Qubits qop Tools Directions

Transcription

1 Qubits qop Tools Directions Steve Reinhardt Director of Software Tools D-Wave Systems

2 The qop goals are to establish key abstractions that are valuable for applications and higherlevel tools and effectively execute them on D-Wave systems.

3 qop Tools Initially developed as prototypes to spur user engagement qbsolv: Hybrid partitioning optimization tool solves a virtual QUBO* ToQ: Constraint-satisfaction language and solver dw: Command-line interface to SAPI qsage: Optimization solver solves an input objective function Quantum Apprentice (QuApp): MS-Excel-based training tool enables experimentation with small numbers of qubits qbqual: Tool that qualifies a QUBO for effective execution on a D- Wave system *Quadratic unconstrained binary optimization problem

4 Outline Givens Strategies Focus areas and Roadmap

5 Givens The D-Wave system will evolve and soon deliver quantum advantage Differing types of users will have differing tool requirements Subject-matter experts (SMEs) are already solving D-Wave-style problems today with existing tools D-Wave is not the only organization developing tools; need to be a good citizen Almost all problems justifying D-Wave access will be bigger than hardware

6 Types of Innovator Users Bilingual: conversant with hardware and application layers Deep hardware and domain knowledge required Potentially necessary for first few gainful apps Scarce Monolingual: conversant with application layers Willing to rework application to new formulation Not willing to learn (much) quantum annealing physics Much more abundant

7 Tool Requirements Bilingual Easier is nice, but don t get in the way of any potentially valuable capability Monolingual If at all practical, let me keep using my domain- or method-specific interface and have that map to D-Wave If I must change to a new programming interface, that is a big deal, so the new interface must last and evolve a long time Shield me from nearly all the details of mapping to a given system

8 Agenda Givens Strategies Focus areas and Roadmap

9 Strategies Establish strong abstractions to foster applications and SME*- focused layered tools Enables concurrent development by hardware, tools, and app developers Add value closer to the system Deliver and respond quickly Distinguish between prototypes and products Collaborate with capable partners via open-source when appropriate When quantum advantage is real, deliver it to multiple domains quickly *Subject-matter expert

10 One Approach to Using the QMI* Map problem to a higher-order binary optimization (HUBO) on an energy scale Map HUBO to a QUBO Optimize the QUBO Partition to fit on hardware after following steps Map from virtual to physical connectivity Scale to limited numerical range Control for hardware skew Micro-adjust annealing for best results Correct for quantum errors *Quantum Machine Instruction

11 One Approach to Using the QMI Map problem to a higher-order binary optimization (HUBO) on an energy scale Map HUBO to a QUBO Methoddependent Methodindependent Virtual QUBO Optimize the QUBO Partition to fit on hardware after following steps Map from virtual to physical connectivity Scale to limited numerical range Control for hardware skew Micro-adjust annealing for best results Correct for quantum errors

12 Virtual QUBO Abstraction Modeling frameworks Is the abstraction highly useful? Virtual QUBO Abstraction Is it effectively implementable? SAPI (C, C++, MATLAB, Python) Solver API (C, C++, MATLAB, Python) QMI Quantum Machine Instruction Target Quantum Processors

13 Effectively Implementable? qbsolv partitions a virtual QUBO for D-Wave execution Algorithm based on Wang, Lu, Glover, and Hao [2012] Numerical results competitive with best other results; execution time (solely classical) 3X faster Solves problems O(10X) bigger than underlying D-Wave system Collaborating with Glover, Lewis, et al. on better algorithm Anticipating open-source availability this fall Other work on hybrid partitioning optimization solvers 1QBit: Building an iterative heuristic solver for a quantum annealer, Spedalieri, Albash et al.: see HPEC and this afternoon s presentations Effectively implementable?: So far, Yes

14 Highly Useful for SME Tools? Karp showed that all NP problems convertible to other NP problems Note: Andrew Lucas ( Ising formulations of many NP problems ) is working with us Glover et al report good success converting high-value problems to BQPs (==QUBOs) The Unconstrained Binary Quadratic Programming Problem: A Survey, ToQ emits vqubo for large problems Enables ToQ to solve problems > hardware Works, but vqubo not ideal for constraint-satisfaction problems Highly useful?: Promising, but insufficient data

15 Agenda Givens Strategies Focus areas and Roadmap

16 One Approach to Using the QMI Map problem to a higher-order binary optimization (HUBO) on an energy scale Map HUBO to a QUBO Methoddependent Methodindependent Optimize the QUBO Virtual QUBO Partition to fit on hardware after following steps Map from virtual to physical connectivity Scale to limited numerical range Control for hardware skew Micro-adjust annealing for best results Correct for quantum errors qbsolv Partitioning constraint-sat solver executor

17 Product Stack Applications Common Languages, IDEs, & APIs RBM SAT Filter Structured SVM Models Standard Modelling Frameworks Boltzmann Samples Hybrid Sampling Methods QUBO Solutions Hybrid Optimization Methods Sampling & Optimization Engine Solver & Sampling Libraries QC System

18 Current qop Components User training Application Common Languages, IDEs, & APIs Models ToQ Standard Modelling Frameworks qsage Hybrid Optimization Method (qbsolv) QuApp, dw, qbqual Sampling & Optimization Engine Solver & Sampling Libraries QC System

19 Practicalities qop tools are prototypes for now Rapid delivery, rapid feedback, rapid evolution Not production quality In time, some will likely become products Packages made available ~quarterly qop 2.2 made available Aug1 ToQ: experimental version added qbsolv: performance improvements qop 2.3 targeted for late October qbsolv: open-source-ready, more performance improvements

20 qop Roadmap Spring 2017 ToQ Constraint Satisfaction e.g., Cryptol, MiniZinc Standard Modelling Frameworks qsage Sampling & Optimization Engine vqubo Hybrid Optimization Solver (qbsolv) executor Solver & Sampling Libraries

21 qop Roadmap Fall 2017 ToQ Constraint Satisfaction e.g., Cryptol, MiniZinc Standard Modelling Frameworks qsage Sampling & Optimization Engine vqubo <???> Hybrid Optimization Solver (qbsolv) Hybrid Constraint- Satisfaction Solver Core Libraries executor

22 The qop goals are to establish key abstractions that are valuable for applications and higherlevel tools and effectively execute them on D-Wave systems.

23 Backup

24 Where are we going? Our goal is to enable our customers to be successful. Applications Models We want the product to enable more users to build effective algorithms, models and applications. Sampling & Optimization Engine Sample/Solution Rate Graph Quality Sample/Solution Quality