quantastica-qps-api 0.9.9

Quantastica Quantum Programming Studio API

Quantum Programming Studio API

Python wrapper for Quantum Programming Studio HTTP API.

Quick start

1. Install QPS API package:

pip install quantastica-qps-api

2. Find your QPS API token:

Login to Quantum Programming Studio, go to Profile -> API Access and copy your API token.

3. Configure QPS API package with your API token:

from quantastica.qps_api import QPS

QPS.save_account("YOUR_API_TOKEN")

That will create a local configuration file where your API token will be stored for future use.

Now you are ready to use QPS API.

Quick start example: find circuit from initial/final vector pairs using Quantum Algorithm Generator

from quantastica.qps_api import QPS

vector_pairs = [
[ [1, 0, 0, 0], [ 0.5+0j,  0.5+0j,  0.5+0j,  0.5+0j ] ],
[ [0, 1, 0, 0], [ 0.5+0j,  0+0.5j, -0.5+0j,  0-0.5j ] ],
[ [0, 0, 1, 0], [ 0.5+0j, -0.5+0j,  0.5+0j, -0.5+0j ] ],
[ [0, 0, 0, 1], [ 0.5+0j,  0-0.5j, -0.5+0j,  0+0.5j ] ]
]

job_id = QPS.generator.circuit_from_vectors(vector_pairs, settings = { "instruction_set": ["h", "cu1", "swap"] })

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Account management

QPS.save_account(api_token, api_url=None)

Run this once to setup your QPS REST API account. Method will create configuration file and your api token will be stored there for future use.

If needed, you can clear your token by running QPS.save_account("") (or by deleting a configuration file).

If api_url is not provided then https://quantum-circuit.com/api/ will be set as default.

QPS.config_path()

You can get config file path by running QPS.config_path().

Default configuration file path:

• On Unix, directory is obtained from environment variable HOME if it is set; otherwise the current user’s home directory is looked up in the password directory through the built-in module pwd.

• On Windows, USERPROFILE will be used if set, otherwise a combination of HOMEPATH and HOMEDRIVE will be used.

Quantum Algorithm Generator API

Quantum Algorithm Generator is a tool based on machine learning which reverse engineers quantum circuits from state vectors (wave functions). Additionally, it can be used to find quantum algorithm for boolean function from truth table, to transpile circuits and to decompose unitary matrices.

Generator job management

Problem sent to generator is called a "job". Each job has unique ID. As generator is resource intensive tool, it is configured to execute only one job at a time. While generator is solving a job, other jobs are queued. When generator finishes executing a job, it takes the next one from the queue.

API provides functions for job manipulation: you can list all jobs (filtered by status), stop running job, cancel queued jobs, stop/cancel all jobs, start previously canceled (draft) job, etc.

QPS.generator.list_jobs(status_filter=None)

List all jobs, optionally filtered by status.

• status_filter String, optional. Can be: draft, queued, running, error, done.

Example 1 - list all (unfiltered) generator jobs:

from quantastica.qps_api import QPS

jobs = QPS.generator.list_jobs()

print(jobs)

Example output:

{
	"list": [
		{ "_id": "r9LskFoLPQW5w7HTp", "name": "Bell state", "type": "vectors", "status": "done" },
		{ "_id": "R8tJH7XoZ233oTREy", "name": "4Q Gauss", "type": "vectors", "status": "queued" },
		{ "_id": "h7fzYbFz8MJvkNhiX", "name": "Challenge", "type": "unitary", "status": "draft" },
		{ "_id": "PC5PNXiGqhh2HmkX8", "name": "Experiment", "type": "vectors", "status": "error"},
		{ "_id": "SNhiCqSCT2WwRWKCd", "name": "Decompose", "type": "unitary", "status": "running" }
	]
}

Example 2 - list running jobs:

from quantastica.qps_api import QPS

jobs = QPS.generator.list_jobs(status_filter="running")

print(jobs)

Example output:

{
	"list": [
		{ "_id": "SNhiCqSCT2WwRWKCd", "name": "Decompose", "type": "unitary", "status": "running" }
	]
}

QPS.generator.job_status(job_id)

Get job status.

