# Quantum circuit

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In quantum information theory, a quantum circuit is a model for quantum computation in which a computation is a sequence of quantum gates, which are reversible transformations on a quantum mechanical analog of an n-bit register. This analogous structure is referred to as an n-qubit register.

Quantum mechanics, including quantum field theory, is a fundamental theory in physics which describes nature at the smallest scales of atoms and subatomic particles.

In quantum computing, a quantum register is a system comprising multiple qubits. It is the quantum analog of the classical processor register. Quantum computers perform calculations by manipulating qubits within a quantum register.

The bit is a basic unit of information in information theory, computing, and digital communications. The name is a portmanteau of binary digit.

## Reversible classical logic gates

The elementary logic gates of a classical computer, other than the NOT gate, are not reversible. Thus, for instance, for an AND gate one cannot always recover the two input bits from the output bit; for example, if the output bit is 0, we cannot tell from this whether the input bits are 0,1 or 1,0 or 0,0.

In electronics, a logic gate is an idealized or physical device implementing a Boolean function; that is, it performs a logical operation on one or more binary inputs and produces a single binary output. Depending on the context, the term may refer to an ideal logic gate, one that has for instance zero rise time and unlimited fan-out, or it may refer to a non-ideal physical device.

Reversible computing is a model of computing where the computational process to some extent is time-reversible. In a model of computation that uses deterministic transitions from one state of the abstract machine to another, a necessary condition for reversibility is that the relation of the mapping from (nonzero-probability) states to their successors must be one-to-one. Reversible computing is a form of unconventional computing.

The AND gate is a basic digital logic gate that implements logical conjunction - it behaves according to the truth table to the right. A HIGH output (1) results only if all the inputs to the AND gate are HIGH (1). If none or not all inputs to the AND gate are HIGH, a LOW output results. The function can be extended to any number of inputs.

However, reversible gates in classical computers are easily constructed for bit strings of any length; moreover, these are actually of practical interest, since irreversible gates must always increase physical entropy. A reversible gate is a reversible function on n-bit data that returns n-bit data, where an n-bit data is a string of bits x1,x2, ...,xn of length n. The set of n-bit data is the space {0,1}n, which consists of 2n strings of 0's and 1's.

In statistical mechanics, entropy is an extensive property of a thermodynamic system. It is closely related to the number Ω of microscopic configurations that are consistent with the macroscopic quantities that characterize the system. Entropy expresses the number Ω of different configurations that a system defined by macroscopic variables could assume. Under the assumption that each microstate is equally probable, the entropy is the natural logarithm of the number of microstates, multiplied by the Boltzmann constant kB. Formally,

In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed. A string is generally considered as a data type and is often implemented as an array data structure of bytes that stores a sequence of elements, typically characters, using some character encoding. String may also denote more general arrays or other sequence data types and structures.

More precisely: an n-bit reversible gate is a bijective mapping f from the set {0,1}n of n-bit data onto itself. An example of such a reversible gate f is a mapping that applies a fixed permutation to its inputs. For reasons of practical engineering, one typically studies gates only for small values of n, e.g. n=1, n=2 or n=3. These gates can be easily described by tables.

## Quantum logic gates

To define quantum gates, we first need to specify the quantum replacement of an n-bit datum. The quantized version of classical n-bit space {0,1}n is the Hilbert space

The mathematical concept of a Hilbert space, named after David Hilbert, generalizes the notion of Euclidean space. It extends the methods of vector algebra and calculus from the two-dimensional Euclidean plane and three-dimensional space to spaces with any finite or infinite number of dimensions. A Hilbert space is an abstract vector space possessing the structure of an inner product that allows length and angle to be measured. Furthermore, Hilbert spaces are complete: there are enough limits in the space to allow the techniques of calculus to be used.

${\displaystyle H_{\operatorname {QB} (n)}=\ell ^{2}(\{0,1\}^{n}).}$

This is by definition the space of complex-valued functions on {0,1}n and is naturally an inner product space. This space can also be regarded as consisting of linear superpositions of classical bit strings. Note that HQB(n) is a vector space over the complex numbers of dimension 2n. The elements of this space are called n-qubits.

In linear algebra, an inner product space is a vector space with an additional structure called an inner product. This additional structure associates each pair of vectors in the space with a scalar quantity known as the inner product of the vectors. Inner products allow the rigorous introduction of intuitive geometrical notions such as the length of a vector or the angle between two vectors. They also provide the means of defining orthogonality between vectors. Inner product spaces generalize Euclidean spaces to vector spaces of any dimension, and are studied in functional analysis. The first usage of the concept of a vector space with an inner product is due to Giuseppe Peano, in 1898.

