| 1. |
- Chen, Liangyu, 1994, et al.
(författare)
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Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier
- 2023
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Ingår i: npj Quantum Information. - : Springer Science and Business Media LLC. - 2056-6387. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- High-fidelity and rapid readout of a qubit state is key to quantum computing and communication, and it is a prerequisite for quantum error correction. We present a readout scheme for superconducting qubits that combines two microwave techniques: applying a shelving technique to the qubit that reduces the contribution of decay error during readout, and a two-tone excitation of the readout resonator to distinguish among qubit populations in higher energy levels. Using a machine-learning algorithm to post-process the two-tone measurement results further improves the qubit-state assignment fidelity. We perform single-shot frequency-multiplexed qubit readout, with a 140 ns readout time, and demonstrate 99.5% assignment fidelity for two-state readout and 96.9% for three-state readout–without using a quantum-limited amplifier.
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| 2. |
- Warren, Christopher, 1992, et al.
(författare)
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Extensive characterization and implementation of a family of three-qubit gates at the coherence limit
- 2023
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Ingår i: npj Quantum Information. - 2056-6387. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- While all quantum algorithms can be expressed in terms of single-qubit and two-qubit gates, more expressive gate sets can help reduce the algorithmic depth. This is important in the presence of gate errors, especially those due to decoherence. Using superconducting qubits, we have implemented a three-qubit gate by simultaneously applying two-qubit operations, thereby realizing a three-body interaction. This method straightforwardly extends to other quantum hardware architectures, requires only a firmware upgrade to implement, and is faster than its constituent two-qubit gates. The three-qubit gate represents an entire family of operations, creating flexibility in the quantum-circuit compilation. We demonstrate a process fidelity of 97.90%, which is near the coherence limit of our device. We then generate two classes of entangled states, the Greenberger–Horne–Zeilinger and Dicke states, by applying the new gate only once; in comparison, decompositions into the standard gate set would have a two-qubit gate depth of two and three, respectively. Finally, we combine characterization methods and analyze the experimental and statistical errors in the fidelity of the gates and of the target states.
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| 3. |
- Ahmed, Shahnawaz, 1995, et al.
(författare)
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Classification and reconstruction of optical quantum states with deep neural networks ()
- 2021
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Ingår i: Physical Review Research. - 2643-1564. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- We apply deep-neural-network-based techniques to quantum state classification and reconstruction. Our methods demonstrate high classification accuracies and reconstruction fidelities, even in the presence of noise and with little data. Using optical quantum states as examples, we first demonstrate how convolutional neural networks (CNNs) can successfully classify several types of states distorted by, e.g., additive Gaussian noise or photon loss. We further show that a CNN trained on noisy inputs can learn to identify the most important regions in the data, which potentially can reduce the cost of tomography by guiding adaptive data collection. Secondly, we demonstrate reconstruction of quantum-state density matrices using neural networks that incorporate quantum-physics knowledge. The knowledge is implemented as custom neural-network layers that convert outputs from standard feed-forward neural networks to valid descriptions of quantum states. Any standard feed-forward neural-network architecture can be adapted for quantum state tomography (QST) with our method. We present further demonstrations of our proposed QST technique with conditional generative adversarial networks (QST-CGAN) [Ahmed et al., Phys. Rev. Lett.127, 140502 (2021)10.1103/PhysRevLett.127.140502]. We motivate our choice of a learnable loss function within an adversarial framework by demonstrating that the QST-CGAN outperforms, across a range of scenarios, generative networks trained with standard loss functions. For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state. The QST-CGAN reconstructs states using up to two orders of magnitude fewer iterative steps than iterative and accelerated projected-gradient-based maximum-likelihood estimation (MLE) methods. We also demonstrate that the QST-CGAN can reconstruct both pure and mixed states from two orders of magnitude fewer randomly chosen data points than these MLE methods. Our paper opens possibilities to use state-of-the-art deep-learning methods for quantum state classification and reconstruction under various types of noise.
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| 4. |
- Ahmed, Shahnawaz, 1995, et al.
(författare)
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Gradient-Descent Quantum Process Tomography by Learning Kraus Operators
- 2023
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Ingår i: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 130:15
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Tidskriftsartikel (refereegranskat)abstract
- We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems.
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| 5. |
- Ahmed, Shahnawaz, 1995, et al.
(författare)
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Implicit differentiation of variational quantum algorithms
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Several quantities important in condensed matter physics, quantum information, and quantum chemistry, as well as quantities required in meta-optimization of machine learning algorithms, can be expressed as gradients of implicitly defined functions of the parameters characterizing the system. Here, we show how to leverage implicit differentiation for gradient computation through variational quantum algorithms and explore applications in condensed matter physics, quantum machine learning, and quantum information. A function defined implicitly as the solution of a quantum algorithm, e.g., a variationally obtained ground- or steady-state, can be automatically differentiated using implicit differentiation while being agnostic to how the solution is computed. We apply this notion to the evaluation of physical quantities in condensed matter physics such as generalized susceptibilities studied through a variational quantum algorithm. Moreover, we develop two additional applications of implicit differentiation -- hyperparameter optimization in a quantum machine learning algorithm, and the variational construction of entangled quantum states based on a gradient-based maximization of a geometric measure of entanglement. Our work ties together several types of gradient calculations that can be computed using variational quantum circuits in a general way without relying on tedious analytic derivations, or approximate finite-difference methods.
