Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems Davide Venturelli, Ph.D. USRA Research Institute for Advanced Computer Science Quantum Artificial Intelligence Laboratory NASA Ames Research Center davide.venturelli@nasa.gov Collaborators: Eleanor Rieffel (NASA), Stuart Hadfield (Columbia), Zhihui Wang (USRA), Immanuel Trummer (Cornell), Bryan O’Gorman (UC Berkeley), Rupak Biswas (NASA), Dominic Marchand (1QBIT), Bibek Pokharel (UCLA) 1
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov The Quantum AI Laboratory @ NASA Ames – and RIACS Machine Learning: Data Analysis and Data Fusion Air Traffic Management Mission Planning and Scheduling, and Coordination Anomaly Detection and Decision Making QuAIL is the space agency's hub for an experiment to assess the potential of quantum computers to perform calculations RIACS conducts basic and applied research in computer science that are difficult or impossible using conventional for the nation’s aeronautics and space-related missions and supercomputers. programs, with the goal to enable a high degree of automation for https://ti.arc.nasa.gov/tech/dash/physics/quail/ every aspect of scientific research and engineering. We are hiring (postdocs, senior researchers) 2
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Real-world combinatorial optimization is empirically HARD for current methods. • → It is worth to investigate if disruptive approaches could help. 3
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Real-world combinatorial optimization is empirically HARD for current methods. • → It is worth to investigate if disruptive approaches could help. • S.o.A. methods to be practical require to be fine-tailored, and industry often employs suboptimal heuristics without performance guarantee. → It is worth to investigate “general purpose methods” that could deliver some speedup if hybridized or if it is not practical to exploit the domain knowledge. 3
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Real-world combinatorial optimization is empirically HARD for current methods. • → It is worth to investigate if disruptive approaches could help. • S.o.A. methods to be practical require to be fine-tailored, and industry often employs suboptimal heuristics without performance guarantee. → It is worth to investigate “general purpose methods” that could deliver some speedup if hybridized or if it is not practical to exploit the domain knowledge. • Figure of merit could be: speed, solution quality, diversity of solutions → The unique sampling properties of quantum methods could be a competitive advantage. 3
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Real-world combinatorial optimization is empirically HARD for current methods. • → It is worth to investigate if disruptive approaches could help. • S.o.A. methods to be practical require to be fine-tailored, and industry often employs suboptimal heuristics without performance guarantee. → It is worth to investigate “general purpose methods” that could deliver some speedup if hybridized or if it is not practical to exploit the domain knowledge. • Figure of merit could be: speed, solution quality, diversity of solutions → The unique sampling properties of quantum methods could be a competitive advantage. • Devised quantum methods that are “quick” (shallow circuits or analog) and “dirty” (not explicitly relying on coherence as fundamental working mechanism, but speedup potential not clear). → They can be tested, solving the chicken-and-egg paradox of quantum computing. They define the rules Hard Constraints + Quality Metric of the game, the “ feasible subspace ” 3
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Combinatorial Optimization for Quantum Computing • Embedding efficiently a problem into a quantum analog annealer • Finding the optimal parameters for annealing time and encoding penalties • Determining the compilation that maximizes fidelity of computation • Finding the optimal parameters for QAOA ansatz • Calibration of a manufactured chip… 4
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Computing for Combinatorial Optimization Combinatorial Optimization for Quantum Computing • Embedding efficiently a problem into a quantum analog annealer • Finding the optimal Build a Quantum Computer Prototype parameters for annealing time and encoding penalties • Determining the compilation that maximizes fidelity of computation • Finding the optimal Effectively Program a Quantum Algorithm parameters for QAOA ansatz • Calibration of a manufactured chip… Run Quantum Optimization 4
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) Physics-inspired Monte-Carlo updates e −∆ E/kT e −∆ E/kT e −∆ E/kT solution 5 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) E({z}) Physics-inspired Monte-Carlo updates E({z}) Temperature e −∆ E/kT e −∆ E/kT E({z}) e −∆ E/kT Time solution Bit flips activated by temperature 5 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) 3 Key differences: 1) Superposition of bit-strings (tunneling) 2) Energy landscape changes over time 3) Equilibration and Adiabatic Theorem 6 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) 3 Key differences: 1) Superposition of bit-strings (tunneling) 2) Energy landscape changes over time 3) Equilibration and Adiabatic Theorem 6 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) 3 Key differences: 1) Superposition of bit-strings (tunneling) 2) Energy landscape changes over time 3) Equilibration and Adiabatic Theorem Time, τ E({*}, τ=0) E({*}, τ<1) E({z}, τ =1) tunneling {z} {z} {z} Bit flips activated by tunneling 6 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (from Simulated Annealing ) 3 Key differences: 1) Superposition of bit-strings (tunneling) 2) Energy landscape changes over time 3) Equilibration and Adiabatic Theorem Time, τ 000 001 E({*}, τ=0) E({*}, τ<1) E({z}, τ =1) 010 P SR ( τ ) =1- ε 011 111 for sufficiently large τ 110 tunneling 101 {z} {z} {z} 100 probability Bit flips activated by tunneling 6 Universities Space Research Association
Challenges to Practical End-to-end Implementation of Quantum Optimization Approaches for Combinatorial problems QTML Workshop – Verona, Nov 6 (2017) - Davide Venturelli – davide.venturelli@nasa.gov Quantum Annealing (à la D-Wave) Minimum of ∑ ij Q ij x i x j B(t) (∑ ij J ij σ z i σ z j + ∑ i h i σ z i ) + A(t) ∑ i σ x i Time, τ E({*}, τ=0) E({*}, τ<1) E({z}, τ =1) σ x σ z i = i = tunneling {z} {z} {z} 7 Universities Space Research Association
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