IBMBackendValueError: number of circuits exceeds the maximum for this backend, 300

Yang · March 26, 2024, 12:50pm

Hi, I am trying to run the QAOA on the IBMQ device. The QAOA circuit consists of 6 qubits, two layers. The number of total trainable parameters for the QAOA circuit is 14. The toy code is as below:

dev = qml.device('qiskit.ibmq', wires=args.dataset.n_node, backend='ibm_kyoto', ibmqx_token=token)
circuit = QAOA(dev)
opt_b = qml.AdamOptimizer(args.lr_qaoa)
param_b, loss = opt_b.step_and_cost(lambda param_b: circuit(param_b, circuit.param_c), circuit.param_b)

After running the code, it successfully run on the IBMQ backend in the beginning by checking the IBMQ dashboard of my account. However, it soon raised the error qiskit_ibm_provider.exceptions.IBMBackendValueError: Number of circuits, 418 exceeds the maximum for this backend, 300).
The detailed pennylane version information is listed in the following:

Name: PennyLane
Version: 0.35.1
Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Home-page: https://github.com/PennyLaneAI/pennylane
Author:
Author-email:
License: Apache License 2.0
Location: /home/yqia7342/qml/lib/python3.10/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-qiskit, PennyLane_Lightning

Platform info:           Linux-5.15.0-58-generic-x86_64-with-glibc2.35
Python version:          3.10.12
Numpy version:           1.26.4
Scipy version:           1.12.0
Installed devices:
- default.clifford (PennyLane-0.35.1)
- default.gaussian (PennyLane-0.35.1)
- default.mixed (PennyLane-0.35.1)
- default.qubit (PennyLane-0.35.1)
- default.qubit.autograd (PennyLane-0.35.1)
- default.qubit.jax (PennyLane-0.35.1)
- default.qubit.legacy (PennyLane-0.35.1)
- default.qubit.tf (PennyLane-0.35.1)
- default.qubit.torch (PennyLane-0.35.1)
- default.qutrit (PennyLane-0.35.1)
- null.qubit (PennyLane-0.35.1)
- lightning.qubit (PennyLane_Lightning-0.35.1)
- qiskit.aer (PennyLane-qiskit-0.35.1)
- qiskit.basicaer (PennyLane-qiskit-0.35.1)
- qiskit.ibmq (PennyLane-qiskit-0.35.1)
- qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.35.1)
- qiskit.ibmq.sampler (PennyLane-qiskit-0.35.1)
- qiskit.remote (PennyLane-qiskit-0.35.1)

Could you please provide some advice to solve this issue? Thanks !

isaacdevlugt · March 27, 2024, 2:49pm

Hey @Yang,

Based on the error you’re getting, tt seems like you’re asking too much from the backend. Are you able to accomplish what you want to accomplish with a less demanding calculation?

Yang · March 28, 2024, 4:30am

Hi @isaacdevlugt ,

Thank you for the advice. I cannot recognize which part of the code requires a larger number of circuits and how to control the demanding resource. Could you please explain it?

isaacdevlugt · March 28, 2024, 5:42pm

Can you attach the code that you’re running? It’s hard to tell why this is happening without it

Yang · March 30, 2024, 7:48am

Thanks for your reply. The following is the simplified version of my code.

def encode_layer(nr_qubits):
    for i in range(nr_qubits):
        qml.Hadamard(wires=i)

def U_C(gamma, weights=None, edges=None):
    for i, edge in enumerate(edges):
        qml.CNOT(wires=[int(edge[0]), int(edge[1])])
        qml.RZ(gamma*weights[i], wires=int(edge[1]))
        qml.CNOT(wires=[int(edge[0]), int(edge[1])])

def U_B(beta, n_qubits=None):
    for i in range(n_qubits):
        qml.RX(beta[i], wires=i)

def circuit(param_b, param_c, edges, weights, n_qubits, n_layer):
    encode_layer(n_qubits)
    for j in range(n_layers):
        U_C(param_c[j], weights, edges)
        U_B(param_b[j], n_qubits)
    return qml.expval(A)

n_node = 6
n_layer =2
degree = 3
edges = nx.random_regular_graph(degree, n_node)
edges = list(edges..edges().keys())
weights = np.random.uniform(0, 1, len(edges)).tolist()
dev = qml.device('qiskit.ibmq', wires=n_node, backend='ibm_kyoto')
circuit_dev = qml.qnode(dev)(circuit)

