Quantum Computing Algorithms for optimised Planning & Scheduling (QCAPS)

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1 Quantum Cmputing Algrithms fr ptimised Planning & Scheduling (QCAPS) Qubits Eurpe 2019 cnference Dr Rbert Desimne March Cpyright 2019 BAE Systems. All Rights Reserved.

2 QCAPS Quantum cmputing algrithms fr ptimised planning/scheduling InnvateUK task ( 400k task 12 mnths Oct 2017-Oct 2018) Premise Existing techniques can be enhanced by quantum algrithms t deliver ptimised plans/schedules in real-time fr cmplex tasks Prcessing pwer may be increased by several rders f magnitude When culd this happen? Hw large a quantum prcessr is required? Technical feasibility Perfrm initial experiments with quantum annealing (D-Wave) Explre hw/where universal quantum algrithm culd realise further gains in ptimised planning applicatins Business Feasibility Explre use cases within telecms ptimisatin/jb-shp scheduling What ther business/market applicatins will benefit? 2 Cpyright 2019 BAE Systems. All Rights Reserved.

3 Wrk packages quantum algrithms fr ptimised planning/scheduling WP1: Quantum annealing experiments Reviewed existing AI planning algrithms fr benchmarking Explred ptins fr enhancing using quantum annealing Mapped algrithms nt D-Wave prcessr and run experiments WP2: Telecm netwrk ptimisatin use cases Identified candidates fr telecm netwrk ptimisatin Re-ran experiments and explred speedup/scaling issues WP3: Cmparisn quantum annealing vs gate-mdel appraches (universal) Perfrmed theretical analysis f imprved speedup/scaling fr gate-mdel appraches Explred strength/weakness f bth quantum cmputing appraches WP4: Business feasibility fr ptimisatin tasks Organised market-fcussed innvatin wrkshp, inviting key industry/market players Distributin lgistics/traffic-flw ptimisatin Telecm netwrk ptimisatin Operatins management (Manufacturing/Infrastructure/Military) 3 Cpyright 2019 BAE Systems. All Rights Reserved.

4 WP1/2 Experiments Mapping nt D-Wave prcessr Transfrming jb-shp scheduling tasks int QUBO frmalism Currently require strng mathematic backgrund t generate QUBOs Key issue is characterising cnstraints within QUBO frmalism As mre cnstraints added, QUBO frmalism becmes mre cmplex Libraries f QUBOs wuld help nn-mathematicians t represent JSPs Mapping nt D-Wave prcessrs Allcating physical qubits vs lgical qubits Minimising chains f physical qubits t represent lgical qubits Allcating weights and cupling strengths t individual qubits D-Wave prvide sftware fr Mapping qubits and setting weights/cupling qbslv algrithm fr larger scheduling prblems Opprtunities fr quantum annealing sftware Which parts shuld be left classical, which quantum Classical: manage glbal search trees, check validity/quality f candidate slutins Quantum: sampled frm prblem space, guide explratin/pruning f search trees 4 Cpyright 2019 BAE Systems. All Rights Reserved.

5 WP1/2 Quantum annealing experiments Achievements Reviewed several types f ptimised planning/scheduling tasks Cnfirmed quantum annealing better suited t ptimised scheduling, rather than planning Cmplexity f ptimised planning requires pwer f universal quantum cmputing algrithms Perfrmed quantum annealing experiments fr JSPs and telecms netwrk ptimisatin Explred key factrs in mapping nt annealers and where best t apply quantum t hybrid slutins Explred largest size tasks addressable by DW2000Q and benchmarked against Ggle OR-tls DW2000Q cannt address hard JSPs, needs 10 9 physical qubits with existing chimera tplgy (6x cnnectivity) Fr half-duplex mesh prblems, needs 300,000 physical qubits fr natinal scale (50,000 cells) Annealing generates near-ptimum slutins after few anneal, even fr large prblems Determined what needs t be dne t address hard JSPs/telecms use cases Imprved vertex cnnectivity is mre challenging than increasing number f qubits (Next Gen) Libraries f QUBOs wuld help nn-mathematicians t represent hard prblems Imprved sftware fr mapping qubits and setting weights/cupling 5 Cpyright 2019 BAE Systems. All Rights Reserved.

6 WP3 Optimised planning/scheduling using universal QC Existing quantum algrithms Nt all quantum algrithms relevant fr ptimisatin prblems Mst deliver quadratic speedup, rather than expnential Knwn (square-rt, prvable) quantum speedups include: Unstructured cmbinatrial search / ptimisatin Backtracking (trial and errr) Mnte Carl methds fr parameter estimatin A quantum speedup is unlikely t be achievable when: The algrithm needs t perate n large amunts f data There is already a fast classical cmpetitr Determining whether a theretical quantum speedup is actually achieved in practice can be a significant challenge! 6 Cpyright 2019 BAE Systems. All Rights Reserved.

7 WP3 Universal (gate-mdel) appraches versus quantum annealing Reviewed several quantum algrithms (gate-mdel) fr planning/scheduling Emphasised algrithms with rigrus perfrmance/crrectness guarantees Graph cluring prblems gd fr representing ptimised scheduling tasks Prvided detailed cmplexity analysis fr quantum backtracking (graph cluring) Analysis prves backtracking algrithm utperfrms classical prcessr (specific scenaris) Cmparisn f gate-mdel versus quantum annealing appraches 7 Cpyright 2019 BAE Systems. All Rights Reserved.