Example:

from quantastica.qps_api import QPS

status = QPS.generator.job_status("PC5PNXiGqhh2HmkX8")

print(status)

Example output:

{ "_id": "PC5PNXiGqhh2HmkX8", "name": "Experiment", "type": "vectors", "status": "error", "message": "connect ECONNREFUSED" }

QPS.generator.get_job(job_id, wait=True)

Get generator job referenced by ID. If wait argument is True (default), then function will wait for a job to finish (or fail) before returning. If wait is False, then job will be immediatelly returned even if it is still running (in which case it will not contain a solution).

Example:

from quantastica.qps_api import QPS

job = QPS.generator.get_job("r9LskFoLPQW5w7HTp")

print(job)

Example output:

{
	"_id": "r9LskFoLPQW5w7HTp",
	"name": "Bell",
	"type": "vectors",
	"source": {
		"vectors": {
			"text1": "[ 1, 0, 0, 0 ]",
			"text2": "[ 1/sqrt(2), 0, 0, 1/sqrt(2) ]",
			"endianness1": "little",
			"endianness2": "little"
		}
	},
	"problem": [
		{
			"input": [ 1, 0, 0, 0 ],
			"output": [ 0.7071067811865475, 0, 0, 0.7071067811865475 ]
		}
	],
	"settings": {
		"max_diff": 0.001,
		"diff_method": "distance",
		"single_solution": False,
		"pre_processing": "",
		"allowed_gates": "u3,cx",
		"coupling_map": [],
		"min_gates": 0,
		"max_gates": 0
	},
	"status": "done",
	"output": {
		"circuits": [
			{
				"qubits": 2,
				"cregs": [],
				"diff": 0,
				"program": [
					{
						"name": "u3",
						"wires": [ 0 ],
						"options": {
							"params": {
								"theta": -1.570796370506287,
								"phi": -3.141592741012573,
								"lambda": -5.327113628387451
							}
						}
					},
					{
						"name": "cx",
						"wires": [ 0, 1 ],
						"options": {}
					}
				],
				"index": 0,
				"qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[0];\ncx q[0], q[1];\n",
				"qasmExt": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[0];\ncx q[0], q[1];\n"
			},
			{
				"qubits": 2,
				"cregs": [],
				"diff": 0,
				"program": [
					{
						"name": "u3",
						"wires": [ 1 ],
						"options": {
							"params": {
								"theta": -1.570796370506287,
								"phi": -3.141592741012573,
								"lambda": -5.327113628387451
							}
						}
					},
					{
						"name": "cx",
						"wires": [ 1, 0 ],
						"options": {}
					}
				],
				"index": 1,
				"qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[1];\ncx q[1], q[0];\n",
				"qasmExt": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[1];\ncx q[1], q[0];\n"				
			}
		],
		"error_code": 0,
		"message": "",
		"time_taken": 0.002,
		"version": "0.1.0"
	},
	"queuedAt": "2021-02-06T23:39:29.676Z",
	"startedAt": "2021-02-06T23:39:29.926Z",
	"finishedAt": "2021-02-06T23:39:30.383Z"
}

QPS.generator.stop_job(job_id)

Stop running or cancel queued job. Job will be put into draft state, and you can start it again later by calling start_job().

Example:

from quantastica.qps_api import QPS

response = QPS.generator.stop_job("SNhiCqSCT2WwRWKCd")

print(response)

Example output:

{ _id: "SNhiCqSCT2WwRWKCd", message: "OK" }

QPS.generator.stop_all_jobs(status_filter=None)

Stop running job / cancel all queued jobs.

• status_filter - you can stop only a running job by providing status_filter="running" (after this, next job from the queue will be executed). Or, you can cancel all queued jobs by providing status_filter="queued" (running job will not be affected - it will continue running).

