Many quantum computers have constraints on the connections between qubits. However, a quantum program may not conform to these constraints. Thus, it is necessary to perform 'layout synthesis for quantum computing', LSQC, which transforms quantum programs prior to execution so that the connectivity issues are resolved.
OLSQ can solve LSQC optimally with respect to depth, number of SWAP gates, or fidelity. There is also a transition-based mode (TB) to speed it up with little loss of optimality. TB-OLSQ can reduce SWAP count by 70% and increase fidelity by 1.30x compared to leading previous works at the time of publication.
For more details on the theory and the experiments, please refer to the paper. For more details on the software implementation, please refer to the API documentation. Below is a brief tutorial on how to use the package.
pip install -U olsq
Please make sure that you have networkx version >=2.5 and z3-solver version >=4.8.9.0 in your Python environment.
from olsq import OLSQ
# initiate olsq with depth as objective, in normal mode
lsqc_solver = OLSQ("depth", "normal")
There are two argument in the constructor of OLSQ: objective_name and mode.
objective_name:"depth","swap", or"fidelity".mode:"normal"or"transition". The latter stands for TB-OLSQ in the paper, which is usually much faster with little loss of optimality.
To perform LSQC, we need to know the connections between the qubits, which is information about the physical device.
We are going to use the setdevice method.
In general, there are three ways:
- Directly construct a device with some properties.
- Use one of the hard-coded devices (including all the devices appeared in the paper).
- Use device defined in other packages: refer to later parts of this tutorial on Cirq and Qiskit.
from olsq.device import qcdevice
# directly construct a device from properties needed by olsq
lsqc_solver.setdevice( qcdevice(name="dev", nqubits=5,
connection=[(0, 1), (1, 2), (1, 3), (3, 4)], swap_duration=3) )
We use a minimalist class qcdevice to store the properties of the device that we need, which can be constructed with these arguments.
(The last three are only for fidelity optimization.)
namenqubits: the number of physical qubitsconnection: a list of physical qubit pairs corresponding to edges in the coupling graphswap_duration: number of cycles a SWAP gate takes. Usually it is either one, or three meaning three CX gates.fmeas: a list of measurement fidelityfsingle: a list of single-qubit gate fidelityftwo: a list of two-qubit gate fidelity, indices aligned withconnection
If name starts with "default_", a hard-coded device stored in olsq/devices/ would be loaded.
Other arguments can still be specified, in which case the original device properties would be replaced by the input.
# use a hard-coded device in olsq/devices/ called ourense
# which actually has the same properties as the device we constructed above
lsqc_solver.setdevice( qcdevice("default_ourense") )
Apart from the device, we need the quantum program/circuit to execute, which can be set with the setprogram method.
To be safe, always set the device first and then the program.
We recommend to use an intermediate representation (IR) of quantum programs specifically for OLSQ. Refer to a later part of this tutorial.
There are other methods of input that are somewhat supported, but may be deprecated.
- Use a string in QASM format
- Use an QASM file, e.g., one of programs used in the paper in
olsq/benchmarks/. - Use programs defined in other packages: refer to later parts of this tutorial on Cirq and Qiskit.
circuit_str = "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[3];\nh q[2];\n" \
"cx q[1], q[2];\ntdg q[2];\ncx q[0], q[2];\nt q[2];\n" \
"cx q[1], q[2];\ntdg q[2];\ncx q[0], q[2];\nt q[1];\nt q[2];\n" \
"cx q[0], q[1];\nh q[2];\nt q[0];\ntdg q[1];\ncx q[0], q[1];\n"
# input the quantum program as a QASM string
lsqc_solver.setprogram(circuit_str)
The example above is a Toffoli gate. We can also load an QASM file of it.
# load one of the QASM files from olsq/benchmarks
lsqc_solver.setprogram("toffoli", input_mode="benchmark")
# load your own QASM file
# circuit_file = open("my-qasm-file", "r").read()
lsqc_solver.setprogram(circuit_file)
# Toffoli Gate:
# ┌───┐
# q_0: ───────────────────■─────────────────────■────■───┤ T ├───■──
# │ ┌───┐ │ ┌─┴─┐┌┴───┴┐┌─┴─┐
# q_1: ───────■───────────┼─────────■───┤ T ├───┼──┤ X ├┤ TDG ├┤ X ├
# ┌───┐┌─┴─┐┌─────┐┌─┴─┐┌───┐┌─┴─┐┌┴───┴┐┌─┴─┐├───┤└┬───┬┘└───┘
# q_2: ┤ H ├┤ X ├┤ TDG ├┤ X ├┤ T ├┤ X ├┤ TDG ├┤ X ├┤ T ├─┤ H ├──────
# └───┘└───┘└─────┘└───┘└───┘└───┘└─────┘└───┘└───┘ └───┘
"""
It can be seen that in the Toffoli gate above, there are two-qubit gates on pair (q_0,q_1), (q_1,q_2), and (q_2,q_0).
However, there are no such triangles on device ourense.
This means that no matter how the qubits in the program are mapped to physical qubits, we need to insert SWAP gates.
# solve LSQC
result = lsqc_solver.solve()
The solve method can take two optional arguemnts
output_mode: can be"IR". Refer here on what would be returned in this case.output_file_name
If output_mode is default, the return is a tuple of three things:
- A string representing the output quantum program in QASM format.
If
output_file_nameis provided, then the QASM string would be written to that file. - final_mapping: from each program qubit to the corresponding physical qubit at the end of execution.