lr = 0.15
opt_b = qml.AdamOptimizer(lr)
param_b = np.random.uniform(0, np.pi * 2, (n_layer, n_node))
param_c = np.random.uniform(0, np.pi * 2, (n_layer))
param_b, loss = opt_b.step_and_cost(lambda param: circuit_dev(param, param_c, edges, weights, n_node, n_layer), param_b)

isaacdevlugt · April 2, 2024, 7:04pm

Interesting… I wasn’t able to replicate the behaviour with this code:

import pennylane as qml
import pennylane.numpy as np
import networkx as nx

n_node = 6
n_layer =2
degree = 3
edges = nx.random_regular_graph(degree, n_node)
edges = list(edges.edges().keys())
weights = np.random.uniform(0, 1, len(edges)).tolist()

def encode_layer(nr_qubits):
    for i in range(nr_qubits):
        qml.Hadamard(wires=i)

def U_C(gamma, weights=None, edges=None):
    for i, edge in enumerate(edges):
        qml.CNOT(wires=[int(edge[0]), int(edge[1])])
        qml.RZ(gamma*weights[i], wires=int(edge[1]))
        qml.CNOT(wires=[int(edge[0]), int(edge[1])])

def U_B(beta, n_qubits=None):
    for i in range(n_qubits):
        qml.RX(beta[i], wires=i)

def circuit(param_b, param_c, edges, weights, n_qubits, n_layer):
    encode_layer(n_qubits)
    for j in range(n_layer):
        U_C(param_c[j], weights, edges)
        U_B(param_b[j], n_qubits)
    return qml.expval(qml.Z(0))

dev = qml.device('qiskit.ibmq', wires=n_node, backend='ibm_kyoto', ibmqx_token="~~~~")
circuit_dev = qml.qnode(dev)(circuit)

with qml.Tracker(dev) as tracker:

    print(circuit_dev.get_best_method(dev, interface="numpy"))
    lr = 0.15
    opt_b = qml.AdamOptimizer(lr)
    param_b = np.random.uniform(0, np.pi * 2, (n_layer, n_node))
    param_c = np.random.uniform(0, np.pi * 2, (n_layer))
    param_b, loss = opt_b.step_and_cost(lambda param: circuit_dev(param, param_c, edges, weights, n_node, n_layer), param_b)

print(tracker.latest)

(<transform: param_shift>, {}, <IBMQDevice device (wires=6, shots=1024) at 0x3109f6290>)
...
{'job_time': {'queued': 0.886248, 'running': 35.419}}

I tried using our tracker (qml.Tracker — PennyLane 0.35.1 documentation) to see what was going on, but everything seems fine! Let me know if you’re able to replicate this. For reference:

Name: PennyLane
Version: 0.35.1
Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Home-page: https://github.com/PennyLaneAI/pennylane
Author: 
Author-email: 
License: Apache License 2.0
Location: /Users/isaac/.virtualenvs/pennylane-qiskit/lib/python3.11/site-packages
Requires: appdirs, autograd, autoray, cachetools, networkx, numpy, pennylane-lightning, requests, rustworkx, scipy, semantic-version, toml, typing-extensions
Required-by: PennyLane-qiskit, PennyLane_Lightning

Platform info:           macOS-14.4.1-arm64-arm-64bit
Python version:          3.11.8
Numpy version:           1.26.4
Scipy version:           1.12.0
Installed devices:
- default.clifford (PennyLane-0.35.1)
- default.gaussian (PennyLane-0.35.1)
- default.mixed (PennyLane-0.35.1)
- default.qubit (PennyLane-0.35.1)
- default.qubit.autograd (PennyLane-0.35.1)
- default.qubit.jax (PennyLane-0.35.1)
- default.qubit.legacy (PennyLane-0.35.1)
- default.qubit.tf (PennyLane-0.35.1)
- default.qubit.torch (PennyLane-0.35.1)
- default.qutrit (PennyLane-0.35.1)
- null.qubit (PennyLane-0.35.1)
- lightning.qubit (PennyLane_Lightning-0.35.1)
- qiskit.aer (PennyLane-qiskit-0.35.1)
- qiskit.basicaer (PennyLane-qiskit-0.35.1)
- qiskit.ibmq (PennyLane-qiskit-0.35.1)
- qiskit.ibmq.circuit_runner (PennyLane-qiskit-0.35.1)
- qiskit.ibmq.sampler (PennyLane-qiskit-0.35.1)
- qiskit.remote (PennyLane-qiskit-0.35.1)

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IBMBackendValueError: number of circuits exceeds the maximum for this backend, 300

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