Example 1 - stop running job and remove all jobs from queue:

from quantastica.qps_api import QPS

stopped = QPS.generator.stop_all_jobs()

print(stopped)

Example output:

{
	"stopped": [ 
		{ "_id": "SNhiCqSCT2WwRWKCd", "name": "Decompose", "type": "unitary" },
		{ "_id": "R8tJH7XoZ233oTREy", "name": "4Q Gauss", "type": "vectors" }
	]
}

Example 2 - stop only a running job. Next job from queue, if any, will start:

from quantastica.qps_api import QPS

stopped = QPS.generator.stop_all_jobs(status_filter="running")

print(stopped)

Example output:

{
	"stopped": [
		{ "_id": "SNhiCqSCT2WwRWKCd", "name": "Decompose", "type": "unitary" }
	]
}

Example 3 - cancel all queued jobs. Running job will not be affected:

from quantastica.qps_api import QPS

stopped = QPS.generator.stop_all_jobs(status_filter="queued")

print(stopped)

Example output:

{
	"stopped": [
		{ "_id": "R8tJH7XoZ233oTREy", "name": "4Q Gauss", "type": "vectors" }
	]
}

QPS.generator.start_job(job_id)

Start previously stopped/canceled job (can be any job with status draft).

Example:

from quantastica.qps_api import QPS

response = QPS.generator.start_job("SNhiCqSCT2WwRWKCd")

print(response)

Example output:

{ _id: "SNhiCqSCT2WwRWKCd", message: "OK" }

Circuit from vectors

Find quantum circuit from pairs of initial & final state vectors (wave functions).

QPS.generator.circuit_from_vectors(vector_pairs, endianness = "little", job_name=None, settings = {}, start_job=True)

• vector_pairs is list containing vector pairs. Each vector pair is list with 2 elements: initial vector and final vector. All vectors in all pairs must be of same length (same number of qubits).

• endianness string. Orientation of bits in state vector (most significant bit/first qubit or least significant bit/first qubit). Can be little (like Qiskit) or big. Default is little.

• job_name string is optional. You can give it a human readable name.

• settings object is optional. Default is:

{
	"allowed_gates": "u3,cx",
	"max_diff": 1e-3,
	"diff_method": "distance",
	"single_solution": True,
	"pre_processing": ""
}

Note: if settings argument is provided, it will overwrite default values, but only provided keys will be overwritten - not entire default settings object.

• start_job if this argument is True (default) the job will be immediatelly sent to execution queue. If start_job is False then it will stay in draft state and you will be able to start it later by calling start_job() method.

Example:

from quantastica.qps_api import QPS

vector_pairs = [
[ [1, 0, 0, 0], [ 0.5+0j,  0.5+0j,  0.5+0j,  0.5+0j ] ],
[ [0, 1, 0, 0], [ 0.5+0j,  0+0.5j, -0.5+0j,  0-0.5j ] ],
[ [0, 0, 1, 0], [ 0.5+0j, -0.5+0j,  0.5+0j, -0.5+0j ] ],
[ [0, 0, 0, 1], [ 0.5+0j,  0-0.5j, -0.5+0j,  0+0.5j ] ]
]

job_id = QPS.generator.circuit_from_vectors(vector_pairs, settings = { "instruction_set": ["h", "cu1", "swap"], "single_solution": False })

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
swap q[0], q[1];
cu1 (2.356194496154785) q[0], q[1];
h q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
cu1 (2.356194496154785) q[0], q[1];
h q[0];
swap q[0], q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
cu1 (2.356194496154785) q[0], q[1];
swap q[0], q[1];
h q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
swap q[0], q[1];
h q[0];
cu1 (2.356194496154785) q[0], q[1];
h q[1];

State preparation

Get circuit which will transform ground state (all qubits reset) to desired final state vector.

QPS.generator.state_preparation(final_vector, endianness = "little", job_name=None, settings = {}, start_job=True)

• final_vector is target vector.

• endianness string. Orientation of bits in state vector (most significant bit/first qubit or least significant bit/first qubit). Can be little (like Qiskit) or big. Default is little.

• job_name string is optional. You can give it a human readable name.

• settings object is optional. Default: see QPS.generator.circuit_from_vectors().

