Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session LL07: V: Quantum Characterization, Verification, and Validation |
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Sponsoring Units: DQI Chair: Luke Govia, IBM TJ Watson Research Center Room: Virtual Room 7 |
Tuesday, March 21, 2023 5:00AM - 5:12AM |
LL07.00001: Gate Sequence Optimization for Quantum State Tomography under Noise Violeta N Ivanova-Rohling, Niklas Rohling Quantum state tomography (QST) is time consuming even for small systems, thus, the optimal QST measurement scheme is highly relevant. While those optimal schemes are known in some ideal cases, e.g. mutually unbiased bases for non-degenerate measurements [1], often they need to be found numerically. Here, we investigate the effect of noise on the optimal QST measurement sets for two noise models: depolarization and over-under-rotation in two-qubit gates [2]. We apply these noise models to measurements realized by a quantum gate sequences followed by a projection on one state, yielding measurements effectively described by independent rank-1 projectors. This applies e.g. to two spin qubits measured by spin-to-charge conversion. We apply reinforcement learning for optimizing the effective times each quantum gate is switched on in a set of gate sequences. We evaluate the benefit of including noisy two-qubit gates. Even when two-qubit gates are not included, our solution outperforms the popular QST quorum by James et al. [3]. We extend the model by including errors from single-qubit gates and more variance in the gate sequences than necessary for realizing arbitrary projectors aiming at higher noise resilience overall. We test our findings on a quantum computing device. [1] Wootters, Fields, Ann. Phys. 191, 363 (1989) [2] Ivanova-Rohling, Rohling, Burkard, arXiv:2203.05677 [3] James et al., Phys. Rev. A 64, 052312 (2001) |
Tuesday, March 21, 2023 5:12AM - 5:24AM |
LL07.00002: Self-consistent noise characterization of quantum devices. Won Kyu Calvin Sun
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Tuesday, March 21, 2023 5:24AM - 5:36AM |
LL07.00003: Calibration of drive non-linearity for arbitrary-angle single-qubit gates using error amplification Stefania Lazar, Quentin Ficheux, Johannes Herrmann, Ants Remm, Nathan Lacroix, Christoph Hellings, François Swiadek, Dante Colao Zanuz, Graham J Norris, Mohsen B Panah, Alexander Flasby, Michael Kerschbaum, Jean-Claude Besse, Christopher Eichler, Andreas Wallraff The ability to execute high-fidelity operations is crucial to scaling up quantum devices to large numbers of qubits. However, signal distortions originating from non-linear components in the control lines can limit the performance of single-qubit gates. In this work, we use a measurement based on error amplification to characterize and correct the small single-qubit rotation errors produced by the non-linear scaling of the qubit drive rate with the amplitude of the programmed pulse. With our hardware, and for a 15-ns pulse, the rotation angles deviate by up to 3.4° from a linear model. Using purity benchmarking, we find that control errors reach 2 × 10-4, which accounts for half of the total gate error. Using cross-entropy benchmarking, we demonstrate arbitrary-angle single-qubit gates with coherence-limited errors of 2 × 10-4 and leakage below 6 × 10-5. While the exact magnitude of these errors is specific to our setup, our method is applicable to most sources of non-linearity. Our work shows that the non-linearity of qubit drive lines imposes an upper limit on the fidelity of single-qubit gates, independent of improvements in coherence times, circuit design, or leakage mitigation. |
Tuesday, March 21, 2023 5:36AM - 5:48AM |
LL07.00004: Linear Cross Entropy Benchmarking with Clifford Circuits Linghang Kong, Jianxin Chen, Dawei Ding, Cupjin Huang With the advent of quantum processors exceeding 100 qubits and the high engineering complexities involved, there is a need for holistically benchmarking the processor to have quality assurance. Linear cross-entropy benchmarking (XEB) has been used extensively for systems with 50 or more qubits but is fundamentally limited in scale due to the exponentially large computational resources required for classical simulation. In this work we propose conducting linear XEB with Clifford circuits, a scheme we call Clifford XEB. Since Clifford circuits can be simulated in polynomial time, Clifford XEB can be scaled to much larger systems. To validate this claim, we run numerical simulations for particular classes of Clifford circuits with noise and observe exponential decays. When noise levels are low, the decay rates are well-correlated with the noise of each cycle assuming a digital error model. We perform simulations of systems up to 1,225 qubits, where the classical processing task can be easily dealt with by a workstation. Furthermore, using the theoretical guarantees in Chen et al. (arXiv:2203.12703), we prove that Clifford XEB with our proposed Clifford circuits must yield exponential decays under a general error model for sufficiently low errors. Our theoretical results explain some of the phenomena observed in the simulations and shed light on the behavior of general linear XEB experiments. |
