Error-mitigated data-driven circuit learning on noisy quantum hardware

Abstract

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.

Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack

“…This could be a result of the way in which noise-aware pytket prioritises noise in its routing scheme, with gate errors taking precedence. 19 Noise Level, Connectivity Trade Off More highly-connected architectures typically allow for shallower implementations of a given circuit as compared to less-connected ones. However, the noise levels may be higher due to crosstalk [75], resulting in a trade-off between connectivity and the total amount of noise incurred when running a computation.…”

“…Reducing the connectivity between superconducting qubits has been used to reduce noise levels [75]. In superconducting qubits, this can also be counteracted using tunable couplers [3], but this is 19 For larger numbers of qubits and deeper circuits, gate errors becomes more impactful on the total noise, and materialises as giving noise-aware pytket an advantage for larger numbers of qubits.…”

“…The second is for the circuit class to be a particular instance of an application. Such benchmarks have been defined in the context of quantum simulation [12][13][14][15][16], quantum machine learning [10,[17][18][19][20], discrete optimisation [21][22][23][24], and quantum computational supremacy [3,[25][26][27]. This approach has the advantage that the definition of success when running a circuit from the class is fairly straightforward, but with the disadvantage that performance, as measured by one instance of an application, may not be predictive of performance for the application generically, or another application.…”

Characterizing the Reproducibility of Noisy Quantum Circuits

“…The varying environment and fluctuating controls present in current NISQ computing platforms lead to transient sources of noise that impact the ability to reproduce the results of a quantum circuit [ 25 , 26 ]. While errors arising from fixed sources of device noise can frequently be mitigated using tailored methods [ 27 , 28 , 29 , 30 , 31 ], ill-characterized and transient noise impedes the reproduction of NISQ circuit results and prevents the replication of quantum computed solutions. It is, therefore, important to assess the conditions under which a noisy quantum circuit may be expected to be reproducible, as well as the correlation with the corresponding noise in the quantum device.…”

Readout rebalancing for near-term quantum computers

“…Instead, a variety of schemes have been proposed to mitigate-without completely eliminating-errors. There are two types of errors that are targeted by these schemes: Those that affect the preparation of the quantum state [20][21][22][23][24][25] and those that affect the measurement of the prepared state [10,[26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. This paper focuses on the latter type-called readout errors-and how one can modify the quantum state prior to readout in order to reduce these errors.…”