• start_job if this argument is True (default) the job will be immediatelly sent to execution queue. If start_job is False then it will stay in draft state and you will be able to start it later by calling start_job() method.

Example:

from quantastica.qps_api import QPS

desired_state = [0.5, 0.5, 0.5, 0.5]

job_id = QPS.generator.state_preparation(desired_state, settings = { "instruction_set": ["u3", "cx"] })

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
u3 (1.570796370506287, 0, 1.217840194702148) q[0];
u3 (1.570796370506287, 0, 0.621559917926788) q[1];

Transpile

Transpile circuit (change instruction set).

QPS.generator.transpile(input_qasm, method="replace_blocks", method_options={}, job_name=None, settings = {}, start_job=True)

• input_qasm is string containing OpenQASM 2.0 code.

• method is method name string. Can be one of: "replace_circuit", "replace_blocks", "replace_gates". Default: "replace_blocks".

• method_options dict with following structure:

  • If method is replace_blocks then: { "block_size": 2, "two_pass": False } (maximum block size is 4).

  • For other methods: no options (method_options is ignored)

• job_name string is optional. You can give it a human readable name.

• settings object is optional. Default is:

{
	"allowed_gates": "u3,cx",
	"max_diff": 1e-3,
	"diff_method": "distance",
	"single_solution": True,
	"pre_processing": "experimental1"
}

Default diff_method is distance, which means that input and output circuit's global phase will match. That also means longer running time and possibly deeper output circuit (especially with new IBM's instruction set id, x, sx, rz, cx). If you are relaxed about global phase (like Qiskit's transpile method), then provide "diff_method": "ignorephase" in settings.

Note: if settings argument is provided, it will overwrite default values, but only provided keys will be overwritten - not entire default settings object.

• start_job if this argument is True (default) the job will be immediatelly sent to execution queue. If start_job is False then it will stay in draft state and you will be able to start it later by calling start_job() method.

Example:

from quantastica.qps_api import QPS

input_qasm = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[0];
cx q[0], q[1];
"""

job_id = QPS.generator.transpile(input_qasm, settings = { "instruction_set": ["id", "x", "sx", "rz", "cx"], "diff_method": "ignorephase" })

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
sx q[0];
rz (1.570796370506287) q[0];
sx q[0];
cx q[0], q[1];

Decompose matrix

Decompose unitary matrix (find circuit from matrix).

QPS.generator.decompose_unitary(unitary, endianness = "big", job_name=None, settings = {}, start_job=True)

• unitary matrix operator.

• endianness - orientation of the matrix. Can be little endian (like Qiskit) or big endian. Default is big. Note that default endianness of the matrix differs from default endianness of vectors in other methods. That's to be aligned with QPS. In Qiskit, both matrices and vectors are little endian. So, if you are solving unitary from Qiskit then provide endianness = "little" argument.

• job_name string is optional. You can give it a human readable name.

• settings object is optional. Default is:

{
	"allowed_gates": "u3,cx",
	"max_diff": 1e-3,
	"diff_method": "distance",
	"single_solution": True,
	"pre_processing": ""
}

Note: if settings argument is provided, it will overwrite default values, but only provided keys will be overwritten - not entire default settings object.

• start_job if this argument is True (default) the job will be immediatelly sent to execution queue. If start_job is False then it will stay in draft state and you will be able to start it later by calling start_job() method.

Example:

from quantastica.qps_api import QPS

unitary = [
[ 0.5+0.0j,  0.5+0.0j,  0.5+0.0j,  0.5+0.0j],
[ 0.5+0.0j,  0.5+0.0j, -0.5+0.0j, -0.5+0.0j],
[ 0.5+0.0j, -0.5+0.0j,  0.0+0.5j,  0.0-0.5j],
[ 0.5+0.0j, -0.5+0.0j,  0.0-0.5j,  0.0+0.5j]
]

job_id = QPS.generator.decompose_unitary(unitary, settings = { "instruction_set": ["h", "cu1", "swap"], "single_solution": False })

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
swap q[0], q[1];
cu1 (1.570796370506287) q[0], q[1];
h q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
cu1 (1.570796370506287) q[0], q[1];
h q[0];
swap q[0], q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
h q[1];
cu1 (1.570796370506287) q[0], q[1];
swap q[0], q[1];
h q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
swap q[0], q[1];
h q[0];
cu1 (1.570796370506287) q[0], q[1];
h q[1];

Create algorithm from truth table

Create circuit which implements logical expression whose truth table is given.

