# Data Structures and Modeling

## Classical Data Structures

Along with user-defined types for representing quantum concepts, the canon also provides operations, functions, and types for working with classical data used in the control of quantum systems. For instance, the Reversed function takes an array as input and returns the same array in reverse order. This can then be used on an array of type Qubit[] to avoid having to apply unnecessary $\operatorname{SWAP}$ gates when converting between quantum representations of integers. Similarly, types of the form (Int, Int -> T) can be useful for representing random access collections, so the LookupFunction function provides a convenient way of constructing such types from array types.

### Pairs

The canon supports functional-style notation for pairs, complementing accessing tuples by deconstruction:

let pair = (PauliZ, register); // type (Pauli, Qubit[])
ApplyToEach(H, Snd(pair)); // No need to deconstruct to access the register.


### Arrays

The canon provides several functions for manipulating arrays. These functions are type-parameterized, and thus can be used with arrays of any Q# type. For instance, the Reversed function returns a new array whose elements are in reverse order from its input. This can be used to change how a quantum register is represented when calling operations:

let leRegister = LittleEndian(register);
// QFT expects a BigEndian, so you can reverse before calling.
QFT(BigEndian(Reversed(leRegister!)));
// This is how the LittleEndianAsBigEndian function is implemented:
QFT(LittleEndianAsBigEndian(leRegister));


Similarly, the Subarray function can be used to reorder or take subsets of the elements of an array:

// Applies H to qubits 2 and 5.
ApplyToEach(H, Subarray([2, 5], register));


When combined with flow control, array manipulation functions such as Zipped can provide a powerful way to express quantum programs:

// Applies X₃ Y₁ Z₇ to a register of any size.
ApplyToEach(
ApplyPauli(_, register),
Map(
EmbedPauli(_, _, Length(register)),
Zipped([PauliX, PauliY, PauliZ], [3, 1, 7])
)
);


## Oracles

In the phase estimation and amplitude amplification literature the concept of an oracle appears frequently. Here the term oracle refers to a quantum subroutine that acts upon a set of qubits and returns the answer as a phase. This subroutine often can be thought of as an input to a quantum algorithm that accepts the oracle, in addition to some other parameters, and applies a series of quantum operations and treating a call to this quantum subroutine as if it were a fundamental gate. Obviously, in order to actually implement the larger algorithm a concrete decomposition of the oracle into fundamental gates must be provided but such a decomposition is not needed in order to understand the algorithm that calls the oracle. In Q#, this abstraction is represented by using that operations are first-class values, such that operations can be passed to implementations of quantum algorithms in a black-box manner. Moreover, user-defined types are used to label the different oracle representations in a type-safe way, making it difficult to accidentally conflate different kinds of black box operations.

Such oracles appear in a number of different contexts, including famous examples such as Grover's search and quantum simulation algorithms. Here, the focus is on the oracles needed for just two applications: amplitude amplification and phase estimation.

### Amplitude amplification oracles

The amplitude amplification algorithm aims to perform a rotation between an initial state and a final state by applying a sequence of reflections of the state. In order for the algorithm to function, it needs a specification of both of these states. These specifications are given by two oracles. These oracles work by breaking the inputs into two spaces, a "target" subspace and an "initial" subspace. The oracles identify such subspaces, similar to how Pauli operators identify two spaces, by applying a $\pm 1$ phase to these spaces. The main difference is that these spaces need not be half-spaces in this application. Also note that these two subspaces are not usually mutually exclusive: there are vectors that are members of both spaces. If this were not true then amplitude amplification would have no effect. Therefore, you need the initial subspace to have non-zero overlap with the target subspace.

Denote the first oracle that you need for amplitude amplification to be $P_0$, defined to have the following action. For all states $\ket{x}$ in the "initial" subspace $P_0 \ket{x} = -\ket{x}$ and for all states $\ket{y}$ that are not in this subspace, you have $P_0 \ket{y} = \ket{y}$. The oracle that marks the target subspace, $P_1$, takes exactly the same form. For all states $\ket{x}$ in the target subspace (that is, for all states that you'd like the algorithm to output), $P_1\ket{x} = -\ket{x}$. Similarly, for all states $\ket{y}$ that are not in the target subspace $P_1\ket{y} = \ket{y}$. These two reflections are then combined to form an operator that enacts a single step of amplitude amplification, $Q = -P_0 P_1$, where the overall minus sign is only important to consider in controlled applications. Amplitude amplification then proceeds by taking an initial state, $\ket{\psi}$ that is in the initial subspace and then performs $\ket{\psi} \mapsto Q^m \ket{\psi}$. Performing such an iteration guarantees that if one starts with an initial state that has overlap $\sin^2(\theta)$ with the marked space then after $m$ iterations this overlap becomes $\sin^2([2m + 1] \theta)$. We therefore typically wish to choose $m$ to be a free parameter such that $[2m+1]\theta = \pi/2$; however, such rigid choices are not as important for some forms of amplitude amplification such as fixed point amplitude amplification. This process allows you to prepare a state in the marked subspace using quadratically fewer queries to the marking function and the state preparation function than would be possible on a strictly classical device. This is why amplitude amplification is a significant building block for many applications of quantum computing.

