NotionNext BLOGThe future of computing is "hybrid": Why will quantum computers work together with classical systems?
光子盒研究院
In the past decade, quantum computers have become increasingly advanced and more accessible to users. Quantum computing can solve complex problems that classical computers struggle with, potentially sparking a revolution in various fields from pharmaceuticals to finance.
In theory, quantum applications can solve a range of finite computational challenges exponentially faster. Today, quantum computing research has produced hundreds of algorithms that have been proven to run on near-term quantum computing systems; recent studies even suggest that these algorithms exhibit quantum advantages over classical computation in specific areas.
Quantum advantage stems from the principles of superposition and entanglement, allowing quantum bits (qubits) to significantly accelerate certain computations. However, significant challenges exist in noisy intermediate-scale quantum (NISQ) systems that hinder progress towards achieving quantum advantage. The notion that quantum computers will replace classical systems is misleading because it overlooks the nuanced capabilities and limitations of each computational mode.
In this article, we argue that the future of computation may not be solely based on either quantum or classical computing but rather a combination of both.
While excitement surrounds quantum computing, it is crucial to temper our enthusiasm with reality.
Due to the limited number of qubits and their instability leading to noise, errors, and information loss, current state-of-the-art quantum computers are unable to address real-world problems. This state of affairs for quantum computing is referred to as the NISQ era proposed by Preskill where these machines only have a few hundred qubits severely constrained by noise. Last year IBM's 433-qubit Osprey processor broke through the 100-qubit milestone for the first time and plans to introduce a 1000-qubit chip named Condor in 2023; recently AtomComputing announced they will launch an atomic array with 1225 sites - making it the world's first commercial computer surpassing 1000 qubits by 2024.
Other aspects beyond just the quantity of qubits also impact practical capabilities such as parallelism in operations and topological layout of qubits. Researchers are exploring innovative methods utilizing existing NISQ systems for solving valuable problems while demonstrating performance advantages over today's classical computers as matured over coming years, enhancing their computational power for broader applications providing assistance within specific practical domains.
Despite having immense potential power at its disposal whether three years down five or ten years ahead there remain limitations inherent within them.
For instance traditional Central Processing Units' data input efficiency far exceeds those achievable via Quantum Processors which are better suited organizing accessing memory resources efficiently Graphics Processing Units excel rendering complex graphics processing large language models CPUs GPUs outperform Quantum processors completing tasks effectively simply put running Zoom meetings drafting documents Microsoft Word would not be ideal nor efficient using Quantum Computers
Compared against Classical Computing Systems when handling large scale calculations involving small datasets Quantum Computers hold an edge speed wise
This raises an important question: what is most effective use case scenario for Quantum Computers? It lies within combining them alongside Classical Computing Systems whereby classic system handles data preparation visualization error correction tasks whilst QPU manages complex computations
This collaborative approach maximizes strengths minimizes weaknesses forming symbiotic relationship poised drive developments across both realms
"Hybrid Quantum Computing" serves as preferred industry term encapsulating concept wherein both types work together resolving problem statements
Essentially speaking this embodies entirety behind concept running almost all facets operating Quantum Computer relies heavily upon Classic Computer From coordinating myriad subcomponents executing gates measuring interpreting results software hardware compiling optimizing converting user-submitted programs runnable cloud-based infrastructure
Regardless how defined ultimate goal every participant remains finding utility purposefulness within constraints imposed currently onto functionality offered by QC
Practically speaking whenever discussing "hybrid" computation method refers cooperative manner different aspects problem statement transition optimally between tools best suited respective stages leveraging benefits each stage offers IonQ VP Product Development Matthew Keesan cited Variational-Quantum-Eigensolver VQE algorithm example illustrating optimal way users leverage present upcoming QC devices effectively
"If I want optimize parameter according function find maximum minimum value compare values under given states part doesn't require QC prepare state bring back result slightly newer one walking space states interaction between CC QC VQE used calculate molecule's 'ground-state' - chemically stable configuration Depending algorithm multiple effective approaches however VQE achieved creating single quant circuit featuring specific parameterized components e.g angles certain gates then altering parameters using classic optimization algorithm achieve desired outcome"
Introduction Variational-Quantum-Algorithms VQA brought forth vital hybrid-computing type Using classic optimizer train parametric quant circuits framework handle variety tasks examples include molecular ground excited states optimization linear equation solving machine learning Quant Approximation Optimization Algorithm QAOA another exemplar employing aforementioned method Some related papers instances convincingly demonstrated prowess hybrid-variable-algorithm emergence While Hybrid Algorithms Platforms might represent merely initial steps still reason believe Q-applications always inherently mixed nature For instance requiring preprocessing step preparing data prior applying Q-algorithm post-processing step readying data after execution
Throughout history integrating diverse compute architectures isn't novel GPU integral high-performance computes works best conjunction CPU Similarly Quanta Processing Unit QPU able deliver maximal efficacy integrated alongside Classic System
Then arises question if tightly integrate QPU along CPU GPU where should these units placed?