QPS.generator.circuit_from_truth_table(truth_table_csv, column_defs, csv_delimiter=None, additional_qubits=1, job_name=None, settings={}, start_job=True)

• truth_table_csv is string containing truth table in CSV format

• column_defs list of strings describing each column from truth table: "input", "output" or "ignore"

• csv_delimiter CSV column delimiter char: None, "," (comma) or "\t" (tab). If delimiter is None (default) it will be automatically detected.

• additional_qubits number of qubits to add (to displace input and output qubits).

• job_name string is optional. You can give it a human readable name.

• settings object is optional. Default is:

{
	"allowed_gates": "x,cx,ccx,swap",
	"max_diff": 1e-3,
	"diff_method": "distance",
	"single_solution": True,
	"pre_processing": ""
}

Example:

from quantastica.qps_api import QPS

truth_table = """
A,B,A_NAND_B
0,0,1
0,1,1
1,0,1
1,1,0
"""

job_id = QPS.generator.circuit_from_truth_table(truth_table, ["input", "input", "output"])

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	raise Exception(job_output["message"])
else:
	if(len(job_output["circuits"]) == 0):
		raise Exception("No results.")
	else:
		for circuit in job_output["circuits"]:
			print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
x q[2];
ccx q[0], q[1], q[2];

Run problem file

Solve problem provided in internal format used by generator.

QPS.generator.solve(problem, settings = {}, start_job=True)

• problem object - generator job exported to json from QPS.

• settings argument is optional. If provided, it will overwrite keys in problem.settings. Note that only provided keys will be overwritten - not entire problem.settings object.

• start_job if this argument is True (default) the job will be immediatelly sent to execution queue. If start_job is False then it will stay in draft state and you will be able to start it later by calling start_job() method.

Example:

from quantastica.qps_api import QPS

problem = {
	"name": "Bell",
	"type": "vectors",
	"source": {
		"vectors": {
			"text1": "[ 1, 0, 0, 0 ]",
			"text2": "[ 1/sqrt(2), 0, 0, 1/sqrt(2) ]",
			"endianness1": "little",
			"endianness2": "little"
		}
	},
	"problem": [
		{
			"input": [
				1,
				0,
				0,
				0
			],
			"output": [
				0.7071067811865475,
				0,
				0,
				0.7071067811865475
			]
		}
	],
	"settings": {
		"allowed_gates": "u3,cx",
		"coupling_map": [],
		"min_gates": 0,
		"max_gates": 0,
		"max_diff": 0.001,
		"diff_method": "distance",
		"single_solution": False,
		"pre_processing": ""
	}
}

job_id = QPS.generator.solve(problem)

job = QPS.generator.get_job(job_id, wait=True)

job_status = job["status"]
job_output = job["output"]

if(job_status == "error"):
	print(job_output["message"])
else:
	for circuit in job_output["circuits"]:
		print(circuit["qasm"])

Example output:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
u3 (-1.570796370506287, -3.141592741012573, -2.675650835037231) q[0];
cx q[0], q[1];

OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
u3 (-1.570796370506287, -3.141592741012573, -2.675650835037231) q[1];
cx q[1], q[0];

Generator output format

Generator job object has following structure:

{
	"name"       : String,
	"type"       : String,
	"source"     : Object,
	"problem"    : Array,
	"settings"   : Object,
	"status"     : String,
	"output"     : Object,
	"queuedAt"   : String,
	"startedAt"  : String,
	"finishedAt" : String
}

Keys important to user are:

• name String: name of the job

• status String: can be draft, queued, running, error, done.

