Optimal depth and a novel approach to variational quantum process tomography
- URL: http://arxiv.org/abs/2404.16541v1
- Date: Thu, 25 Apr 2024 11:58:06 GMT
- Title: Optimal depth and a novel approach to variational quantum process tomography
- Authors: Vladlen Galetsky, Pol Julià Farré, Soham Ghosh, Christian Deppe, Roberto Ferrara,
- Abstract summary: We present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD.
Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography.
U-VQSVD outperforms an uninformed attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension.
- Score: 11.496254312838659
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography and decreases the required state initializations from $4^{n}$ to just $2^{n}$, all while ensuring high-fidelity reconstruction of the targeted unitary channel $U$. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to Quantum Deep Neural Network (QDNN) and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a general channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel Quantum Physical Unclonable Function (QPUF). U-VQSVD outperforms an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.
Related papers
- Lazy Qubit Reordering for Accelerating Parallel State-Vector-based Quantum Circuit Simulation [0.0]
Two quantum operation scheduling methods are proposed for quantum circuit simulation.
The proposed methods reduce all-to-all communication caused by qubit reordering.
We develop these methods tailored for two primary procedures in variational quantum eigensolver (VQE) simulation.
arXiv Detail & Related papers (2024-10-05T18:20:37Z) - Distribution-Flexible Subset Quantization for Post-Quantizing
Super-Resolution Networks [68.83451203841624]
This paper introduces Distribution-Flexible Subset Quantization (DFSQ), a post-training quantization method for super-resolution networks.
DFSQ conducts channel-wise normalization of the activations and applies distribution-flexible subset quantization (SQ)
It achieves comparable performance to full-precision counterparts on 6- and 8-bit quantization, and incurs only a 0.1 dB PSNR drop on 4-bit quantization.
arXiv Detail & Related papers (2023-05-10T04:19:11Z) - Quantum Gate Generation in Two-Level Open Quantum Systems by Coherent
and Incoherent Photons Found with Gradient Search [77.34726150561087]
We consider an environment formed by incoherent photons as a resource for controlling open quantum systems via an incoherent control.
We exploit a coherent control in the Hamiltonian and an incoherent control in the dissipator which induces the time-dependent decoherence rates.
arXiv Detail & Related papers (2023-02-28T07:36:02Z) - Automatic Network Adaptation for Ultra-Low Uniform-Precision
Quantization [6.1664476076961146]
Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability.
It ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference.
This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization.
arXiv Detail & Related papers (2022-12-21T09:41:25Z) - RepQ-ViT: Scale Reparameterization for Post-Training Quantization of
Vision Transformers [2.114921680609289]
We propose RepQ-ViT, a novel PTQ framework for vision transformers (ViTs)
RepQ-ViT decouples the quantization and inference processes.
It can outperform existing strong baselines and encouragingly improve the accuracy of 4-bit PTQ of ViTs to a usable level.
arXiv Detail & Related papers (2022-12-16T02:52:37Z) - Optimizing the depth of variational quantum algorithms is strongly
QCMA-hard to approximate [0.6445605125467572]
Variational Quantum Algorithms (VQAs) have seen intense study towards near-term applications on quantum hardware.
A crucial parameter for VQAs is the emphdepth' of the variational ansatz'' used.
We show that approximating the optimal depth for a given VQA ansatz is intractable.
arXiv Detail & Related papers (2022-11-22T19:00:01Z) - Learning Representations for CSI Adaptive Quantization and Feedback [51.14360605938647]
We propose an efficient method for adaptive quantization and feedback in frequency division duplexing systems.
Existing works mainly focus on the implementation of autoencoder (AE) neural networks for CSI compression.
We recommend two different methods: one based on a post training quantization and the second one in which the codebook is found during the training of the AE.
arXiv Detail & Related papers (2022-07-13T08:52:13Z) - ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum
Algorithms [51.02972483763309]
Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of quantum computers.
This work is accompanied by the release of the open-source Python package $textitorqviz$, which provides code to compute and flexibly plot 1D and 2D scans.
arXiv Detail & Related papers (2021-11-08T18:17:59Z) - A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems.
We numerically demonstrate that the MNIST image dataset satisfies such conditions.
We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
arXiv Detail & Related papers (2021-07-19T23:41:03Z) - Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
We study two different methods to prepare excited states on a quantum computer.
We benchmark these techniques on emulated and real quantum devices.
These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
arXiv Detail & Related papers (2020-09-28T17:21:25Z) - MoG-VQE: Multiobjective genetic variational quantum eigensolver [0.0]
Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers.
Here, we propose the approach which can combine both low depth and improved precision.
We observe nearly ten-fold reduction in the two-qubit gate counts as compared to the standard hardware-efficient ansatz.
arXiv Detail & Related papers (2020-07-08T20:44:50Z)
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.