Internally deployed quanta comp holds strict control execution priority security benefits faces high upfront costs specialized knowledge challenge Conversely Cloud-based quanta services offer flexibility lower initial costs could raise concerns regarding data security latency issues To clearly distinguish all differing viewpoints surrounding Hybrid-QC employed workflow methodology proposing workflows specify activity set required execute hybrid-quantum-classical application sequence partially reveal overall structure Such activities expanded further into sub-workflows Typically activities represented nodes directed graph low dependency control edges graph Example Workflow Hybrid Application depicted above specifically shows subset calculation cluster activities In essence Hybrid Computation categorized two major classes:
1 Vertical Mixed-QC: includes classic steps necessary make Quanta Program run on Quanta Comp similar fashion provide compilation control stack
2 Horizontal Mixed-QC: encompasses operation activities needed perform algorithm using Quanta Comp Classic Comp distinguishing various sorting orderings between Quantal Regular Computational Steps Clear-cut terminology aid developing concise tools throughout Quantal Stack Leveraging architecture realize programming possibilities available solely relying either Classical or Quantal Compute alone By harnessing dual-mode advantages Hybrid Algorithms showcase us vision Integrated Compute Future - synergistic collaboration b/w Classical & Quantal Resources tackling some world’s challenging issues Now introducing Class Computations into realm Quinta overcome latter’s hurdles however former brings own sets challenges merging two introduces new ones Forbes article once pointed controlling large entangled system particularly challenging moreover long timescales involved acquiring sufficient quibits useful computations Time scales increase circuit size parameter count increasing challenge optimizing parameters – primary class challenge investing Quinta Computation Utilizing Digital Quinta Computer conducting quintessential simulation requires fault tolerance yet existing error rates fall short Further simulating quintessence system demands precise controls something digital quinta comp presently incapable delivering Hence Simulated Quinta Computation becomes solution Paper titled “The intrinsic difficulties simulating” authored Jukka Knuutinen Ljubomir Budinski Ph.D.s at company called Quanscient discussed fundamental issue faced algorithms namely linearity inherent physics many problems need solved involve nonlinear equations As integration levels rise determining when employ Quinta processing versus Classical processing becomes imperative Developing extant tools facilitating creation hybrid-quintile apps necessitates substantial amount classic compute resources especially memory Besides augmenting available quintile compute resources adopting new algorithms demanding fewer resources essential Additionally developed must prove advantageous compared counterparts withstand challenges posed by heuristic quintile algorithms Finally interoperability whether hardware software mandates standardized degree standardization hasn’t even commenced Success hinges upon fidelity longevity quibits electronic technologies controlling them advancing innovative software components hardware architectures simultaneously supporting creative scalable ways Above forward-looking initiative signifies? - Integrated Infrastructure considering adopting unified compute infrastructure blending Classic & Quintile Resources isolated islands capability Algo Methods researching hybrids fully exploiting strengths both modes Recognizing each processor excels particular aspect others leveraging ‘best’ strategy Management Framework opting SLURM Simple Linux Utility Resource Management among other integrated management systems complete resource allocation scheduling prioritization accounting etc Sometimes proved effective traditional environments found equally beneficial mixed environment Deployment Choices evaluating internally deploying quintile comp cloud-based service aligns organization’s computational security needs Vendor Strategies varying suppliers offering degrees integration compared Classic Systems attention ease difficulty integrating their solutions pre-existing infrastructures All reasons why many participants field High Performance Computing HPC harbor strong interest toward Quinte Computes never entirely supplant Traditional Computes instead becoming integral component larger HPC strategies Entering new era focus should lie establishing seamless integrated compute environment fully capitalizing strengths worlds Strategic standpoint working tandemly resolve currently insurmountable issues chemistry materials cosmology finance etc
References
[1]https://www.hpcwire.com/2023/05/08/starting-your-hybrid-classical-quantum-journey/
[2]https://ionq.com/resources/what-is-hybrid-quantum-computing
[3]https://arxiv.org/pdf/2210.15314.pdf
[4]https://ethz.ch/en/news-and-events/eth-news/news/2023/05/for-very-small-problem-sizes-a-classical-computer-is-faster.html
[5]https://www.frontiersin.org/articles/10.3389/fphy.2022.940293/full
[6]https://www.forbes.com/sites/forbestechcouncil/2023/11/10/the-future-of-computing-is-hybrid-why-quantum-computers-will-work-alongside-classical-systems/?sh=3538820d58c2
[7]https://www.quera.com/blog-posts/quantum-simulation-challenges-and-solutions
[8]https://quanscient.com/blog/the-inherent-problem-in-quantum-simulations-and-how-to-tackle-it
[9]https://learn.microsoft.com/en-us/azure/quantum/hybrid-computing-overview[10]https://journals.aps.org/pra/abstract/10.1103/PhysRevA.106.010101
- Giscus
Last update: 2024-3-18