Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology

• URL: http://arxiv.org/abs/2601.08213v1
• Date: Tue, 13 Jan 2026 04:37:43 GMT
• Title: Quantum State Discrimination Enhanced by FPGA-Based AI Engine Technology
• Authors: Anastasiia Butko, Artem Marisov, David I. Santiago, Irfan Siddiqi,
• Abstract summary: We present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology.<n>A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying the state of a quantum bit (qubit), known as quantum state discrimination, is a crucial operation in quantum computing. However, it has been the most error-prone and time-consuming operation on superconducting quantum processors. Due to stringent timing constraints and algorithmic complexity, most qubit state discrimination methods are executed offline. In this work, we present an enhanced real-time quantum state discrimination system leveraging FPGA-based AI Engine technology. A multi-layer neural network has been developed and implemented on the AMD Xilinx VCK190 FPGA platform, enabling accurate in-situ state discrimination and supporting mid-circuit measurement experiments for multiple qubits. Our approach leverages recent advancements in architecture research and design, utilizing specialized AI/ML accelerators to optimize quantum experiments and reduce the use of FPGA resources.

Related papers

• Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
  This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
  arXiv  Detail & Related papers  (2025-08-29T10:37:19Z)
• Advancing Quantum State Preparation Using Decision Diagram with Local Invertible Maps [5.328178128965817]
  We propose a family of efficient Quantum State Preparation (QSP) algorithms tailored to different numbers of available ancilla qubits.<n>Our approach exploits the power of Local Invertible Map Decision Diagrams (LimTDDs) to reduce quantum circuit complexity.
  arXiv  Detail & Related papers  (2025-07-23T03:34:44Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Runtime Quantum Advantage with Digital Quantum Optimization [36.136619420474766]
  bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm on IBM's 156-qubit devices.<n>We suitably select problem instances that are challenging for classical methods, running in fractions of minutes even with multicore processors.<n>Our results indicate that available digital quantum processors, when combined with specific-purpose quantum algorithms, exhibit a runtime quantum advantage even in the absence of quantum error correction.
  arXiv  Detail & Related papers  (2025-05-13T15:24:17Z)
• ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements [7.469519605046083]
  This paper introduces QubiCML, a field-programmable gate array (FPGA) based system for real-time state discrimination. A multi-layer neural network has been designed and deployed on an FPGA to ensure accurate in-situ state discrimination. We evaluate QubiCML's performance on superconducting quantum processors and obtained an average accuracy of 98.5% with only 500 ns readout.
  arXiv  Detail & Related papers  (2024-06-27T00:45:37Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Sparse Quantum State Preparation for Strongly Correlated Systems [0.0]
  In principle, the encoding of the exponentially scaling many-electron wave function onto a linearly scaling qubit register offers a promising solution to overcome the limitations of traditional quantum chemistry methods. An essential requirement for ground state quantum algorithms to be practical is the initialisation of the qubits to a high-quality approximation of the sought-after ground state. Quantum State Preparation (QSP) allows the preparation of approximate eigenstates obtained from classical calculations, but it is frequently treated as an oracle in quantum information.
  arXiv  Detail & Related papers  (2023-11-06T18:53:50Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• DisQ: A Novel Quantum Output State Classification Method on IBM Quantum Computers using OpenPulse [4.695687634290403]
  This paper proposes DisQ, a quantum output state classification approach which reduces error rates of quantum programs on NISQ devices. Superconducting quantum computing technology has ushered in a new era of computational possibilities.
  arXiv  Detail & Related papers  (2021-02-01T20:43:53Z)

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.