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IEEE International Conference on Quantum Computing and Engineering (QCE)

General Algorithms for SPAM Noise Characterization

Abhijith Jayakumar, Stefano Chessa, Carleton Coffrin, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra

Published Sep 17, 2023• 0 citations
Publisher

Abstract

We present a general denoising algorithm for characterizing state preparation and measurement (SPAM) errors. This algorithm works by constructing a set of canonical super-operators using a set of efficient unitary circuits. This allows us to completely estimate the state and noise in the measurement apparatus up to the unavoidable “blame” gauge. We also show how this blame gauge can be fixed given prior information about the system, like the purity of the original state and independence properties of detector noise.

DOI
10.1109/qce57702.2023.10305

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We present a general denoising algorithm for characterizing state preparation and measurement (SPAM) errors. This algorithm works by constructing a set of canonical super-operators using a set of efficient unitary circuits. This allows us to completely estimate the state and noise in the measurement apparatus up to the unavoidable “blame” gauge. We also show how this blame gauge can be fixed given prior information about the system, like the purity of the original state and independence properties of detector noise.
We present a general denoising algorithm for performing simultaneous tomography of quantum states and measurement noise. This algorithm allows us to fully characterize state preparation and measurement (SPAM) errors present in any quantum system. Our method is based on the analysis of the properties of the linear operator space induced by unitary operations. Given any quantum system with a noisy measurement apparatus, our method can output the quantum state and the noise matrix of the detector up to a single gauge degree of freedom. We show that this gauge freedom is unavoidable in the general case, but this degeneracy can be generally broken using prior knowledge on the state or noise properties, thus fixing the gauge for several types of state-noise combinations with no assumptions about noise strength. Such combinations include pure quantum states with arbitrarily correlated errors, and arbitrary states with block independent errors. This framework can further use available prior information about the setting to systematically reduce the number of observations and measurements required for state and noise detection. Our method effectively generalizes existing approaches to the problem, and includes as special cases common settings considered in the literature requiring an uncorrelated or invertible noise matrix, or specific probe states.
Whereas in standard quantum-state tomography one estimates an unknown state by performing various measurements with known devices, and whereas in detector tomography one estimates the positive-operator-valued-measurement elements of a measurement device by subjecting to it various known states, we consider here the case of SPAM (state preparation and measurement) tomography where neither the states nor the measurement device are assumed known. For ddd-dimensional systems measured by ddd-outcome detectors, we find there are at most ${d}^{2}({d}^{2}\ensuremath{-}1)$ ``gauge'' parameters that can never be determined by any such experiment, irrespective of the number of unknown states and unknown devices. For the case d=2d=2d=2 we find gauge-invariant quantities that can be accessed directly experimentally and that can be used to detect and describe SPAM errors. In particular, we identify conditions whose violations detect the presence of correlations between SPAM errors. From the perspective of SPAM tomography, standard quantum-state tomography and detector tomography are protocols that fix the gauge parameters through the assumption that some set of fiducial measurements is known or that some set of fiducial states is known, respectively.
Correctly characterizing state preparation and measurement (SPAM) processes is a necessary step towards building reliable quantum processing units (QPUs). In this work, we discuss the subtleties behind separately measuring SPAM errors. We propose a protocol that can separately estimate SPAM errors, in the case where quantum gates are ideal. In the case where the quantum gates are imperfect, we derive bounds on the estimated SPAM error rates, based on gate error measures, which can be estimated independently of SPAM processes. Our method shows that the gauge ambiguity in characterizing SPAM operations can be resolved by assuming that there exists one qubit whose initial state is uncorrelated with other qubits in a QPU. We test the protocol on a publicly available five-qubit QPU and demonstrate its validity by comparing our results with simulations.
We introduce information erasure and quantum imprint as two properties that classify quantum instruments. Information erasure is the property that an appropriate postselection can render the distribution of earlier measurement outcomes independent of the initial quantum state while retaining all outcome branches. Quantum imprint is the complementary property that no admissible postselection can eliminate this state dependence. We show that this classification has a nontrivial structure and that the natural intuition that measurements providing more information about the initial quantum state should be less likely to exhibit information erasure does not hold in general. We further show that, under a sufficiently reliable postselection, information erasure enables first-order separation of state-preparation and measurement (SPAM) errors. Specifically, the first-order contribution of state-preparation error vanishes from the posterior distribution, whereas visible first-order contributions of measurement error remain. This result recasts SPAM error separation from the problem of simultaneously characterizing state preparation and measurement into the problem of realizing a reliable postselection.
Abstract In many platforms, errors from state-preparation and measurement (SPAM) dominate over single-qubit gate errors. To inform further hardware improvements and the development of more effective SPAM mitigation strategies, it is necessary to separately characterize the error contributions from SP and measurement (M). Here, we show how to construct a protocol that can efficiently and separately characterize the SP and M error parameters by using only high-fidelity single-qubit gates and repeated single-qubit measurements without reset. The measurements are assumed to be non-destructive and M errors are taken to be (spatially and temporally) uncorrelated and classical. Notably, the circuit depth of the protocol is independent of system size, and the target parameters may be characterized to a precision that is only limited by the number of experimental repetitions. We employ our protocol for the parallel characterization of SPAM errors on multiple qubits in the IBM quantum platform devices, where we find SP infidelities up to 6.57% and readout assignment errors up to 19.1%. Using numerical simulations, we also demonstrate how measurement-error mitigation that does not properly account for SP errors generally leads to a biased estimate of measured observable expectation values.
Noise characterization of multi-qubit entangling Clifford operations is a key practical bottleneck for quantum error mitigation and for the calibration, validation, and optimization of quantum error-correction protocols, especially in the presence of state preparation and measurement (SPAM) errors. Although cycle benchmarking can isolate some Pauli error components, it cannot resolve the problem of coupled error parameters, which leads to unlearnable degrees of freedom even in simple noisy gates, not to mention general nnn-qubit Clifford gates. Here we introduce mid-circuit-measurement-based generalized cycle benchmarking, a framework that makes otherwise unidentifiable Pauli fidelities and non-Markovian noise learnable via repeated measurements and classical post-processing. Applying the deferred feed-forward principle to generalized cycle benchmarking, we show that an insertion of mid-circuit measurements can reverse Pauli cycles induced by a general Clifford gate. This fact enables us to reveal a Pauli-noise learnability condition for Clifford gates. Assuming sufficient state preparation quality, we numerically demonstrate the feasibility of characterizing the previously unlearnable noise components. We implement the protocol on superconducting quantum processing units and validate its effectiveness in disambiguating the coupled noise components, benchmarked against conventional tomography. Finally, we observe consistent measurement-induced bit-flip bias and non-Markovian correlations, which define a range of applicability for the Pauli noise model and the proposed noise-characterization protocol.