolation probability, p_\mathrm{delete}, percolation theory predicts phase-transitions in graph connectivity at some percolation threshold, p_c.

Consider a lattice of micro-clusters. As a function of their size and gate success probability, there exists an associated probability of edge deletion in the subsequent reduced substrate graph.

The relationship between lattice dimension and computational power implies one between percolation probability and computational power [Browne et al. (2008); Pant et al. (2019); Wei, Affleck & Raussendorf (2012)]. Since the likelihood of finding paths through a graph is a function of the percolation rate, so is the expected density, hence dimension, of a lattice reduced from the available paths.

A percolated lattice graph state with missing qubits (i.e. vertex percolations — one could likewise consider edge percolations), where squares are qubits, yellow if present.

A route-finding algorithm finds sets of edge-disjoint left-to-right (red) and top-to-bottom (green) paths. The path density is a function of the percolation rate.

The path set is reduced to a regularised substrate, here an alternating bridge decomposition, a resource for universal MBQC.

All qubits not belonging to our path-set are measured out in the Z basis, while paths are contracted using Y measurements, reducing the graph to a regularised substrate of some dimension, which directly relates to its computational power.

This yields a relationship between computational power and percolation probability, where we observe computational phase-transitions for large graph dimension, L\to\infty. Above threshold, p_\mathrm{delete}>p_c, we rapidly lose computational power as the graph disconnects into islands and spanning paths cease to exist. The percolation threshold is an important parameter for engineering scalable architectures using this approach.

Since the dimension of the residual lattice is a function of the percolation rate, in turn a function of micro-cluster size and gate success probability, there exists a trade-off between the resources invested into micro-cluster size and computational return, a point of optimisation from an engineering perspective.

#### Entangling operations

Although by definition, the edges in graph states represent the action of CZ gates, these are not the only gates capable of growing graph states. Depending on the physical implementation, various other approaches to creating graph edges exist.

In photonic quantum computing, fusion gates [Browne & Rudolph (2005)] can be used to create connections in graphs. Fusion gates are partial Bell measurements, implemented using polarising beamsplitters (see my previous post, “How do photonic Bell measurements work?”). While fusion gates create new edges in a graph state, they are also destructive, meaning they destroy the two qubits they operate on. This implies some resource overhead, although this overhead is far more favourable than that imposed by optical CZ gates, which are highly complex and resource intensive to implement.

In atomic systems with suitable level structure, where energy levels couple to optical modes, a beamsplitter followed by photo-detection implements which-path erasure on photons emitted via relaxation processes, projecting the two atoms into a symmetric superposition of one being excited state and the other relaxed, equivalent to a Bell projection.

#### Error propagation

As with quantum circuits, graph states are subject to various error models. How do these propagate through graph states?

Since quantum information flows through graph states via teleportation, so will errors. Consider the single-qubit teleportation circuit, where the local correction applied to the second qubit depends on the measurement outcome of the first. If a Pauli error was introduced onto the first qubit, flipping its measurement outcome, m, this would subsequently manifest itself as a flipped local correction on the second qubit, thereby propagating the error.

In general, as qubits are measured out of a graph state, errors acting upon them are teleported onto neighbouring qubits and accumulate. In the context of micro-cluster-based approaches for tolerating gate failure, the dangling bonds facilitating multiple bonding attempts, which must subsequently be removed, imposes a trade-off between errors. While more dangling bonds implies greater tolerance against gate failure, it simultaneously implies lower tolerance against Pauli errors, which accumulate whenever a redundant bond is removed via measurement.

It is desirable to avoid unnecessarily preparing and subsequently measuring out redundant qubits, and the overall design of a measurement-based architecture must carefully evaluate the inherent tradeoffs between different error types.

Although lattice graphs are universal for MBQC, as arbitrary circuit structures can be etched into them, in addition to being wasteful, this results in unnecessary accumulation of errors.

#### Quantum error correction

Graph states are not inherently error-protected, and quantum error correction (QEC) is required as with any other model of quantum computation. Thankfully, this does not require abandoning the inherent elegance of graph states as there are graph-native QEC codes. In particular, topological codes naturally lend themselves to graph-based implementation [Raussendorf, Harrington & Goyal (2006)].

An example is the surface code [Kitaev (2003)], defined relative to a square lattice graph. Although the way surface code stabilisers are defined is distinct from graph states, a direct mapping exists between them under appropriate graph transformations [Bravyi & Raussendorf (2007)].

Such graph-based QEC codes connect us with the field of topology, where the manifestation of errors and implementation of logical operations may be defined in terms of topological invariants — properties that remain invariant under continuous deformation, from which they inherit their robustness.

For example, continuously deforming a non-trivial loop around a torus (see figure below) preserves its topological characterisation. Associating logical operators with operator strings around a loop bestows redundancy in how the logical operator can be applied. Should defects rule out a particular path, another topologically equivalent one can be chosen, allowing defects to be bypassed.

#### Fusion-based quantum computing

Fusion-based quantum computing [Bartolucci et al. (2021)] is a scalable, fault-tolerant, photonic architecture (although also applicable to other physical architectures), integrating many of the concepts we have discussed. This scheme admits more elaborate resource states with small QEC codes built into them, such that some qubits act as parity checks when measured.

Fusion gates (non-deterministic, destructive Bell measurements), implemented using polarising beamsplitters (discussed earlier), enable fusion between unit cells of encoded resource states into a three-dimensional fusion network with random defects associated with unsuccessful fusions.

Fusion measurement outcomes reveal a percolated three-dimensional lattice with average case connectivity sufficient to post-select a substructure supporting a scalable topological code in addition to the syndrome outcomes for the associated code.

