Quick Start

In this quick start, we list several common use cases for Yao before you go deeper into the manual.

Create a quantum register/state

A register is an object that describes a device with an internal state. See Registers for more details. Yao use registers to represent quantum states. The most common register is the ArrayReg, you can create it by feeding a state vector to it, e.g

julia> using Yao
julia> ArrayReg(rand(ComplexF64, 2^3))ArrayReg{2, ComplexF64, Array...}
    active qubits: 3/3
    nlevel: 2
julia> zero_state(5)ArrayReg{2, ComplexF64, Array...}
    active qubits: 5/5
    nlevel: 2
julia> rand_state(5)ArrayReg{2, ComplexF64, Array...}
    active qubits: 5/5
    nlevel: 2
julia> product_state(bit"10100")ArrayReg{2, ComplexF64, Array...}
    active qubits: 5/5
    nlevel: 2
julia> ghz_state(5)ArrayReg{2, ComplexF64, Array...}
    active qubits: 5/5
    nlevel: 2

the internal quantum state can be accessed via statevec method

julia> statevec(ghz_state(2))4-element Vector{ComplexF64}:
 0.7071067811865476 - 0.0im
                0.0 + 0.0im
                0.0 + 0.0im
 0.7071067811865476 - 0.0im

for more functionalities about registers please refer to the manual of registers.

Create quantum circuit with Yao blocks

Yao uses the quantum "block"s to describe quantum circuits, e.g the following code creates a 2-qubit circuit

julia> chain(2, put(1=>H), put(2=>X))nqubits: 2
chain
├─ put on (1)
│  └─ H
└─ put on (2)
   └─ X

where H gate is at 1st qubit, X gate is at 2nd qubit. A more advanced example is the quantum Fourier transform circuit

julia> A(i, j) = control(i, j=>shift(2π/(1<<(i-j+1))))A (generic function with 1 method)
julia> B(n, k) = chain(n, j==k ? put(k=>H) : A(j, k) for j in k:n)B (generic function with 1 method)
julia> qft(n) = chain(B(n, k) for k in 1:n)qft (generic function with 1 method)
julia> qft(3)nqubits: 3
chain
├─ chain
│  ├─ put on (1)
│  │  └─ H
│  ├─ control(2)
│  │  └─ (1,) shift(1.5707963267948966)
│  └─ control(3)
│     └─ (1,) shift(0.7853981633974483)
├─ chain
│  ├─ put on (2)
│  │  └─ H
│  └─ control(3)
│     └─ (2,) shift(1.5707963267948966)
└─ chain
   └─ put on (3)
      └─ H

Create Hamiltonian with Yao blocks

the quantum "block"s are expressions on quantum operators, thus, it can also be used to represent a Hamiltonian, e.g we can create a simple Ising Hamiltonian on 1D chain as following

julia> sum(kron(5, i=>Z, mod1(i+1, 5)=>Z) for i in 1:5)nqubits: 5
+
├─ +
│  ├─ +
│  │  ├─ +
│  │  │  ├─ kron
│  │  │  │  ├─ 1=>Z
│  │  │  │  └─ 2=>Z
│  │  │  └─ kron
│  │  │     ├─ 2=>Z
│  │  │     └─ 3=>Z
│  │  └─ kron
│  │     ├─ 3=>Z
│  │     └─ 4=>Z
│  └─ kron
│     ├─ 4=>Z
│     └─ 5=>Z
└─ kron
   ├─ 1=>Z
   └─ 5=>Z

Automatic differentiate a Yao block

Yao has its own automatic differentiation rule implemented, this allows one obtain gradients of a loss function by simply putting a ' mark behind expect or fidelity, e.g

julia> expect'(X, zero_state(1)=>Rx(0.2))ArrayReg{2, ComplexF64, Array...}
    active qubits: 1/1
    nlevel: 2 => Any[-0.0]

or for fiedlity

julia> fidelity'(zero_state(1)=>Rx(0.1), zero_state(1)=>Rx(0.2))(ArrayReg{2, ComplexF64, Array...}
    active qubits: 1/1
    nlevel: 2 => Any[0.02498958463533917], ArrayReg{2, ComplexF64, Array...}
    active qubits: 1/1
    nlevel: 2 => Any[-0.02498958463533917])

using Yao.EasyBuild, YaoPlots
using Compose

# show a qft circuit
Compose.SVG(plot(qft_circuit(5)))

Quick Start

Create a quantum register/state

Create quantum circuit with Yao blocks

Create Hamiltonian with Yao blocks

Automatic differentiate a Yao block

Combine Yao with ChainRules/Zygote

Symbolic calculation with Yao block

Plot quantum circuits

Convert quantum circuits to tensor network

Simplify quantum circuit with ZX calculus