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Author: schootward

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README.md

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+# Q-score evaluation on D-Wave solvers
+
+This repository contains python code to run the Q-score benchmark on five different D-Wave devices and solvers, namely its Advantage and 2000-Q QPU solvers, its Simulated Annealing and qbsolv classical solver and its hybrid solver. For an introduction to the Q-score, see below. The code allows running a single Max-Cut instance on each of the five solvers with different sizes and timeout limits. The code returns both the max cut result as the corresponding beta value. To compute the Q-score, one runs for increasing graph size sufficiently many instances of below code to check whether the average beta is larger than 0.2.
+
+This code was used to obtain results for the paper: "Evaluating the Q-score of quantum annealers", Ward van der Schoot et al. (IEEE QSW 2022).
+
+## Set up D-Wave configuration
+To use this code, we assume that users have created a D-Wave Leap account and configured access to D-Wave's solvers correctly. To make an account, please visit https://cloud.dwavesys.com/leap/login/?next=/leap/ and to configure access to the solvers correctly, please visit https://docs.ocean.dwavesys.com/en/stable/overview/sapi.html.
+
+## Usage
+The Q-score evaluation can be ran as follows:
+
+```python
+#Run a Max-Cut problem instance of size 10 on the Advantage QPU solver of D-Wave with a time limit of 60 seconds, returning 100 reads.
+python evaluation.py -s 10 -t 60 -n 100 -solver "Advantage_system4.1"
+```
+
+#### Q-score introduction
+
+Atos introduced in December 2020 a new quantum metric, called Q-score, that is supposed to be a universal quantum metric applicable to all programmable quantum processors. The idea behind Q-score is to determine how well a quantum system the Max-Cut problem, a real-life combinatorial problem, can solve. The Q-score is determined by the maximum number of variables within such a problem that the quantum system can optimize for, see [this paper](https://arxiv.org/abs/2102.12973) for a more elaborate description. 
+
+The official announcement of Atos can be found [here](https://atos.net/en/2020/press-release_2020_12_04/atos-announces-q-score-the-only-universal-metrics-to-assess-quantum-performance-and-superiority) and the project with tools to calculate Q-score benchmarks on gate-based devices can be found in this [gitlab project](https://github.com/myQLM/qscore).

evaluate.py

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+"""
+Run one Max-Cut instance on one of the five solvers.
+"""
+import argparse
+from collections import defaultdict
+
+import networkx as nx
+import numpy as np
+
+from run.run_hybrid import run_hybrid
+from run.run_qbsolv import run_qbsolv
+from run.run_qpu import run_qpu
+from run.run_SA import run_SA
+
+
+def parse_args() -> argparse.Namespace:
+    """
+    Parser function.
+    """
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "-s",
+        "--size",
+        help="Problem size",
+        type=int,
+        required=True,
+    )
+    parser.add_argument(
+        "-seed",
+        "--seed",
+        help="Random seed",
+        type=int,
+        required=False,
+    )
+    parser.add_argument(
+        "-t",
+        "--timeout",
+        help="Solver timeout",
+        type=int,
+        required=True,
+    )
+    parser.add_argument(
+        "-n",
+        "--num_reads",
+        help="Number of qpu reads in case of a QPU or Simulated Annealing solver",
+        type=int,
+        required=False,
+    )
+    parser.add_argument(
+        "-solver",
+        "--solver",
+        help="String of the D-Wave solver.",
+        choices=[
+            "Advantage_system4.1",
+            "DW_2000Q_6",
+            "hybrid",
+            "qbsolv",
+            "Simulated_Annealing",
+        ],
+        type=str,
+        required=True,
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def create_qubo(size: int, seed=None) -> defaultdict(int):
+    """
+    Create a QUBO formulation of a random Max-Cut instance of an Erdös-Renyí graph.
+
+    :param size: Problem instance size.
+    :param seed: Random seed.
+
+    :return: QUBO of a random Max-Cut instance given size.
+    """
+    if seed is None:
+        seed = np.random.randint(100000)
+    G = nx.erdos_renyi_graph(size, 1 / 2, seed=seed)
+
+    # Initialize our Q matrix
+    Q = defaultdict(int)
+    for i, j in G.edges:
+        Q[(i, i)] += -1
+        Q[(j, j)] += -1
+        Q[(i, j)] += 2
+
+    return Q
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    size = args.size
+    seed = args.seed
+    timeout = args.timeout
+    num_reads = args.num_reads
+    solver = args.solver
+    if num_reads is None and solver in [
+        "Advantage_system4.1",
+        "DW_2000Q_6",
+        "Simulated_Annealing",
+    ]:
+        raise ValueError("num_reads has not been submitted while required by solver.")
+    else:
+        # Create qubo:
+        Q = create_qubo(size, seed)
+
+        # Solve Max cut instance
+        if solver in ["Advantage_system4.1", "DW_2000Q_6"]:
+            max_cut_result, beta = run_qpu(Q, timeout, size, solver, num_reads)
+        elif solver == "hybrid":
+            max_cut_result, beta = run_hybrid(Q, timeout, size)
+        elif solver == "Simulated_Annealing":
+            max_cut_result, beta = run_SA(Q, timeout, size, num_reads)
+        elif solver == "qbsolv":
+            max_cut_result, beta = run_qbsolv(Q, timeout, size)
+        else:
+            raise NotImplementedError(f"Provided Solver {solver} is not implemented")
+
+    print(f"Max cut result: {max_cut_result}")
+    print(f"Beta: {beta}")

requirements.txt

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+numpy==1.22.1
+networkx==2.5
+dwave-ocean-sdk==4.4.0

run/run_SA.py

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+"""
+Run a Max-Cut instance on the D-Wave Simulated Annealing solver.
+"""
+
+import time
+from collections import defaultdict
+from functools import partial
+from typing import Tuple
+
+import neal
+import numpy as np
+from dwave.embedding.chain_strength import uniform_torque_compensation
+
+
+def run_SA(
+    Q: defaultdict(int), timeout: int, size: int, num_reads: int
+) -> Tuple[float]:
+    """
+    Function that solves a Max-Cut instance on the D-Wave Simulated Annealing solvers
+
+    :param Q: QUBO-formulation of Max-Cut instance.
+    :param timeout: timeout parameter.
+    :param size: Problem size.
+    :param num_reads: Number of states to be read from solver.
+
+    :return: The largest found cut and the corresponding value of beta.
+    """
+    start = time.time()
+
+    sampler = neal.SimulatedAnnealingSampler()
+    chain_strength = partial(uniform_torque_compensation, prefactor=2)
+    sampleset = sampler.sample_qubo(
+        Q,
+        chain_strength=chain_strength,
+        num_reads=num_reads,
+        label=f"Maximum Cut {size:2d}",
+    )
+
+    max_cut_result = -sampleset.first.energy
+    random_score = size ** 2 / 8
+    beta = (max_cut_result - random_score) / (0.178 * pow(size, 3 / 2))
+
+    time_taken = time.time() - start
+    if time_taken > timeout:
+        print("failed to find a cut within timeout limit")
+        max_cut_result = np.nan
+        beta = 0
+    return max_cut_result, beta