FPGA-Placement via Quantum Annealing

• URL: http://arxiv.org/abs/2312.15467v1
• Date: Sun, 24 Dec 2023 12:23:09 GMT
• Title: FPGA-Placement via Quantum Annealing
• Authors: Thore Gerlach, Stefan Knipp, David Biesner, Stelios Emmanouilidis, Klaus Hauber, Nico Piatkowski
• Abstract summary: Field-Programmable Gate Arrays (FPGAs) have asserted themselves as vital assets in contemporary computing. The procedure of placement -- determining the optimal spatial arrangement of functional blocks on an FPGA to minimize communication delays and enhance performance -- is an NP-hard problem. In this paper, we re-formulate the placement problem as a series of so called unconstrained binary optimization (QUBO) problems which are subsequently solved via AQC.
• Score: 1.1639171061272031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Field-Programmable Gate Arrays (FPGAs) have asserted themselves as vital assets in contemporary computing by offering adaptable, reconfigurable hardware platforms. FPGA-based accelerators incubate opportunities for breakthroughs in areas, such as real-time data processing, machine learning or cryptography -- to mention just a few. The procedure of placement -- determining the optimal spatial arrangement of functional blocks on an FPGA to minimize communication delays and enhance performance -- is an NP-hard problem, notably requiring sophisticated algorithms for proficient solutions. Clearly, improving the placement leads to a decreased resource utilization during the implementation phase. Adiabatic quantum computing (AQC), with its capability to traverse expansive solution spaces, has potential for addressing such combinatorial problems. In this paper, we re-formulate the placement problem as a series of so called quadratic unconstrained binary optimization (QUBO) problems which are subsequently solved via AQC. Our novel formulation facilitates a straight-forward integration of design constraints. Moreover, the size of the sub-problems can be conveniently adapted to the available hardware capabilities. Beside the sole proposal of a novel method, we ask whether contemporary quantum hardware is resilient enough to find placements for real-world-sized FPGAs. A numerical evaluation on a D-Wave Advantage 5.4 quantum annealer suggests that the answer is in the affirmative.

Related papers

• A Fast and Adaptable Algorithm for Optimal Multi-Qubit Pathfinding in Quantum Circuit Compilation [0.0]
  This work focuses on multi-qubit pathfinding as a critical subroutine within the quantum circuit compilation mapping problem. We introduce an algorithm, modelled using binary integer linear programming, that navigates qubits on quantum hardware optimally with respect to circuit SWAP-gate depth. We have benchmarked the algorithm across a variety of quantum hardware layouts, assessing properties such as computational runtimes, solution SWAP depths, and accumulated SWAP-gate error rates.
  arXiv  Detail & Related papers  (2024-05-29T05:59:15Z)
• Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization [0.8121127831316319]
  We propose a method for constructing quantum circuits which greatly reduces inter-device communication costs. We show that we can construct tractable circuits nearly three times the size of previous QDCA methods while retaining a similar or greater level of quality.
  arXiv  Detail & Related papers  (2024-05-01T20:49:50Z)
• Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach [6.347685922582191]
  We introduce a novel compiler that prioritizes reducing the expected execution time by jointly managing the generation and routing of EPR pairs. We present a real-time, adaptive approach to compiler design, accounting for the nature of entanglement generation and the operational demands of quantum circuits. Our contributions are twofold: (i) we model the optimal compiler for DQC using a Markov Decision Process (MDP) formulation, establishing the existence of an optimal algorithm, and (ii) we introduce a constrained Reinforcement Learning (RL) method to approximate this optimal compiler.
  arXiv  Detail & Related papers  (2024-04-25T23:03:20Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Measurement-Based Quantum Approximate Optimization [0.24861619769660645]
  We focus on measurement-based quantum computing protocols for approximate optimization. We derive measurement patterns for applying QAOA to the broad and important class of QUBO problems. We discuss the resource requirements and tradeoffs of our approach to that of more traditional quantum circuits.
  arXiv  Detail & Related papers  (2024-03-18T06:59:23Z)
• Exploring Non-Linear Programming Formulations in QuantumCircuitOpt for Optimal Circuit Design [0.0]
  We present a new version of QuantumOpt, an open-source software for quantum algorithmic modeling. We show that QCOpt can run up to 11.3x-up with speeds up to 11.3x-up on average. We also present opportunities for exploring the behavior of gradient-based NLP solvers.
  arXiv  Detail & Related papers  (2023-10-27T17:16:58Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• 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)
• Polynomial unconstrained binary optimisation inspired by optical simulation [52.11703556419582]
  We propose an algorithm inspired by optical coherent Ising machines to solve the problem of unconstrained binary optimization. We benchmark the proposed algorithm against existing PUBO algorithms, and observe its superior performance. The application of our algorithm to protein folding and quantum chemistry problems sheds light on the shortcomings of approxing the electronic structure problem by a PUBO problem.
  arXiv  Detail & Related papers  (2021-06-24T16:39:31Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

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.