• output Object with following structure:

{
	"error_code" : Integer,
	"message"    : String,
	"time_taken" : Float,
	"version"    : String,
	"circuits"   : Array of Object
}

• error_code Integer: 0 on success, non-zero on error

• message String: error message if error code is non-zero

• time_taken Float: number of seconds

• version String: generator version

• circuits Array: resulting circuits. Each is object with following structure:

{
	"qubits"  : Integer,
	"cregs"   : Array,
	"program" : Array,
	"diff"    : Float,
	"index"   : Integer,
	"qasm"    : String,
	"qasmExt" : String
}

Keys important to user are:

• qasm OpenQASM 2.0 source code of the resulting circuit.

• qasmExt OpenQASM 2.0 with extended instruction set (all gates supported by Quantum Programming Studio).

Difference between qasm and qasmExt: if circuit contains gate supported by QPS but not directly supported by OpenQASM 2.0 then qasm will contain equivalent circuit transpiled to OpenQASM 2.0 instruction set, but qasmExt will contain gates as is.

For example if circuit contains IONQ native gate gpi2(2.51678906856393) on first qubit:

qasm will be:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[1];
u3 (1.5707963267948966, 0.9459927417690333, -0.9459927417690333) q[0];

qasmExt will contain:

OPENQASM 2.0;
include "qelib1.inc";
qreg q[1];
gpi2 (2.51678906856393) q[0];

Example job object with output:

{
	"_id": "r9LskFoLPQW5w7HTp",
	"name": "Bell",
	"type": "vectors",
	"source": {
		"vectors": {
			"text1": "[ 1, 0, 0, 0 ]",
			"text2": "[ 1/sqrt(2), 0, 0, 1/sqrt(2) ]",
			"endianness1": "little",
			"endianness2": "little"
		}
	},
	"problem": [
		{
			"input": [ 1, 0, 0, 0 ],
			"output": [ 0.7071067811865475, 0, 0, 0.7071067811865475 ]
		}
	],
	"settings": {
		"max_diff": 0.001,
		"diff_method": "distance",
		"single_solution": False,
		"pre_processing": "",
		"allowed_gates": "u3,cx",
		"coupling_map": [],
		"min_gates": 0,
		"max_gates": 0
	},
	"status": "done",
	"output": {
		"circuits": [
			{
				"qubits": 2,
				"cregs": [],
				"diff": 0,
				"program": [
					{
						"name": "u3",
						"wires": [ 0 ],
						"options": {
							"params": {
								"theta": -1.570796370506287,
								"phi": -3.141592741012573,
								"lambda": -5.327113628387451
							}
						}
					},
					{
						"name": "cx",
						"wires": [ 0, 1 ],
						"options": {}
					}
				],
				"index": 0,
				"qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[0];\ncx q[0], q[1];\n",
				"qasmExt": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[0];\ncx q[0], q[1];\n"
			},
			{
				"qubits": 2,
				"cregs": [],
				"diff": 0,
				"program": [
					{
						"name": "u3",
						"wires": [ 1 ],
						"options": {
							"params": {
								"theta": -1.570796370506287,
								"phi": -3.141592741012573,
								"lambda": -5.327113628387451
							}
						}
					},
					{
						"name": "cx",
						"wires": [ 1, 0 ],
						"options": {}
					}
				],
				"index": 1,
				"qasm": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[1];\ncx q[1], q[0];\n",
				"qasmExt": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[2];\nu3 (-1.570796370506287, -3.141592741012573, -5.327113628387451) q[1];\ncx q[1], q[0];\n"
			}
		],
		"error_code": 0,
		"message": "",
		"time_taken": 0.002,
		"version": "0.1.0"
	},
	"createdAt": "2021-02-06T23:39:29.108Z",
	"modifiedAt": "2021-02-06T23:39:30.383Z",
	"queuedAt": "2021-02-06T23:39:29.676Z",
	"startedAt": "2021-02-06T23:39:29.926Z",
	"finishedAt": "2021-02-06T23:39:30.383Z"
}

Using Generator with Qiskit

Generator is using OpenQASM 2.0 format for input and output, so integration with Qiskit (and other frameworks that support QASM) is easy.