In order to understand how to use the algorithm, it is useful to provide an example that gives a construction of the oracles. Consider performing Grover's algorithm for database searches in this setting. In Grover's search, the goal is to transform the state $\ket{+}^{\otimes n} = H^{\otimes n} \ket{0}$ into one of (potentially) many marked states. To further simplify, look at the case where the only marked state is $\ket{0}$. Then, you design two oracles: one that only marks the initial state $\ket{+}^{\otimes n}$ with a minus sign and another that marks the marked state $\ket{0}$ with a minus sign. The latter gate can be implemented using the following process operation, by using the control flow operations in the canon:

operation ReflectAboutAllZeros(register : Qubit[]) : Unit

// Apply $X$ gates to every qubit.
ApplyToEach(X, register);

// Apply an $n-1$ controlled $Z$-gate to the $n^{\text{th}}$ qubit.
// This gate leads to a sign flip if and only if every qubit is
// $1$, which happens only if each of the qubits were $0$ before step 1.
Controlled Z(Most(register), Tail(register));

// Apply $X$ gates to every qubit.
ApplyToEach(X, register);
}


This oracle is then a special case of the RAll1 operation, which allows for rotating by an arbitrary phase instead of the reflection case $\phi = \pi$. In this case, RAll1 is similar to the R1 operation, in that it rotates about $\ket{11\cdots1}$ instead of the single-qubit state $\ket{1}$.

The oracle that marks the initial subspace is constructed similarly. In pseudocode:

1. Apply $H$ gates to every qubit.
2. Apply $X$ gates to every qubit.
3. Apply an $n-1$ controlled $Z$-gate to the $n^{\text{th}}$ qubit.
4. Apply $X$ gates to every qubit.
5. Apply $H$ gates to every qubit.

This example demonstrates using the ApplyWith operation together with the RAll1 operation discussed earlier:

operation ReflectAboutInitial(register : Qubit[]) : Unit
ApplyWithCA(ApplyToEach(H, _), ApplyWith(ApplyToEach(X, _), RAll1(_, PI()), _), register);
}


You can combine these two oracles together to rotate between the two states and deterministically transform $\ket{+}^{\otimes n}$ to $\ket{0}$ using a number of layers of Hadamard gates that is proportional to $\sqrt{2^n}$ (ie $m\propto \sqrt{2^n}$), versus the roughly $2^n$ layers that would be needed to non-deterministically prepare the $\ket{0}$ state by preparing and measuring the initial state until the outcome $0$ is observed.

### Phase estimation oracles

For phase estimation the oracles are somewhat more natural. The aim in phase estimation is to design a subroutine that is capable of sampling from the eigenvalues of a unitary matrix. This method is indispensable in quantum simulation because for many physical problems in chemistry and material science these eigenvalues give the ground-state energies of quantum systems which provides valuable information about the phase diagrams of materials and reaction dynamics for molecules. Every flavor of phase estimation needs an input unitary. This unitary is customarily described by one of two types of oracles.

Tip

The first type of oracle, called a discrete query oracle and represent with the DiscreteOracle user defined type, simply involves a unitary matrix. If $U$ is the unitary whose eigenvalues you wish to estimate, then the oracle for $U$ is simply a stand-in for a subroutine that implements $U$. For example, you could take $U$ to be the oracle $Q$ defined earlier for amplitude estimation. The eigenvalues of this matrix can be used to estimate the overlap between the initial and target states, $\sin^2(\theta)$, using quadratically fewer samples than you would need otherwise. This earns the application of phase estimation using the Grover oracle $Q$ as input the moniker of amplitude estimation. Another common application, widely used in quantum metrology, involves estimating a small rotation angle. In other words, you wish to estimate $\theta$ for an unknown rotation gate of the form $R_z(\theta)$. In such cases, the subroutine that you would interact with in order to learn this fixed value of $\theta$ for the gate is \begin{align} U & = R_z(\theta) \\ & = \begin{bmatrix} e^{-i \theta / 2} & 0 \\ 0 & e^{i\theta/2} \end{bmatrix}. \end{align}

The second type of oracle used in phase estimation is the continuous query oracle, represented by the ContinuousOracle user defined type. A continuous query oracle for phase estimation takes the form $U(t)$, where $t$ is a classically known real number. If you let $U$ be a fixed unitary, then the continuous query oracle takes the form $U(t) = U^t$. This allows you to query matrices such as $\sqrt{U}$, which could not be implemented directly in the discrete query model.

