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artificial intelligence and (QPUs) QPUs is a networking product designed for enterprise and data center networking.

QPU is a networking product designed for enterprise and data center networking.

NVIDIA CUDA-Q This product is a networking product designed for enterprise and data center networking.

SC24, NVIDIA CUDA-QX CUDA-Q is a networking product designed for enterprise and data center networking.

CUDA-QX is a networking product designed for AI clusters.

1, CUDA-QX

CUDA-QX:

CUDA-Q QEC： QEC is a networking product designed for enterprise and data center networking.
CUDA-Q: This product is a networking product designed for enterprise and data center networking.

PyTorch is a networking product designed for enterprise and data center networking.

CUDA-Q QEC

QPU is a networking product designed for enterprise and data center networking.

CUDA-Q is a networking product designed for enterprise and data center networking.

CUDA-Q is a networking product designed for AI clusters.

CUDA-Q QEC:

CUDA-Q is a networking product designed for enterprise and data center networking.

QEC is a networking product designed for enterprise and data center networking.

CUDA-Q is a networking product designed for enterprise and data center networking.

import numpy as np                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      

import cudaq_qec as qec 

QEC is a networking product designed for enterprise and data center networking.

steane = qec.get_code("steane")

CUDA-Q is a networking product designed for enterprise and data center networking.

Steane CSS,, or for (X) or (ZSteane is a networking product designed for enterprise and data center networking.

Hz = steane.get_parity_z()

Hx = steane.get_parity_x()

H = steane.get_parity()

observable  = steane.get_observables_z()

This product is a networking product designed for enterprise and data center networking.

QEC is a networking product designed for enterprise and data center networking.

decoder = qec.get_decoder("steane_lut_decoder", Hz) 

Product Information 0 QEC is a networking product designed for enterprise and data center networking. p=0.1。

A diagram shows the Steane code procedure performed on the probability of a data qubit bit flip error in the following code example.

2. Steane

QEC is a networking product designed for enterprise and data center networking.

This product is a networking product designed for enterprise and data center networking.

# Probability of a data qubit bit flip error

p = 0.1

nShots = 10

nLogicalErrors = 0

for i in range(nShots):

    # Generate noisy data                                                                                                                                                                                                                                                          

    data = qec.generate_random_bit_flips(Hz.shape[1], p)                                                                                                                                                                                                                    

    # Calculate which syndromes are flagged                                                                                                                                                                                                                                        

    syndrome = Hz@data % 2                                                                                                                                                                                                                                                        

    # Decode syndromes to determine predicted observables                                                                                                                                                                                                                                                                               

    result = decoder.decode(syndrome)                                                                                                                                                                                                                                              

    data_prediction = np.array(result.result, dtype=np.uint8)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      

    predicted_observable = observable@data_prediction % 2                                                                                                                                                                                                                             

    # Determine actual observables directly from the data                                                                                                                                                                                                                                                                               

    actual_observable = observable@data % 2                                                                                                                                                                                                                                           

    # Add to counter if logical error occurred                                                                                                                                                                                                                                  

    if (predicted_observable != actual_observable):

        nLogicalErrors += 1                                                                                                                                                                                                     

# Print the logical error rate

print(nLogicalErrors/nShots)

This product is a networking product designed for enterprise and data center networking.

syndromes, data = qec.sample_code_capacity(Hz, nShots, p).

This product is a networking product designed for enterprise and data center networking.

A line plot shows the physical error rate against the logical error rate for the Steane code capacity analysis.

3. Steane

QEC sample_memory_circuit , provides QEC and Product Information QEC is a networking product designed for enterprise and data center networking.

syndromes, data = qec.sample_memory_circuit(steane, numShots, numRounds, noise=noise)

provides,sample_memory_circuit QEC is a networking product designed for enterprise and data center networking.

CUDA-Q QEC (including C++, and), CUDA-Q QEC 。

CUDA-Q

CUDA-Q VQE is a networking product designed for enterprise and data center networking.

CUDA-Q is a networking product designed for enterprise and data center networking.

CUDA-Q:

QPU is a networking product designed for enterprise and data center networking.

ADAPT-VQE is a networking product designed for enterprise and data center networking.

CUDA-QX:

import cudaq, cudaq_solvers as solvers

import numpy as np

from scipy.optimize import minimize

Product Information create_molecule This product is a networking product designed for enterprise and data center networking.

geometry=[('N', (0.0, 0.0, 0.5600)), ('N', (0.0,0.0, -0.5600))]

molecule = solvers.create_molecule(geometry,

                                            'sto-3g',    #basis set

                                            0,           #charge

                                            0,           #multiplicity

                                            nele_cas=2,

                                            norb_cas=3,

                                            ccsd=True,

                                            casci=True,

                                            verbose=True)

Product Information nele_cas and norb_cas This product is a networking product designed for enterprise and data center networking. ccsd or casci Product Information True This product is a networking product designed for enterprise and data center networking. print(molecule.energies) This product is a networking product designed for enterprise and data center networking.

CUDA-Q is a networking product designed for enterprise and data center networking. CUDA-QX 。

CUDA-Q: ADAPT-VQE

Pseudo-Trotter is a networking product designed for enterprise and data center networking.

The diagram shows the ADAPT-VQE workflow, which iteratively builds an ansatz from an operator pool to more efficiently converge to a ground state energy.

4, ADAPT-VQE (: Product Information )

Product Information Product Information ADAPT-VQE is a networking product designed for enterprise and data center networking.

numQubits = molecule.n_orbitals * 2

numElectrons = molecule.n_electrons

Product Informationcudaqx.solvers.get_operator_pool ansatz,, (op_pool_uccsdUCCSD is a networking product designed for enterprise and data center networking.

# Extract operators

operators=solvers.get_operator_pool("uccsd",

                                            num_qubits=numQubits,

                                            num_electrons=numElectrons)

# Retrieve number of operators                                            

count=len(operators)

# Make a list of initial parameters

init_params=[0.05]*count

print(init_params)

# Make final operator pool form operators and parameters

op_pool_uccsd=[1j*coef*op for coef,op in zip(init_params, operators)]

Hartree-Fock is a networking product designed for enterprise and data center networking.

@cudaq.kernel

def initState(q: cudaq.qview):

    for i in range(numElectrons):

        x(q[i])

ADAPT-VQE, molecule.hamiltonian Jordan-Wigner is a NVIDIA networking product designed for enterprise and data center networking.

energy, thetas, ops = solvers.adapt_vqe(initState,

                                                molecule.hamiltonian,

                                                op_pool_uccsd,

                                                optimizer=minimize,

                                                method='L-BFGS-B',

                                                jac='3-point',

                                                tol=1e-7)

print('Adapt-VQE energy: ', energy)

print('Optimum pool operators: ', [op.to_string(False) for op in ops])

CUDA-Q is a networking product designed for enterprise and data center networking.

QPU runs, cudaq.set_target(‘nvidia’, mqpu=’True’) and cuadq.mpi.initialize Product Information MQPU ADAPT-VQE is a networking product designed for enterprise and data center networking. cuadq.mpi.finalize。

QPU, 16 (8 6) H 4.5, (5)

A line chart shows the time to compute the gradient in seconds is accelerated with multiple NVIDIA H100 GPUs, up to 4.5x faster with six GPUs.

5, GPU CUDA-Q ADAPT-VQE 。

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