#### Quantum communications networks

The goal of quantum communications networks is usually to distribute long-range Bell pairs, equivalently two-qubit graph states. Bell pairs act as a universal resource for quantum communication as they enable quantum state teleportation.

Since long-range quantum communication is generally very lossy, with attenuation increasing exponentially with distance, quantum repeater networks (or entanglement distribution networks) use divide-and-conquer techniques to subdivide long-range links into multiple short-range ones, which are iteratively merged using entanglement swapping, enabling the exponential scaling of loss with distance to be overcome and replaced with polynomial scaling.

This can also be conceptualised in terms of graph states. Based upon the same intuition for efficiently growing arbitrarily large graph states using non-deterministic gates, graph states can similarly be employed for efficient entanglement distribution in the presence of lossy channels, allowing the exponential attenuation of single-shot transmission to be overcome and replaced with efficient polynomial scaling.

Our graph state tools for dealing with gate failure (equivalently loss) can be adapted for this purpose. Using micro-cluster-type concepts, redundant bonds counter channel loss by facilitating multiple bonding attempts, where the failure of a single attempt does not compromise the remaining state.

Applying this iteratively, the goal is to engineer a distributed graph state, which can subsequently be reduced to a long-range Bell pair upon measuring out redundant, intermittent qubits.

In addition to their utility in efficiently overcoming loss in quantum repeater networks, distributed graph states act as a versatile resource for entanglement distribution. Assuming nodes in the network are cooperative and classically communicating, we have the tools to etch out subgraphs (using Z measurements) and contract them down to involve only the desired parties (using X and Y measurements).

Of particular interest are Bell pairs. Our graph transformation rules for Pauli measurements guarantee that any connected graph may be reduced to a Bell pair between any pair of nodes.

Other states of interest might also be available depending on the initial graph’s topology. GHZ states are of interest as they are maximally entangled states that facilitate various multiparty quantum protocols such as open-destination quantum state teleportation and quantum anonymous broadcasting [Christandl & Wehner (2005)]. These can be realised by etching out star graphs. In the special case of 3-qubit GHZ states, any connected 3-qubit graph state, of which there are only two (star and fully connected), is locally equivalent to a GHZ state, and any connected graph with at least three qubits can necessarily be reduced to these using Pauli measurements.

However, we are not limited to measurements when manipulating distributed graph states. We can also play local complementation games, which can be exploited in graph-state-based entanglement routing [Hahn, Pappa & Eisert (2019)].

#### Distributed quantum computing

In a futuristic scenario where large-scale quantum computers and quantum communications networks are readily available, there is a strong incentive to unify geographically dispersed quantum computational resources. Given that the power of quantum computers can, depending on the application, grow exponentially with their number of logical qubits, unifying quantum computers to act as one effectively multiplies their power, which would only accumulate additively in the absence of unification, as per classical computers.

Graph states provide an elegant framework for conceptualising how to architecturally achieve this unification.

The dimensions of the underlying lattice graph dictate the size and power of a MBQC. In the earlier example, the width of the lattice equated with circuit depth and height with the number of logical qubits involved in the computation.

Suppose two independent quantum computers individually had the capacity for N\times N lattice graphs. Rather than use them separately, with quantum communication at our disposal, enabling the creation of long-range links, our two units could stitch their lattices together in a patchwork manner, providing a distributed 2N\times N lattice, affording twice as many logical qubits or twice the circuit depth, an idea that logically generalises to any topology or number of nodes.

#### Conclusion

Graph states are often equated with measurement-based quantum computing and seen merely as a different way of achieving the same thing. However, graph states provide far more than a direct equivalence with other models of quantum computation, like the circuit model. They provide an alternate framework for conceptualising the flow and interaction of quantum information, enabling unique insights that would not come naturally from the confines of other models. These insights have been of enormous value beyond computation, finding widespread utility in other quantum information processing protocols.

The geometric abstractions graph states provide have facilitated significant advances in solving practical problems, without which a quantum future would be far less likely. In photonic quantum computing, for example, implementation via the circuit model imposes formidable — effectively prohibitive — resource overheads, for which the graph state model affords natural solutions.

Graph states have enabled quantum information processing to be united with otherwise disparate fields of mathematics, providing direct connections with graph theory, percolation theory and topology. Graph theory provides powerful tools for conceptualising the flow of quantum information and associating quantum operations with graph transformations. The connection with the field of percolation theory offers insight into the relationship between errors and quantum computational power. Graph theoretical approaches towards quantum error correction intimately connect with the field of topology, in which codes and operations can be abstracted in terms of topological invariants.

As quantum information scientists, we attempt to understand things inherently too complex to contemplate directly and must rely on different types and levels of abstraction. Problems can generally be approached from different angles, and looking at the same problem through a different lens can yield new insights and a deeper understanding. Having a broad toolkit of different angles of interpretation for viewing problems is essential. Graph states are an incredibly powerful one, enabling us to find solutions to problems that might otherwise not have been found.

#### Acknowledgements

Thank you very much to the authors allowing figures from their work to be reproduced in this post, acknowledged individually in figure captions. Thanks to Dan Browne for helpful feedback.

## 5 thoughts on “An introduction to graph states”

1. Ryan says:

Hi Peter,

Very nice post!

I believe LC-equivalence between two graphs is polynomial time via a result of Bouchet (see Recognizing locally equivalent graphs).

1. Thanks for that Ryan. I’d better double check. As we know a minor difference in definition can have a big impact on complexity. I’ll have to double check.

2. Samuel Kushnir says:

Fascinating! One request though, to better be able to read your work, is to have a way to read through the titles of your blog posts without having to scroll through the entire blog post first.

1. Thank you Samuel and thanks for pointing this out. I‘ll try and figure out how to change this.