Example transpile Qiskit circuit:

from qiskit import QuantumCircuit
from qiskit.circuit.random import random_circuit
from qiskit.quantum_info import Operator

from quantastica.qps_api import QPS

# Generate random Qiskit circuit
qc = random_circuit(5, 5, measure=False)

# Get QASM code
input_qasm = qc.qasm()

# Transpile with generator
job_id = QPS.generator.transpile(input_qasm, settings = { "instruction_set": ["id", "u3", "cx"], "diff_method": "ignorephase" })
job = QPS.generator.get_job(job_id, wait=True)
job_status = job["status"]
job_output = job["output"]
if(job_status == "error"):
    raise Exception(job_output["message"])

transpiled_circuit = job_output["circuits"][0]

# Get QASM code
transpiled_qasm = transpiled_circuit["qasm"]

# Create Qiskit circuit
transpiled_qc = QuantumCircuit.from_qasm_str(transpiled_qasm)

# Show circuit
print("Depth:", transpiled_qc.depth())
print("Ops:", sum(j for i, j in transpiled_qc.count_ops().items()))
display(transpiled_qc.draw(output="mpl"))

Quantum Language Converter API

Quantum Language Converter is a tool which converts quantum program between different quantum programming languages and frameworks. It is also available as a q-convert command line tool and as a web UI at https://quantum-circuit.com/qconvert.

QPS has integrated quantum language converter API which you can access directly from python code:

QPS.converter.convert(input, source, dest)

Converts input quantum program given as string from source format into dest format.

• input String. Program source code

• source String. Input format:

  • qasm OpenQASM 2.0 source code
  • quil Quil source code
  • qobj QObj
  • ionq IONQ (json)
  • quantum-circuit quantum-circuit object (json)
  • toaster Qubit Toaster object (json)

• dest String. Output format:

  • qiskit Qiskit
  • qasm OpenQASM 2.0
  • qasm-ext OpenQASM 2.0 with complete instruction set supported by QPS (and other Quantastica tools)
  • qobj QObj
  • quil Quil
  • pyquil pyQuil
  • braket Braket
  • cirq Cirq
  • tfq TensorFlow Quantum
  • qsharp QSharp
  • quest QuEST
  • js quantum-circuit (javascript)
  • quantum-circuit quantum-circuit (json)
  • toaster Qubit Toaster
  • svg SVG (standalone)
  • svg-inline SVG (inline)

Example 1 - convert QASM 2.0 program to QUIL:

from quantastica.qps_api import QPS

input_program = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
creg c[2];
h q[0];
cx q[0], q[1];
measure q[0] -> c[0];
measure q[1] -> c[1];
"""

output_program = QPS.converter.convert(input_program, "qasm", "quil")

print(output_program)

Output:

DECLARE ro BIT[2]
H 0
CNOT 0 1
MEASURE 0 ro[0]
MEASURE 1 ro[1]

Example 2 - convert QASM 2.0 program to circuit drawing as vector image:

from quantastica.qps_api import QPS

input_program = """
OPENQASM 2.0;
include "qelib1.inc";
qreg q[2];
creg c[2];
h q[0];
cx q[0], q[1];
measure q[0] -> c[0];
measure q[1] -> c[1];
"""

output_svg = QPS.converter.convert(input_program, "qasm", "svg")

open("output.svg", "w").write(output_svg)

Utils API

QPS.utils.random_circuit(num_qubits=5, output_format="quantum-circuit", options=None)

Returns random quantum circuit.

• num_qubits Integer. Number of qubits. Default: 5.

• format String. Output format. The same as QPS.converter.convert() function's dest argument. Example: "qasm". Default: "quantum-circuit".

• options Dict. Optional. Can contain following keys:

  • instruction_set List of gates to use. Example: ["u3", "cx"]. Default: [ "u3", "rx", "ry", "rz", "cx", "cz" ].
  • num_gates Integer. Number of gates in the circuit. Default is num_qubits * 8.
  • mid_circuit_measurement Bool. Default: False.
  • mid_circuit_reset Bool. Default: False.
  • classic_control Bool. Default: False.