- objective_value
The result of the Toffoli example is shown below. Note that a SWAP gate, decomposed into three CX gates, has been inserted.
# a LSQC solution to the Toffoli gate on device 'ourense'
# ┌───┐ ┌───┐┌───┐ ┌───┐ ┌─┐
# q_0: ───────────────────■─────────────────────■──┤ X ├──■──┤ X ├┤ T ├─┤ H ├──────┤M├──────
# ┌───┐┌───┐┌─────┐┌─┴─┐┌───┐┌───┐┌─────┐┌─┴─┐└─┬─┘┌─┴─┐└─┬─┘└───┘ ├───┤ └╥┘┌─┐
# q_1: ┤ H ├┤ X ├┤ TDG ├┤ X ├┤ T ├┤ X ├┤ TDG ├┤ X ├──■──┤ X ├──■────■───┤ T ├───■───╫─┤M├───
# └───┘└─┬─┘└─────┘└───┘└───┘└─┬─┘└┬───┬┘└───┘ └───┘ ┌─┴─┐┌┴───┴┐┌─┴─┐ ║ └╥┘┌─┐
# q_2: ───────■─────────────────────■───┤ T ├─────────────────────┤ X ├┤ TDG ├┤ X ├─╫──╫─┤M├
# └───┘ └───┘└─────┘└───┘ ║ ║ └╥┘
# q_3: ─────────────────────────────────────────────────────────────────────────────╫──╫──╫─
# ║ ║ ║
# q_4: ─────────────────────────────────────────────────────────────────────────────╫──╫──╫─
# ║ ║ ║
# c: 5/═════════════════════════════════════════════════════════════════════════════╩══╩══╩═
# 2 0 1
We can input a networkx.Graph object representing the devie to setdevicegraph.
Note that the method name is different from setdevice.
(Such a representation is used in some components in Cirq, e.g.,device_graph on this line.)
We can input a cirq.Circuit object as program in setprogram.
from olsq.olsq_cirq import OLSQ_cirq
lsqc_solver = OLSQ_cirq("depth", "normal")
# use a cirq.Circuit object as program
lsqc_solver.setprogram(circuit)
# use a networkx.Graph object representing the device
lsqc_solver.setdevicegraph(device_graph)
# result_circuit is a cirq.Circuit object
result_circuit, final_mapping, objective_value = lsqc_solver.solve()
A backend from IBMQ can be input to the setdevice method with the second argument set to "ibm".
There are two arguments for the setprogram method of OLSQ_qiskit: if the second is "qasm", input a QASM string representing the quantum program as the first argument; if the second is none, then input a QuantumCircuit object in Qiskit as the first argument.
from qiskit import IBMQ
from olsq.olsq_qiskit import OLSQ_qiskit
lsqc_solver = OLSQ_qiskit("depth", "normal")
# use a qiskit.QuantumCircuit object as program
lsqc_solver.setprogram(circuit)
provider = IBMQ.load_account()
backend = provider.get_backend("ibmq_lima") # change to your backend of choice
# use an IBMQ backend as the device
lsqc_solver.setdevice(backend, "ibm")
# result_circuit is a qiskit.QuantumCircuit object
result_circuit, final_mapping, objective_value = lsqc_solver.solve()
The transition-based mode is enabled if chosen at the initiation of OLSQ.
Roughly speaking, we only use a kind of coarse-grain time in this mode, so the runtime is much shorter.
For theoretical details, please refer to the paper.
The returned QASM string and final_mapping should be similar to what they were before.
Only if the objective is "depth", the objective value would be very different from the normal mode.
There is only one SWAP inserted, so there are only two coarse-grain time steps, separated by the SWAP, whereas there are 14 time steps if using exact time.
OLSQ IR contains three things:
count_program_qubit: the number of qubits in the program.gates: a list of tuples representing qubit(s) acted on by a gate, each tuple has one index if it is a single-qubit gate, two indices if it is a two-qubit gate.gate_spec: list of type/name of each gate, which is not important to OLSQ, and only needed when generating output.
# For the following circuit
# q_0: ───────────────────■───
# │
# q_1: ───────■───────────┼───
# ┌───┐┌─┴─┐┌─────┐┌─┴─┐
# q_2: ┤ H ├┤ X ├┤ TDG ├┤ X ├─
# └───┘└───┘└─────┘└───┘
# count_program_qubit = 3
# gates = ((2,), (1,2), (2,), (0,1))
# gate_spec = ("h", "cx", "tdg", "cx")
If in the solve method, output_mode is set to "IR", the return is a tuple of five things
result_depth: depth of the resulting quantum programlist_scheduled_gate_name: similar togate_specin the IRlist_scheduled_gate_qubits: similar togatesin the IRfinal_mappingobjective_value
@InProceedings{iccad20-tan-cong-optimal-layout-synthesis,
author = {Tan, Bochen and Cong, Jason},
booktitle = {Proceedings of the 39th International Conference on Computer-Aided Design},
title = {Optimal Layout Synthesis for Quantum Computing},
year = {2020},
address = {New York, NY, USA},
publisher = {Association for Computing Machinery},
series = {ICCAD '20},
archiveprefix = {arXiv},
eprint = {2007.15671},
primaryclass = {quant-ph},
articleno = {137},
doi = {10.1145/3400302.3415620},
isbn = {9781450380263},
keywords = {quantum computing, scheduling, allocation, mapping, placement, layout synthesis},
location = {Virtual Event, USA},
numpages = {9},
url = {https://doi.org/10.1145/3400302.3415620},
}