This type of oracle is valuable when you're not probing a particular unitary, but rather wish to learn the properties of the generator of the unitary. For example, in dynamical quantum simulation the goal is to devise quantum circuits that closely approximate $U(t)=e^{-i H t}$ for a Hermitian matrix $H$ and evolution time $t$. The eigenvalues of $U(t)$ are directly related to the eigenvalues of $H$. To see this, consider an eigenvector of $H$: $H \ket{E} = E\ket{E}$ then it is easy to see from the power-series definition of the matrix exponential that $U(t) \ket{E} = e^{i\phi}\ket{E}= e^{-iEt}\ket{E}$. Thus, estimating the eigenphase of $U(t)$ gives the eigenvalue $E$ assuming the eigenvector $\ket{E}$ is input into the phase estimation algorithm. However, in this case the value $t$ can be chosen at the user's discretion since for any sufficiently small value of $t$ the eigenvalue $E$ can be uniquely inverted through $E=-\phi/t$. Since quantum simulation methods provide the ability to perform a fractional evolution, this grants phase estimation algorithms an additional freedom when querying the unitary, specifically while the discrete query model allows only unitaries of the form $U^j$ to applied for integer $j$ the continuous query oracle allows you to approximate unitaries of the form $U^t$ for any real valued $t$. This is important to squeeze every last ounce of efficiency out of phase estimation algorithms because it allows you to choose precisely the experiment that would provide the most information about $E$; whereas methods based on discrete queries must make do with compromising by choosing the best integer number of queries in the algorithm.

As a concrete example of this, consider the problem of estimating not the rotation angle of a gate but the procession frequency of a rotating quantum system. The unitary that describes such quantum dynamics is $U(t)=R_z(2\omega t)$ for evolution time $t$ and unknown frequency $\omega$. In this context, you can simulate $U(t)$ for any $t$ using a single $R_z$ gate and as such do not need to restrict ourselves to only discrete queries to the unitary. Such a continuous model also has the property that frequencies greater than $2\pi$ can be learned from phase estimation processes that use continuous queries because phase information that would otherwise be masked by the branch-cuts of the logarithm function can be revealed from the results of experiments performed on non-commensurate values of $t$. Thus for problems such as this continuous query models for the phase estimation oracle are not only appropriate but are also preferable to the discrete query model. For this reason Q# has functionality for both forms of queries and leave it to the user to decide upon a phase estimation algorithm to fit their needs and the type of oracle that is available.

## Dynamical generator modeling

Generators of time-evolution describe how states evolve through time. For instance, the dynamics of a quantum state $\ket{\psi}$ is governed by the Schrödinger equation \begin{align} i\frac{d \ket{\psi(t)}}{d t} & = H \ket{\psi(t)}, \end{align} with a Hermitian matrix $H$, known as the Hamiltonian, as the generator of motion. Given an initial state $\ket{\psi(0)}$ at time $t=0$, the formal solution to this equation at time $t$ may be, in principle, written \begin{align} \ket{\psi(t)} = U(t)\ket{\psi(0)}, \end{align} where the matrix exponential $U(t)=e^{-i H t}$ is known as the unitary time-evolution operator. Note that, though the focus is on generators of this form in the following, the emphasis is that the concept applies more broadly, such as to the simulation of open quantum systems, or to more abstract differential equations.

A primary goal of dynamical simulation is to implement the time-evolution operator on some quantum state encoded in qubits of a quantum computer. In many cases, the Hamiltonian may be broken into a sum of some $d$ simpler terms

\begin{align} H & = \sum^{d-1}_{j=0} H_j, \end{align}

where time-evolution by each term alone is easy to implement on a quantum computer. For instance, if $H_j$ is a Pauli $X_1X_2$ operator acting on the 1st and 2nd elements of the qubit register qubits, time-evolution by it for any time $t$ may be implemented simply by calling the operation Exp([PauliX,PauliX], t, qubits[1..2]), which has signature ((Pauli[], Double, Qubit[]) => Unit is Adj + Ctl). As discussed later in Hamiltonian Simulation, one solution then is to approximate time-evolution by $H$ with a sequence of simpler operations

\begin{align} U(t) & = \left( e^{-iH_0 t / r} e^{-iH_1 t / r} \cdots e^{-iH_{d-1} t / r} \right)^{r} + \mathcal{O}(d^2 \max_j \|H_j\|^2 t^2/r), \end{align}

where the integer $r > 0$ controls the approximation error.

