Run a circuit on a Virtual QPU

In the previous tutorial, we introduced some basic concepts of quantum computing, namely the quantum circuit and quantum gates.

We also learnt how to build a quantum circuit using Snowflurry and simulate the result of such circuit using our local machine.

In this tutorial, we will cover the steps involved in running a quantum circuit on a virtual Quantum Processing Unit (QPU).

QPU Object

Interactions with different QPUs are facilitated using structs (objects) that represent QPU hardware. These structures are used to implement a harmonized interface and are derived from an abstract type called AbstractQPU. This interface gives you a unified way to write code that is agnostic of the quantum service you are using. The interface dictates how to get metadata about the QPU, how to run a quantum circuit on the QPU, and more.

Now that you know what QPU objects are, let's get started by importing Snowflurry:

using Snowflurry

Virtual QPU

Next, we are going to create a virtual QPU which will run on our local machine:

qpu_v = VirtualQPU()
# output
Quantum Simulator:
   developers:  Anyon Systems Inc.
   package:     Snowflurry.jl

We can print QPU's meta data by simply using

print(qpu_v)
# output
Quantum Simulator:
   developers:  Anyon Systems Inc.
   package:     Snowflurry.jl

or alternatively, retrieve the QPU metadata in a Dict{String,String} format through the following command:

get_metadata(qpu_v)
# output
Dict{String, Union{Int64, String, Vector{Int64}}} with 2 entries:
  "developers" => "Anyon Systems Inc."
  "package"    => "Snowflurry.jl"

Now, let's create a circuit to create a Bell pair as was explained in the previous tutorial:

c = QuantumCircuit(qubit_count = 2)
push!(c, hadamard(1), control_x(1, 2))
# output
Quantum Circuit Object:
   qubit_count: 2 
   bit_count: 2 
q[1]:──H────*──
            |  
q[2]:───────X──

Although we've created a circuit that makes a Bell pair, we need to measure something in order to collect any results. In Snowflurry, we can use a Readout instruction to perform a measurement, which are built using the readout() helper function.

push!(c, readout(1, 1), readout(2, 2))
# output
Quantum Circuit Object:
   qubit_count: 2 
   bit_count: 2 
q[1]:──H────*────✲───────
            |            
q[2]:───────X─────────✲──

Here we see that a readout instruction can be added to a circuit like any other gate. Each readout instruction needs two parameters: which qubit to measure and which result bit to write to. For example, readout(2, 4) means "read qubit 2 and store the result in classical bit 4". So, in this example, the first readout reads from qubit 1 and writes that result to bit 1 of the result. The second readout does the same for qubit 2 and bit 2 of the result.

In Snowflurry, readout instructions can read from any qubit and write to any result bit but with some restrictions:

• Any readout operation must be the final operation applied to its target qubit
  • We plan to lift this restriction in future versions of Snowflurry
• Separate readout operations must write to separate result bits

With our measurements defined, we can now run this circuit on the virtual qpu for let's say 100 shots.

shots_count = 100
result, qpu_time = run_job(qpu_v, c, shots_count)

The result object is a Dict{String, Int64} that summarizes how many times each state was measured in the shots run on the QPU. It contains only non-zero entries.

print(result)

Dict("00" => 53, "11" => 47)

Here we see that, after measurement, the classical result bits were set to $\left|00\right\rangle$ in 53 of the 100 shots. In the other 47 shots, the result bits were set to $\left|11\right\rangle$. (The qubit ordering convention used is qubit number 1 on the left, with each following qubit to the right of it.)

The virtual QPU currently mimics an ideal hardware with no error. Therefore, the states $\left|01\right\rangle$ and $\left|10\right\rangle$ have a probability of zero, and they are never measured. In future versions, we expect to add noise models for sources such as crosstalk, thermal noise, etc.

In the next tutorial, we will show how to submit a job to real quantum processing hardware.