Benchmarking Hamiltonian Noise in the D-Wave Quantum Annealer

Abstract

Various sources of noise limit the performance of quantum computers by altering qubit states in an uncontrolled manner throughout computations and reducing their coherence time. In quantum annealers, this noise introduces additional fluctuations to the parameters defining the original problem Hamiltonian, such that they find the ground states of problems perturbed from those originally programmed. Here we describe a method to benchmark the amount of noise affecting the programmed Hamiltonian of a quantum annealer. We show that a sequence of degenerate runs with the coefficients of the programmed Hamiltonian set to zero leads to an estimate of the noise spectral density affecting Hamiltonian parameters "in situ" during the quantum annealing protocol. The method is demonstrated in D-Wave's lower noise 2000 qubit device (DW_2000Q_6) and in its recently released 5000 qubit device (Advantage_system1.1). Our benchmarking of DW_2000Q_6 shows Hamiltonian noise dominated by the 1/f 0.7 frequency dependence characteristic of flux noise intrinsic to the materials forming flux qubits. In contrast, Advantage_system1.1 is found to be affected by additional noise sources for low annealing times, with underlying intrinsic flux noise amplitudes 2 − 3 times higher than in DW_2000Q_6 for all annealing times.

Perturbed ferromagnetic chain: Tunable test of hardness in the transverse-field Ising model

“…This can be attributed to thermally assisted quantum transitions, whereby the relaxation rate [Eq. (22)] is nonzero about the minimum gap (Fig. 8) and can return probability to the ground state from the first excited state.…”

“…This Hamiltonian has been extensively studied on experimental quantum annealers for the past two decades, both within the context of combinatorial optimization [9][10][11][12][13][14][15] and quantum simulation [6,[16][17][18][19]. However, the quantumness of the dynamics used for computation on quantum annealers is still subject to debate due to the prevalent quantum and classical noise sources that can obscure coherent quantum processes [20][21][22][23].…”

qRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization

“…Continuing with the analysis of the results, Table 2 shows us the computational results of our picking and batching algorithm considering 1 qRobot through AWS-Braket and on the real quantum computer D-Wave Advantage_system1 [81]. The time value is an average and not counting latency time, job creation, and job return time.…”

“…In the tests we have done, the quantum computer is in the U.S. West (Oregon). Of all the tests that we have done, we have had an average latency time of about 2 plus all the work management processes that rises more or less to about 8 s. For the number of qubits exceeding 30, it is very convenient to use AWS-Braket (Advantage_system1.1) [81] instead of Qiskit or Pennylane. By the number of qubits and the execution time, that is differentially better.…”

“…In this table, we only consider the running time of the quantum algorithm on the real quantum computer from the qRobot (Advantage_system1.1[81]), not counting latency time, job creation, and job return time.…”