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| 6. |
- Ahmed, Shahnawaz, 1995
(författare)
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Machine learning for quantum information and computing
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This compilation thesis explores the merger of machine learning, quantum information, and computing. Inspired by the successes of neural networks and gradient-based learning, the thesis explores how such ideas can be adapted to tackle complex problems that arise during the modeling and control of quantum systems, such as quantum tomography with noisy data or optimizing quantum operations, by incorporating physics-based constraints. We also discuss the Bayesian estimation of a quantum state with uncertainty estimates using physically meaningful priors. Classical machine learning could inspire new quantum-computing algorithms. One such idea is presented to extend the capabilities of variational quantum algorithms using implicit differentiation, enabling straightforward computation of physically interesting quantities on a quantum computer as a gradient. Implicit differentiation also leads to a novel method to generate multipartite entangled quantum states and allows hyperparameter tuning of quantum machine learning algorithms. Several new experiments were possible due to the theoretical and numerical techniques developed in the thesis — robust generation of a Gottesman- Kitaev-Preskill and cubic phase state in a 3D cavity, fast process tomography of a new family of superconducting gates with known noise, efficient process tomography of a physical operation implementing a logical gate on a bosonic error-correction code, and the reconstruction of a photoelectron’s quantum state.
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| 7. |
- Ahmed, Shahnawaz, 1995
(författare)
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Quantum state characterization with deep neural networks
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this licentiate thesis, I explain some of the interdisciplinary topics connecting machine learning to quantum physics. The thesis is based on the two appended papers, where deep neural networks were used for the characterization of quantum systems. I discuss the connections between parameter estimation, inverse problems and machine learning to put the results of the appended papers in perspective. In these papers, we have shown how to incorporate prior knowledge of quantum physics and noise models in generative adversarial neural networks. This thesis further discusses how automatic differentiation techniques allow training such custom neural-network-based methods to characterize quantum systems or learn their description. In the appended papers, we have demonstrated that the neural-network approach could learn a quantum state description from an order of magnitude fewer data points and faster than an iterative maximum-likelihood estimation technique. The goal of the thesis is to bring such tools and techniques from machine learning to the physicist’s arsenal and to explore the intersection between quantum physics and machine learning.
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| 8. |
- Ahmed, Shahnawaz, 1995, et al.
(författare)
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Quantum State Tomography with Conditional Generative Adversarial Networks ()
- 2021
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Ingår i: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 127:14
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Tidskriftsartikel (refereegranskat)abstract
- Quantum state tomography (QST) is a challenging task in intermediate-scale quantum devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the CGAN framework, two dueling neural networks, a generator and a discriminator, learn multimodal models from data. We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix. To reconstruct the density matrix, the generator and discriminator networks train each other on data using standard gradient-based methods. We demonstrate that our QST-CGAN reconstructs optical quantum states with high fidelity, using orders of magnitude fewer iterative steps, and less data, than both accelerated projected-gradient-based and iterative maximum-likelihood estimation. We also show that the QST-CGAN can reconstruct a quantum state in a single evaluation of the generator network if it has been pretrained on similar quantum states.
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| 9. |
- Kudra, Marina, 1992, et al.
(författare)
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Robust Preparation of Wigner-Negative States with Optimized SNAP-Displacement Sequences
- 2022
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Ingår i: PRX Quantum. - : AMER PHYSICAL SOC. - 2691-3399. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- Hosting nonclassical states of light in three-dimensional microwave cavities has emerged as a promising paradigm for continuous-variable quantum information processing. Here we experimentally demonstrate high-fidelity generation of a range of Wigner-negative states useful for quantum computation, such as Schrodinger-cat states, binomial states, Gottesman-Kitaev-Preskill states, as well as cubic phase states. The latter states have been long sought after in quantum optics and have never been achieved experimentally before. We use a sequence of interleaved selective number-dependent arbitrary phase (SNAP) gates and displacements. We optimize the state preparation in two steps. First we use a gradient-descent algorithm to optimize the parameters of the SNAP and displacement gates. Then we optimize the envelope of the pulses implementing the SNAP gates. Our results show that this way of creating highly nonclassical states in a harmonic oscillator is robust to fluctuations of the system parameters such as the qubit frequency and the dispersive shift.
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| 10. |
- Lambert, Neill, et al.
(författare)
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Modelling the ultra-strongly coupled spin-boson model with unphysical modes
- 2019
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- A quantum system weakly coupled to a zero-temperature environment will relax, via spontaneous emission, to its ground-state. However, when the coupling to the environment is ultra-strong the ground-state is expected to become dressed with virtual excitations. This regime is difficult to capture with some traditional methods because of the explosion in the number of Matsubara frequencies, i.e., exponential terms in the free-bath correlation function. To access this regime we generalize both the hierarchical equations of motion and pseudomode methods, taking into account this explosion using only a biexponential fitting function. We compare these methods to the reaction coordinate mapping, which helps show how these sometimes neglected Matsubara terms are important to regulate detailed balance and prevent the unphysical emission of virtual excitations. For the pseudomode method, we present a general proof of validity for the use of superficially unphysical Matsubara-modes, which mirror the mathematical essence of the Matsubara frequencies.
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