In physics and mathematics, the dimension of a mathematical space is informally defined as the minimum number of coordinates needed to specify any point within it. Thus a line has a dimension of one because only one coordinate is needed to specify a point on it – for example, the point at 5 on a number line. A surface such as a plane or the surface of a cylinder or sphere has a dimension of two because two coordinates are needed to specify a point on it – for example, both a latitude and longitude are required to locate a point on the surface of a sphere. The inside of a cube, a cylinder or a sphere is three-dimensional because three coordinates are needed to locate a point within these spaces.

Using Dirac ket notation, if x1,x2, ...,xn is a classical bit string, then

${\displaystyle |x_{1},x_{2},\cdots ,x_{n}\rangle \quad }$

is a special n-qubit corresponding to the function which maps this classical bit string to 1 and maps all other bit strings to 0; these 2n special n-qubits are called computational basis states. All n-qubits are complex linear combinations of these computational basis states.

Quantum logic gates, in contrast to classical logic gates, are always reversible. One requires a special kind of reversible function, namely a unitary mapping, that is, a linear transformation of a complex inner product space that preserves the Hermitian inner product. An n-qubit (reversible) quantum gate is a unitary mapping U from the space HQB(n) of n-qubits onto itself.

Typically, we are only interested in gates for small values of n.

A reversible n-bit classical logic gate gives rise to a reversible n-bit quantum gate as follows: to each reversible n-bit logic gate f corresponds a quantum gate Wf defined as follows:

${\displaystyle W_{f}(|x_{1},x_{2},\cdots ,x_{n}\rangle )=|f(x_{1},x_{2},\cdots ,x_{n})\rangle .}$

Note that Wf permutes the computational basis states.

Of particular importance is the controlled NOT gate (also called CNOT gate) WCNOT defined on a quantized 2 qubit. Other examples of quantum logic gates derived from classical ones are the Toffoli gate and the Fredkin gate.

However, the Hilbert-space structure of the qubits permits many quantum gates that are not induced by classical ones. For example, a relative phase shift is a 1 qubit gate given by multiplication by the unitary matrix:

${\displaystyle U_{\theta }={\begin{bmatrix}e^{i\theta }&0\\0&1\end{bmatrix}},}$

so

${\displaystyle U_{\theta }|0\rangle =e^{i\theta }|0\rangle \quad U_{\theta }|1\rangle =|1\rangle .}$

## Reversible logic circuits

Again, we consider first reversible classical computation. Conceptually, there is no difference between a reversible n-bit circuit and a reversible n-bit logic gate: either one is just an invertible function on the space of n bit data. However, as mentioned in the previous section, for engineering reasons we would like to have a small number of simple reversible gates, that can be put together to assemble any reversible circuit.

To explain this assembly process, suppose we have a reversible n-bit gate f and a reversible m-bit gate g. Putting them together means producing a new circuit by connecting some set of k outputs of f to some set of k inputs of g as in the figure below. In that figure, n=5, k=3 and m=7. The resulting circuit is also reversible and operates on n+mk bits.

We will refer to this scheme as a classical assemblage (This concept corresponds to a technical definition in Kitaev's pioneering paper cited below). In composing these reversible machines, it is important to ensure that the intermediate machines are also reversible. This condition assures that intermediate "garbage" is not created (the net physical effect would be to increase entropy, which is one of the motivations for going through this exercise).

Now it is possible to show that the Toffoli gate is a universal gate. This means that given any reversible classical n-bit circuit h, we can construct a classical assemblage of Toffoli gates in the above manner to produce an (n+m)-bit circuit f such that

${\displaystyle f(x_{1},\ldots ,x_{n},\underbrace {0,\dots ,0} )=(y_{1},\ldots ,y_{n},\underbrace {0,\ldots ,0} )}$

where there are m underbraced zeroed inputs and

${\displaystyle (y_{1},\ldots ,y_{n})=h(x_{1},\ldots ,x_{n})}$.

Notice that the end result always has a string of m zeros as the ancilla bits. No "rubbish" is ever produced, and so this computation is indeed one that, in a physical sense, generates no entropy. This issue is carefully discussed in Kitaev's article.

More generally, any function f (bijective or not) can be simulated by a circuit of Toffoli gates. Obviously, if the mapping fails to be injective, at some point in the simulation (for example as the last step) some "garbage" has to be produced.

For quantum circuits a similar composition of qubit gates can be defined. That is, associated to any classical assemblage as above, we can produce a reversible quantum circuit when in place of f we have an n-qubit gate U and in place of g we have an m-qubit gate W. See illustration below:

The fact that connecting gates this way gives rise to a unitary mapping on n+mk qubit space is easy to check. In a real quantum computer the physical connection between the gates is a major engineering challenge, since it is one of the places where decoherence may occur.