Tuesday, March 21, 2023 5:48AM - 6:00AM |
LL07.00005: Comparison of Random Circuit Sampling on Quantum and Classical Processors Sangchul Oh, Sabre Kais Random circuit sampling, a task to sample bit-strings from a random unitary operator, has been performed on the Sycamore quantum processor with 53 qubits and on the Zuchongzhi quantum processor with 56 and 61 qubits to demonstrate quantum advantage. In parallel, classical computers using tensor networks [Pan et al., Phys. Rev. Lett. 129, 090502 (2022) and Kalachev et al. arXiv:2112.15083 (2021)] could catch up with current quantum processors for random circuit sampling. While the linear cross entropy benchmark fidelity has been used to certify these advantage claims, it may not capture the statistical properties of output bit-strings. Here, we compare the samples generated by the Sycamore and Zuchongzhi quantum processors, and classical computers using tensor networks We found that the heat maps of all samples show stripe patterns. Some Zuchongzhi samples and Kalachev et al.'s samples pass the NIST random number tests. Using the Marchenko-Pastur distribution or the Wasserstein distance, we showed that the distances of the Sycamore samples from classical uniform bit-strings, measured as a function of the number of qubits or the number of cycles are different from those of the Zuchongzhi samples while the linear cross entropy fidelity for both samples decrease exponentially [1,2]. Also, it is shown that Kalachev et al.'s samples are statistically closer to the Sycamore samples than Pan et al.'s samples. Our results imply that various tools are needed to verify quantum advantage. |
Tuesday, March 21, 2023 6:00AM - 6:12AM |
LL07.00006: Error minimization for fidelity estimation of GHZ states with arbitrary noise Liangzhong Ruan Fidelity estimation is an essential building block for the quality control of entanglement distribution networks. This work considers a scenario in which multiple nodes share noisy GHZ states. Because measurements collapse quantum states, the nodes randomly sample a subset of noisy GHZ states for measurement and then estimate the average fidelity of the unsampled states conditioned on the measurement outcome. By constructing a fidelity-preserving diagonalization operation, analyzing the Bloch representation of GHZ states, and maximizing the Fisher information, the proposed estimation protocol achieves the lowest mean squared estimation error in a difficult scenario with arbitrary noise and no prior information. Moreover, this protocol is implementation friendly as it only performs local Pauli operators according to a predefined sequence. Numerical studies show that compared to existing fidelity estimation protocols, the proposed protocol reduces the estimation error in both scenarios with i.i.d. noise and correlated noise. |
Tuesday, March 21, 2023 6:12AM - 6:24AM Author not Attending |
LL07.00007: Detecting Measurement Induced Phase Transition on SuperconductingQuantum Computers with Neural Network Decoders Hossein Dehghani, Mohammad Hafezi, Michael J Gullans Measurement induced phase transition is an entanglement entropy phase transition |
Tuesday, March 21, 2023 6:24AM - 6:36AM |
LL07.00008: Learning the Structure of Quantum Phases of Matter with a Quantum Convolutional Neural Network Yu-Jie Liu, Michael Knap, Frank Pollmann Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-independent protocol for training QCNNs to discover order parameters which are unchanged under phase-preserving perturbations. We initiate the training sequence with the fixed-point wavefunctions of the quantum phase and then add translation invariant noise which respects the symmetries of the system to mask the fixed-point structure on short length scales. We illustrate this approach by training the QCNN on phases protected by time-reversal symmetry in one dimension, and test it on several time- reversal symmetric models exhibiting trivial, symmetry-breaking, and symmetry-protected topological order. The QCNN discovers a set of order parameters that identifies all three phases and accurately predicts the location and the shape of the phase boundary. Our training method provides a hardware-efficient and scalable way to perform quantum phase classification on a programmable quantum processor. |
Tuesday, March 21, 2023 6:36AM - 6:48AM |
LL07.00009: Universal compilation for quantum state tomography Le Bin Ho, Vu Tuan Hai Quantum state tomography is essential for quantum computing and quantum information technology. Recent progress in quantum technologies opened a new paradigm in quantum state tomography, such as using quantum computers. However, even under the quantum algorithms aid, the quantum state tomography requires a heavy growth of the number of measurements. |
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