After getting the basics of QC down with quantum.country, I find thinking about and surveying quantum programming languages a lot of fun.

Below, I have my textbook implementation of Grover search in Cirq (runnable Colab). Cirq docs have many other textbook algorithm implementations here.

Some raw thoughts:

1. One has to think in terms of individual single-qubit and multi-qubit logic gates, which is very error-prone and tedious, especially when uncomputation is needed. This is in contrast with classical programming, where thinking in terms of elementary NAND gates is too low level.
2. Depending on your quantum hardware, some circuits might need redesigning, since they may not be suited to the underlying device’s constraints. Cirq exposes these via devices. While it is easy to grab a NamedQubit in simulation mode as needed. In real code, compilers will need to help programmers translate higher level ideas to circuits that can actually run on the target machine.
3. Testing and debugging quantum programs will be interesting. Black-box testing of classical results can probably be done by running tests on simulators. Interactively debugging programs will be challenging as we now have a legitimate case for Heisenbugs! Interesting papers: 1, 2.
4. Imagine a computer game that introduces quantum effects. Most of the game will be a classical program, which will call out to a quantum subsystem to efficiently do the quantum stuff. Would a single language for both classical and quantum components make sense? I think at least in the near future, a separate quantum service will be the norm in large part due to quantum hardware being only available with a few providers.

I will next look at a few quantum programming frameworks, e.g.,

__END__

## Grover search impl

In the code below, our “solution” is known in advance. To solve a real search problem, we would need additional logic to check whether a quantum state corresponds to the sought-after solution. Since our solution is known at circuit-build time, we just use some controlled-nottery for the solution-checking step.

BTW, cirq comes with a bunch of algorithm implementations, including Grover search

from cirq import NamedQubit, TOFFOLI, X, CNOT, H
import math

nqubits = 4
state_space_size = 2 ** nqubits
num_grover_iterations = round(math.pi / (4. * math.asin(1. / math.sqrt(state_space_size))) - 0.5)

print(f'State space size=#{state_space_size}, num iters=#{num_grover_iterations}')

x1 = NamedQubit('x1')
x2 = NamedQubit('x2')
x3 = NamedQubit('x3')
x4 = NamedQubit('x4')

w1 = NamedQubit('w1')
w2 = NamedQubit('w2')
w3 = NamedQubit('w3')
w4 = NamedQubit('w4')
w5 = NamedQubit('w5')

# I am not sure if reusing ancilla across iterations is ok, so creating
# one per iteration.
ancilla = [cirq.NamedQubit(f'a#{i}') for i in range(num_grover_iterations)]

def make_ancilla():
"""Returns a sequence of operations that prepare each ancilla bit in the
|-⟩ = H|1⟩ state."""
prepare_ancilla = []
for q in ancilla:
prepare_ancilla.append(X(q))
prepare_ancilla.append(H(q))
return prepare_ancilla

def make_equal_superposition_state():
return [H(x1), H(x2), H(x3), H(x4)]

def make_oracle(solution, z):
"""Returns a sequence of gates that takes |x>|z> to -|x>|z> if
|x> == |solution>, else leaves the state alone. Note that the solution
is known here, but in general this oracle will check if there is a solution
in |x>."""
assert len(solution) == 4, "I assumed a 4 qubit solution"
gates = []
uncompute_gates = []

# Convenience fn to add uncomputation operations as we add the main
# operations.
gates.append(gate(*operands))
uncompute_gates.append(cirq.inverse(gate(*operands)))

input_qubits = [x1, x2, x3, x4]
for i, x in enumerate(solution):
assert x in (1, 0), "Expected a binary solution vector"
if x == 0:

# Copy the output to the ancilla qubit since uncomputation will reset w3
# to its input state.
gates.append(CNOT(w3, z))

uncompute_gates.reverse()
gates.extend(uncompute_gates)
return gates

def make_search_step(z):
"""Returns a sequence of ops that runs the diffusion step of Grover search
by reflecting |x> about |E> where |E> is the equal superposition state.
z is an ancilla qubit. |x>|z> should be first passed through an oracle step.
"""
return [
H(x1), H(x2), H(x3), H(x4),
X(x1), X(x2), X(x3), X(x4),
TOFFOLI(x1, x2, w1),
TOFFOLI(w1, x3, w2),
TOFFOLI(w2, x4, w3),

# Leave the result in the ancilla.
CNOT(w3, z),

# uncompute
TOFFOLI(w2, x4, w3),
TOFFOLI(w1, x3, w2),
TOFFOLI(x1, x2, w1),
X(x1), X(x2), X(x3), X(x4),
H(x1), H(x2), H(x3), H(x4),
]

def make_ckt(solution):
"""Creates a circuit to search for the given solution, which should be a
4-element binary (0/1) array."""
circuit = cirq.Circuit()
circuit.append(make_equal_superposition_state())
circuit.append(make_ancilla())
for iter in range(num_grover_iterations):
circuit.append(make_oracle(solution, ancilla[iter]))
circuit.append(make_search_step(ancilla[iter]))
circuit.append([cirq.measure(q) for q in (x1, x2, x3, x4)])
return circuit


Now we instantiate the algorithm to find the solution “1001” and simulate:

circuit = make_ckt([1, 0, 0, 1])
print(circuit)

sim = cirq.Simulator()
result = sim.simulate(circuit, initial_state=0)
print(result)


Output:

measurements: x1=1 x2=0 x3=0 x4=1
output vector: -0.354|0000001001⟩ + 0.354|0010001001⟩ + 0.354|0100001001⟩ - 0.354|0110001001⟩ + 0.354|1000001001⟩ - 0.354|1010001001⟩ - 0.354|1100001001⟩ + 0.354|1110001001⟩