8 WP4 Business feasibility fr ptimisatin Achievements Engaging with key stakehlders Held innvatin wrkshp with 50+ participants Ptential end-users, quantum experts (academia/industry/fund-hlders) Prvide update n current experiments and ther case study applicatins Use case: Telecms netwrk ptimisatin (BT) Use case: Traffic flw ptimisatin (VW) Use case: Distributin Lgistics (Ocad) Characterised majr business applicatins Identified range f practical ptimisatin prblems acrss 3 market sectrs Questinnaire cvering tp 20 questins (technical/business feasibility), including majr enablers and barriers t intrductin in key markets Market assessment fr ptimised planning and scheduling tasks Defined ptential glbal market size fr hybrid slutins ver next 5-10 years Supply-chain pprtunities fr UK business fr hybrid slutins/services Radmaps fr pragmatic capability develpment 8 Cpyright 2019 BAE Systems. All Rights Reserved.

9 WP4 Innvatin wrkshp Brainstrming utputs Team1 : Telecms Traffic engineering Quality f service Ruting/spectrum management Batching streams & cntent Traffic mnitring Feature analysis/pattern ID Deep packet inspectin (DPI) Applicatins f machine learning Resurce scheduling Wrkfrce ptimisatin Netwrk peratins/maintenance Tplgy design Infrastructure layut design Lcatin f base statins/masts Team2: Distributin Distributin lgistics Vehicle ruting (trunk/lcal netwrks) Warehusing/supply depts Lgistics scheduling (strategic/tactical) Traffic-flw ptimisatin Vehicle flw ptimisatin(cars/trucks) Rail netwrk ptimisatin Maritime traffic management Air traffic management/cntrl Maintenance scheduling Predictive maintenance Staff resurce allcatin Autmated manufacturing Team3: Operatins Scheduling peratins Hspital planning Fleet/inventry management Airprt/flight scheduling Military peratins Pwer/grid management Oil/gas (upstream/dwnstream) Strategic decisin-making Drug discvery/develpment Materials mdelling Cancer screening/fd standards Met Office frecasting Supply chain ptimisatin Infrastructure management (cities) Urban transprtatin netwrks Critical natinal infrastructure (CNI) Operatinal resilience Managing data deluge (IT) Understanding daily threats Managing critical ndes (glbal) 9 Cpyright 2019 BAE Systems. All Rights Reserved.

10 WP4 Business feasibility Market assessment Glbal market fr hybrid slutins Hybrid (verall) date Hybrid (verall) date Telecms netwrk ptimisatin $1.6Bn ($31.8Bn) 2023 $6.9Bn ($46.4Bn) 2028) Distributin lgistics $0.97Bn ($19.4Bn) 2021 $4.9Bn ($32.9Bn) 2026 Traffic-flw ptimisatin (land/air/sea/rail) $1.4Bn ($28.1Bn) 2022 $12.7Bn ($84.8Bn) 2027 Assumptin: Hybrids slutins/services at 5% verall market size( ) and 15% ( ) Radmap: Capability develpment Quantum annealing: ptimal scheduling 8-15 years Imprved cnnectivity/near ptimal/relaxed quantum advantage: 3-5 years Universal (gate-mdel): ptimal planning/scheduling years Imprved fault tlerance/near ptimal/relaxed quantum advantage: >10 years Other factrs affecting emergence f hybrid slutins Strngly integrated hybrid quantum classical systems/slutins SWAP benefits f quantum prcessrs 10 Cpyright 2019 BAE Systems. All Rights Reserved.

11 Hardware & Systems Specialist quantum prcessrs (e.g. D-Wave, InQ) Hybrid quantum/classical system engineering (Design/Test/Integratin) Supply chain cmpnents (phtnics, crystat) (e.g. e2v, M2Lasers, Oxfrd instruments) Sftware Cmpilers, Cde ptimisers (e.g. CQC) Algrithms and Prgramming languages (e.g Riverlane Research) Libraries f quantum designs End-users rganisatins & specialist grups Finance, Transprt, Lgistics, Telecms, Energy, Manufacturing, Defence/Security Specialist cnsulting rgs (e.g. 1QBit, QxBranch, QCware) Academia Cntinued develpment f science, technlgy and systems engineering Opprtunities fr UK plc Training the next generatin f quantum experts & practitiners 11 Cpyright 2019 BAE Systems. All Rights Reserved.

12 Summary f achievements Quantum algrithms fr ptimisatin prblems WP1: Quantum annealing experiments Cnfirmed quantum annealing better suited t ptimised scheduling, rather than planning Perfrmed quantum annealing experiments fr JSPs (mapping/hybrid slutins) Determined hw t address hard JSPs (increase cnnectivity/number f qubits) WP2: Telecm quantum annealing experiments Characterised several hard prblems in telecms netwrk ptimisatin Perfrmed quantum annealing experiments n half-duplex mesh netwrk and FAP prblems Highlighted size f prcessr required t address key telecms industry prblems WP3: Optimised planning/scheduling using universal(gate-mdel) appraches First detailed cmparisn different quantum appraches (gate-mdel versus annealing) Cnfirmed number f lgical/physical qubits fr graph cluring fr bth appraches Outlined technlgy radmap fr addressing (industry-scale)ptimisatin prblems WP4: Business feasibility fr ptimisatin Engaged with key quantum stakehlders and characterised several business applicatins (3 market sectrs) Prvided a detailed market assessment (market size, UK pprtunities/radmaps fr early adptin) Delivered thrugh technical/business feasibility study f hybrid slutins fr ptimised planning/scheduling tasks in three market sectrs 12 Cpyright 2019 BAE Systems. All Rights Reserved.