The dynamical generator modeling library provides a framework for systematically encoding complicated generators in terms of simpler generators. Such a description may then be passed to, say, the simulation library to implement time-evolution by a simulation algorithm of choice, with many details automatically taken care of.

Tip

The dynamical generator library described here is covered in the samples. For an example based on the Ising model, please see the IsingGenerators sample. For an example based on molecular Hydrogen, please see the H2SimulationCmdLine and H2SimulationGUI samples.

### Complete description of a generator

At the top level, a complete description of a Hamiltonian is contained in the EvolutionGenerator user-defined type which has two components.:

newtype EvolutionGenerator = (EvolutionSet, GeneratorSystem);


The GeneratorSystem user-defined type is a classical description of the Hamiltonian.

newtype GeneratorSystem = (Int, (Int -> GeneratorIndex));


The first element Int of the tuple stores the number of terms $d$ in the Hamiltonian, and the second element (Int -> GeneratorIndex) is a function that maps an integer index in ${0,1,...,d-1}$ to a GeneratorIndex user-defined type which uniquely identifies each primitive term in the Hamiltonian. Note that by expressing the collection of terms in the Hamiltonian as a function rather than as an array GeneratorIndex[], this allows for on-the-fly computation of the GeneratorIndex which is especially useful when describing Hamiltonians with a large number of terms.

Crucially, a convention is not imposed on which primitive terms identified by the GeneratorIndex are easy-to-simulate. For instance, primitive terms could be Pauli operators as discussed earlier, but they could also be Fermionic annihilation and creation operators commonly used in quantum chemistry simulation. By itself, a GeneratorIndex is meaningless as it does not describe how time-evolution by the term it points to may be implemented as a quantum circuit.

This is resolved by specifying an EvolutionSet user-defined type that maps any GeneratorIndex, drawn from some canonical set, to a unitary operator, the EvolutionUnitary, expressed as a quantum circuit. The EvolutionSet defines the convention of how a GeneratorIndex is structured, and also defines the set of possible GeneratorIndex.

newtype EvolutionSet = (GeneratorIndex -> EvolutionUnitary);


### Pauli operator generators

A concrete and useful example of generators are Hamiltonians that are a sum of Pauli operators, each possibly with a different coefficient. \begin{align} H & = \sum^{d-1}_{j=0} a_j H_j, \end{align} where each $\hat H_j$ is now drawn from the Pauli group. For such systems, you provide the PauliEvolutionSet() of type EvolutionSet that defines a convention for how an element of the Pauli group and a coefficient may be identified by a GeneratorIndex, which has the following signature.

newtype GeneratorIndex = ((Int[], Double[]), Int[]);


In this encoding, the first parameter Int[] specifies a Pauli string, where $\hat I\rightarrow 0$, $\hat X\rightarrow 1$, $\hat Y\rightarrow 2$, and $\hat Z\rightarrow 3$. The second parameter Double[] stores the coefficient of the Pauli string in the Hamiltonian. Note that only the first element of this array is used. The third parameter Int[] indexes the qubits that this Pauli string acts on, and must have no duplicate elements. Thus the Hamiltonian term $0.4 \hat X_0 \hat Y_8\hat I_2\hat Z_1$ may be represented as

let generatorIndexExample = GeneratorIndex(([1,2,0,3], [0.4]]), [0,8,2,1]);


The PauliEvolutionSet() is a function that maps any GeneratorIndex of this form to an EvolutionUnitary with the following signature.

newtype EvolutionUnitary = ((Double, Qubit[]) => Unit is Adj + Ctl);


The first parameter represents a time-duration, that is multiplied by the coefficient in the GeneratorIndex, of unitary evolution. The second parameter is the qubit register the unitary acts on.

### Time-dependent generators

In many cases, it might also be interesting to model time-dependent generators, as might occur in the Schrödinger equation \begin{align} i\frac{d \ket{\psi(t)}}{d t} & = \hat H(t) \ket{\psi(t)}, \end{align} where the generator $\hat H(t)$ is now time-dependent. The extension from the time-independent generators earlier to this case is straightforward. Rather than having a fixed GeneratorSystem describing the Hamiltonian for all times $t$, you instead have the GeneratorSystemTimeDependent user-defined type.

newtype GeneratorSystemTimeDependent = (Double -> GeneratorSystem);


The first parameter is a continuous schedule parameter $s\in [0,1]$, and functions of this type return a GeneratorSystem for that schedule. Note that the schedule parameter may be linearly related to the physical time parameter, for example, $s = t / T$, for some total time of simulation $T$. In general however, this need not be the case.

Similarly, a complete description of this generator requires an EvolutionSet, and so you define an EvolutionSchedule user-defined type.

newtype EvolutionSchedule = (EvolutionSet, GeneratorSystemTimeDependent);