Residue Number System (RNS) based Distributed Quantum Addition

• URL: http://arxiv.org/abs/2406.05294v1
• Date: Fri, 7 Jun 2024 23:39:14 GMT
• Title: Residue Number System (RNS) based Distributed Quantum Addition
• Authors: Bhaskar Gaur, Travis S. Humble, Himanshu Thapliyal,
• Abstract summary: We propose substituting a higher depth quantum addition circuit with Residue Number System (RNS) based quantum modulo6% adders. The RNS-based distributed quantum addition circuits possess lower depth and are distributed across multiple quantum computers/jobs. We show that RNS-based distributed quantum addition has 11.3 to 133.15% higher output probability over 6-bit to 10-bit non-distributed quantum full adders.
• Score: 0.5188841610098435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum Arithmetic faces limitations such as noise and resource constraints in the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers. We propose using Distributed Quantum Computing (DQC) to overcome these limitations by substituting a higher depth quantum addition circuit with Residue Number System (RNS) based quantum modulo adders. The RNS-based distributed quantum addition circuits possess lower depth and are distributed across multiple quantum computers/jobs, resulting in higher noise resilience. We propose the Quantum Superior Modulo Addition based on RNS Tool (QSMART), which can generate RNS sets of quantum adders based on multiple factors such as depth, range, and efficiency. We also propose a novel design of Quantum Diminished-1 Modulo (2n + 1) Adder (QDMA), which forms a crucial part of RNS-based distributed quantum addition and the QSMART tool. We demonstrate the higher noise resilience of the Residue Number System (RNS) based distributed quantum addition by conducting simulations modeling Quantinuum's H1 ion trap-based quantum computer. Our simulations demonstrate that RNS-based distributed quantum addition has 11.36% to 133.15% higher output probability over 6-bit to 10-bit non-distributed quantum full adders, indicating higher noise fidelity. Furthermore, we present a scalable way of achieving distributed quantum addition higher than limited otherwise by the 20-qubit range of Quantinuum H1.

Related papers

• Residue Number System (RNS) based Distributed Quantum Multiplication [0.4604003661048266]
  We propose utilizing the Residue Number System (RNS) based distributed quantum multiplication.<n>We provide estimates of quantum resource usage and compare them with those of an existing non-distributed quantum multiplier for 6 to 16 qubit sized output.<n>Our comparative analysis estimates up to 46.018% lower Toffoli depth, and reduction in T gates of 34.483% to 86.25%.
  arXiv  Detail & Related papers  (2025-06-21T05:00:03Z)
• Enhancing variational quantum algorithms by balancing training on classical and quantum hardware [1.8377902806196762]
  Variational Quantum Eigensolver (VQE) and quantum phase classification for up to 12-qubit Hamiltonians using quantum neural networks.<n>We numerically evaluate our approach for ground-state estimation of 6 to 18-qubit Hamiltonians using VQE and quantum phase classification for up to 12-qubit Hamiltonians using quantum neural networks.
  arXiv  Detail & Related papers  (2025-03-20T17:17:58Z)
• A Logarithmic Depth Quantum Carry-Lookahead Modulo $(2^n-1)$ Adder [0.8192907805418581]
  Development of quantum arithmetic circuits for modulo addition is vital for implementing quantum algorithms. Current Noisy Intermediate Scale Quantum (NISQ) era quantum computers cannot handle the additional computational cost associated with fault-tolerant designs. This work presents quantum carry-lookahead modulo $(2n - 1)$ adder (QCLMA), which is designed to receive two n-bit numbers and perform their addition with an O(log n) depth.
  arXiv  Detail & Related papers  (2024-08-02T04:31:22Z)
• 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)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Assisted quantum simulation of open quantum systems [0.0]
  We introduce the quantum-assisted quantum algorithm, which reduces the circuit depth of UQA via NISQ technology. We present two quantum-assisted quantum algorithms for simulating open quantum systems.
  arXiv  Detail & Related papers  (2023-02-26T11:41:02Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Quantum reservoir neural network implementation on coherently coupled quantum oscillators [1.7086737326992172]
  We propose an implementation for quantum reservoir that obtains a large number of densely connected neurons. We analyse a specific hardware implementation based on superconducting circuits. We obtain state-of-the-art accuracy of 99 % on benchmark tasks.
  arXiv  Detail & Related papers  (2022-09-07T15:24:51Z)
• Variational Quantum Reinforcement Learning via Evolutionary Optimization [0.0]
  We present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization. We propose a hybrid framework where the quantum RL agents are equipped with hybrid tensor network-variational quantum circuit (TN-VQC) architecture. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs.
  arXiv  Detail & Related papers  (2021-09-01T16:36:04Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Mitigating noise in digital and digital-analog quantum computation [0.0]
  Digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm. DAQC consistently surpasses digital approaches in fidelity, particularly as processor size increases. These results establish DAQC as a viable alternative for quantum computing in the NISQ era.
  arXiv  Detail & Related papers  (2021-07-27T17:30:23Z)
• 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.