There are also universality theorems for certain sets of well-known gates; such a universality theorem exists, for instance, for the pair consisting of the single qubit phase gate Uθ mentioned above (for a suitable value of θ), together with the 2-qubit CNOT gate WCNOT. However, the universality theorem for the quantum case is somewhat weaker than the one for the classical case; it asserts only that any reversible n-qubit circuit can be approximated arbitrarily well by circuits assembled from these two elementary gates. Note that there are uncountably many possible single qubit phase gates, one for every possible angle θ, so they cannot all be represented by a finite circuit constructed from {Uθ, WCNOT)}.

## Quantum computations

So far we have not shown how quantum circuits are used to perform computations. Since many important numerical problems reduce to computing a unitary transformation U on a finite-dimensional space (the celebrated discrete Fourier transform being a prime example), one might expect that some quantum circuit could be designed to carry out the transformation U. In principle, one needs only to prepare an n qubit state ψ as an appropriate superposition of computational basis states for the input and measure the output Uψ. Unfortunately, there are two problems with this:

• One cannot measure the phase of ψ at any computational basis state so there is no way of reading out the complete answer. This is in the nature of measurement in quantum mechanics.
• There is no way to efficiently prepare the input state ψ.

This does not prevent quantum circuits for the discrete Fourier transform from being used as intermediate steps in other quantum circuits, but the use is more subtle. In fact quantum computations are probabilistic.

We now provide a mathematical model for how quantum circuits can simulate probabilistic but classical computations. Consider an r-qubit circuit U with register space HQB(r). U is thus a unitary map

${\displaystyle H_{\operatorname {QB} (r)}\rightarrow H_{\operatorname {QB} (r)}.}$

In order to associate this circuit to a classical mapping on bitstrings, we specify

• An input registerX = {0,1}m of m (classical) bits.
• An output registerY = {0,1}n of n (classical) bits.

The contents x = x1, ..., xm of the classical input register are used to initialize the qubit register in some way. Ideally, this would be done with the computational basis state

${\displaystyle |{\vec {x}},0\rangle =|x_{1},x_{2},\cdots ,x_{m},\underbrace {0,\dots ,0} \rangle ,}$

where there are r-m underbraced zeroed inputs. Nevertheless, this perfect initialization is completely unrealistic. Let us assume therefore that the initialization is a mixed state given by some density operator S which is near the idealized input in some appropriate metric, e.g.

${\displaystyle \operatorname {Tr} \left({\big |}|{\vec {x}},0\rangle \langle {\vec {x}},0|-S{\big |}\right)\leq \delta .}$

Similarly, the output register space is related to the qubit register, by a Y valued observable A. Note that observables in quantum mechanics are usually defined in terms of projection valued measures on R; if the variable happens to be discrete, the projection valued measure reduces to a family {Eλ} indexed on some parameter λ ranging over a countable set. Similarly, a Y valued observable, can be associated with a family of pairwise orthogonal projections {Ey} indexed by elements of Y. such that

${\displaystyle I=\sum _{y\in Y}\operatorname {E} _{y}.}$

Given a mixed state S, there corresponds a probability measure on Y given by

${\displaystyle \operatorname {Pr} \{y\}=\operatorname {Tr} (S\operatorname {E} _{y}).}$

The function F:XY is computed by a circuit U:HQB(r)HQB(r) to within ε if and only if for all bitstrings x of length m

${\displaystyle \left\langle {\vec {x}},0{\big |}U^{*}\operatorname {E} _{F(x)}U{\big |}{\vec {x}},0\right\rangle =\left\langle \operatorname {E} _{F(x)}U(|{\vec {x}},0\rangle ){\big |}U(|{\vec {x}},0\rangle )\right\rangle \geq 1-\epsilon .}$

Now

${\displaystyle \left|\operatorname {Tr} (SU^{*}\operatorname {E} _{F(x)}U)-\left\langle {\vec {x}},0{\big |}U^{*}\operatorname {E} _{F(x)}U{\big |}{\vec {x}},0\right\rangle \right|\leq \operatorname {Tr} ({\big |}|{\vec {x}},0\rangle \langle {\vec {x}},0|-S{\big |})\|U^{*}\operatorname {E} _{F(x)}U\|\leq \delta }$

so that

${\displaystyle \operatorname {Tr} (SU^{*}\operatorname {E} _{F(x)}U)\geq 1-\epsilon -\delta .}$

Theorem. If ε + δ < 1/2, then the probability distribution

${\displaystyle \operatorname {Pr} \{y\}=\operatorname {Tr} (SU^{*}\operatorname {E} _{y}U)}$

on Y can be used to determine F(x) with an arbitrarily small probability of error by majority sampling, for a sufficiently large sample size. Specifically, take k independent samples from the probability distribution Pr on Y and choose a value on which more than half of the samples agree. The probability that the value F(x) is sampled more than k/2 times is at least

${\displaystyle 1-e^{-2\gamma ^{2}k},}$

where γ = 1/2 - ε - δ.

This follows by applying the Chernoff bound.

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