Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor

• URL: http://arxiv.org/abs/2109.13189v1
• Date: Mon, 27 Sep 2021 17:05:13 GMT
• Title: Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor
• Authors: Akshay Gaikwad and Arvind and Kavita Dorai
• Abstract summary: We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix $chi$ corresponding to various two- and three-qubit quantum gates with a high fidelity. We also experimentally characterized the reduced dynamics of a two-qubit subsystem embedded in a three-qubit system.
• Score: 4.291616110077346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix $\chi$ corresponding to various two- and three-qubit quantum gates with a high fidelity. The CS algorithm is implemented using two different operator bases, namely, the standard Pauli basis and the Pauli-error basis. We experimentally demonstrate that the performance of the CS algorithm is significantly better in the Pauli-error basis, where the constructed $\chi$ matrix is maximally sparse. We compare the standard least square (LS) optimization QPT method with the CS-QPT method and observe that, provided an appropriate basis is chosen, the CS-QPT method performs significantly better as compared to the LS-QPT method. In all the cases considered, we obtained experimental fidelities greater than 0.9 from a reduced data set, which was approximately five to six times smaller in size than a full data set. We also experimentally characterized the reduced dynamics of a two-qubit subsystem embedded in a three-qubit system, and used the CS-QPT method to characterize processes corresponding to the evolution of two-qubit states under various $J$-coupling interactions.

Related papers

• Block encoding by signal processing [0.0]
  We demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU) can themselves be efficiently utilized for BE implementation. We present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons. We find that, while using QSVT for BE results in the best gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to $lesssim11$ qubits.
  arXiv  Detail & Related papers  (2024-08-29T18:00:02Z)
• Evaluating Ground State Energies of Chemical Systems with Low-Depth Quantum Circuits and High Accuracy [6.81054341190257]
  We develop an enhanced Variational Quantum Eigensolver (VQE) ansatz based on the Qubit Coupled Cluster (QCC) approach. We evaluate our enhanced QCC ansatz on two distinct quantum hardware, IBM Kolkata and Quantinuum H1-1.
  arXiv  Detail & Related papers  (2024-02-21T17:45:03Z)
• QuIP: 2-Bit Quantization of Large Language Models With Guarantees [44.212441764241]
  This work studies post-training parameter quantization in large language models (LLMs) We introduce quantization with incoherence processing (QuIP), a new method based on the insight that quantization benefits from $textitincoherent$ weight and Hessian matrices.
  arXiv  Detail & Related papers  (2023-07-25T07:44:06Z)
• Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective [74.48124653728422]
  Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically. We argue that an overlooked problem of oscillation is in the PTQ methods.
  arXiv  Detail & Related papers  (2023-03-21T14:52:52Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Gradient-descent quantum process tomography by learning Kraus operators [63.69764116066747]
  We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems. We use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes.
  arXiv  Detail & Related papers  (2022-08-01T12:48:48Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• An Ensemble Approach for Compressive Sensing with Quantum [1.8477401359673713]
  We leverage the idea of a statistical ensemble to improve the quality of quantum annealing based binary compressive sensing. Our experiments, on a D-Wave 2000Q quantum processor, demonstrated that the proposed ensemble scheme is notably less sensitive to the calibration of the penalty parameter.
  arXiv  Detail & Related papers  (2020-06-08T15:32:22Z)
• True experimental reconstruction of quantum states and processes via convex optimization [4.291616110077346]
  We use a constrained convex optimization (CCO) method to experimentally characterize arbitrary quantum states and unknown quantum processes on a two-qubit NMR quantum information processor.
  arXiv  Detail & Related papers  (2020-03-31T08:02:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.