OnionVQE Optimization Strategy for Ground State Preparation on NISQ Devices

• URL: http://arxiv.org/abs/2407.10415v1
• Date: Mon, 15 Jul 2024 03:32:27 GMT
• Title: OnionVQE Optimization Strategy for Ground State Preparation on NISQ Devices
• Authors: Katerina Gratsea, Johannes Selisko, Maximilian Amsler, Christopher Wever, Thomas Eckl, Georgy Samsonidze,
• Abstract summary: Variational Quantum Eigensolver (VQE) is used to exploit the capabilities of current Noisy Intermediate-Scale Quantum (NISQ) devices. VQE algorithms suffer from a plethora of issues, such as barren plateaus, local minima, quantum hardware noise, and limited qubit connectivity. We propose a VQE optimization strategy that builds upon recent advances in the literature, and exhibits very shallow circuit depths.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Variational Quantum Eigensolver (VQE) is one of the most promising and widely used algorithms for exploiting the capabilities of current Noisy Intermediate-Scale Quantum (NISQ) devices. However, VQE algorithms suffer from a plethora of issues, such as barren plateaus, local minima, quantum hardware noise, and limited qubit connectivity, thus posing challenges for their successful deployment on hardware and simulators. In this work, we propose a VQE optimization strategy that builds upon recent advances in the literature, and exhibits very shallow circuit depths when applied to the specific system of interest, namely a model Hamiltonian representing a cuprate superconductor. These features make our approach a favorable candidate for generating good ground state approximations on current NISQ devices. Our findings illustrate the potential of VQE algorithmic development for leveraging the full capabilities of NISQ devices.

Related papers

• Identifying Bottlenecks of NISQ-friendly HHL algorithms [0.0]
  We study noise resilience of NISQ-adaptation Iterative QPE and its HHL algorithm. Results indicate that noise mitigation techniques, such as Qiskit readout and Mthree readout packages, are insufficient for enabling results recovery even in the small instances tested here.
  arXiv  Detail & Related papers  (2024-06-10T14:11:27Z)
• Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
  This thesis focuses on identifying functional quantum circuits in noisy quantum hardware. We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently. In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
  arXiv  Detail & Related papers  (2024-02-21T12:30:39Z)
• A joint optimization approach of parameterized quantum circuits with a tensor network [0.0]
  Current intermediate-scale quantum (NISQ) devices remain limited in their capabilities. We propose the use of parameterized Networks (TNs) to attempt an improved performance of the Variational Quantum Eigensolver (VQE) algorithm.
  arXiv  Detail & Related papers  (2024-02-19T12:53:52Z)
• Variational Quantum Eigensolvers with Quantum Gaussian Filters for solving ground-state problems in quantum many-body systems [2.5425769156210896]
  We present a novel quantum algorithm for approximating the ground-state in quantum many-body systems. Our approach integrates Variational Quantum Eigensolvers (VQE) with Quantum Gaussian Filters (QGF) Our method shows improved convergence speed and accuracy, particularly under noisy conditions.
  arXiv  Detail & Related papers  (2024-01-24T14:01:52Z)
• Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits [70.97518416003358]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices. While tensor-train networks (TTNs) can enhance VQC representation and generalization, the resulting hybrid model, TTN-VQC, faces optimization challenges due to the Polyak-Lojasiewicz (PL) condition. To mitigate this challenge, we introduce Pre+TTN-VQC, a pre-trained TTN model combined with a VQC.
  arXiv  Detail & Related papers  (2023-05-18T03:08:18Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)
• Quantum Volume in Practice: What Users Can Expect from NISQ Devices [0.9208007322096533]
  Quantum volume (QV) has become the de-facto standard benchmark to quantify the capability of Noisy Intermediate-Scale Quantum (NISQ) devices. We perform our own series of QV calculations on 24 NISQ devices currently offered by IBM Q, IonQ, Rigetti, Oxford Quantum Circuits, and Quantinuum.
  arXiv  Detail & Related papers  (2022-03-08T02:31:26Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.